Repository: sustainable-processes/summit (language: python)

Issue:
`Domain.to_dict()` doesn't include information for `maximize` of objectives
* Operating System: Win 11 22H2
* Python version: 3.8.13
* summit version used: 0.8.7

### Description

When trying to print out the constructed domain, the `maximize` of the objectives is not presented in the dictionary.

### What I Did

```python
from summit.domain import Domain
from summit.domain import ContinuousVariable
domain = Domain()
domain += ContinuousVariable(
    name='name',
    description='description',
    bounds=[-5, 5],
    is_objective=True,
    maximize=True
)
domain += ContinuousVariable(
    name='name',
    description='description',
    bounds=[-50, 50],
    is_objective=True,
    maximize=False
)
print(domain.to_dict())
```

Outputs:
```
[{'type': 'ContinuousVariable',
  'is_objective': True,
  'name': 'name',
  'description': 'description',
  'units': None,
  'bounds': [-5.0, 5.0]},
 {'type': 'ContinuousVariable',
  'is_objective': True,
  'name': 'name',
  'description': 'description',
  'units': None,
  'bounds': [-50.0, 50.0]}]
```



Discussion / hints:

Fix patch:
diff --git a/scripts/train_emulators/README.md b/scripts/train_emulators/README.md
index 1b274e62..1c16fc34 100644
--- a/scripts/train_emulators/README.md
+++ b/scripts/train_emulators/README.md
@@ -5,13 +5,13 @@ The `train_emulators.py` script will train emulators and create this report.
 This is the data from training of the reizman suzuki benchmark for 1000 epochs with 5 cross-validation folds.
 | case   |   avg_fit_time |   avg_val_r2 |   avg_val_RMSE |   avg_test_r2 |   avg_test_RMSE |
 |:-------|---------------:|-------------:|---------------:|--------------:|----------------:|
-| case_1 |           8.63 |         0.82 |          11.14 |          0.93 |            7.54 |
-| case_2 |           8.8  |         0.61 |           5.38 |          0.66 |            4.99 |
-| case_3 |           8.24 |         0.78 |          12.91 |          0.84 |           11.9  |
-| case_4 |           8.31 |         0.7  |          15.67 |          0.73 |           14.06 |
+| case_1 |           3.52 |         0.81 |          11.21 |          0.93 |            7.66 |
+| case_2 |           3.58 |         0.59 |           5.54 |          0.67 |            4.91 |
+| case_3 |           3.61 |         0.76 |          13.11 |          0.84 |           12.04 |
+| case_4 |           3.6  |         0.7  |          15.99 |          0.74 |           13.8  |
 ## Baumgartner C-N Cross Cross Coupling 
 This is the data from training of the Baumgartner C-N aniline cross-coupling benchmark for 1000 epochs with 5 cross-validation folds.
 | case        |   avg_fit_time |   avg_val_r2 |   avg_val_RMSE |   avg_test_r2 |   avg_test_RMSE |
 |:------------|---------------:|-------------:|---------------:|--------------:|----------------:|
-| one-hot     |           8.17 |         0.8  |           0.18 |          0.89 |            0.13 |
-| descriptors |           8.19 |         0.86 |           0.15 |          0.91 |            0.11 |
+| one-hot     |           3.54 |         0.79 |           0.18 |          0.88 |            0.13 |
+| descriptors |           3.52 |         0.84 |           0.16 |          0.9  |            0.12 |
diff --git a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png
index 022780cc..0fa15ebc 100644
Binary files a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png and b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling.png differ
diff --git a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png
index f9d5a1a8..e0b93f18 100644
Binary files a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png and b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_descriptors.png differ
diff --git a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv
index ca09c72c..de9e5482 100644
--- a/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv
+++ b/scripts/train_emulators/results/baumgartner_aniline_cn_crosscoupling_scores.csv
@@ -1,3 +1,3 @@
 case,avg_fit_time,avg_score_time,avg_val_r2,avg_val_neg_root_mean_squared_error,avg_test_r2,avg_test_neg_root_mean_squared_error
-one-hot,8.166110754013062,0.005637454986572266,0.801623251767853,-0.17928863167762757,0.8913133689375095,-0.12660206639121113
-descriptors,8.191894769668579,0.006843852996826172,0.8564572592750457,-0.15195560306310654,0.9149636380225289,-0.11228418010873668
+one-hot,3.535422945022583,0.0018382549285888671,0.7885762443951967,-0.1828158438205719,0.8784990184881403,-0.13178804204825675
+descriptors,3.520027923583984,0.0021105289459228517,0.8410473186245367,-0.16089824438095093,0.9011444270426143,-0.12116910725437406
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_1.png b/scripts/train_emulators/results/reizman_suzuki_case_1.png
index 1d4f3d9d..9643c0b4 100644
Binary files a/scripts/train_emulators/results/reizman_suzuki_case_1.png and b/scripts/train_emulators/results/reizman_suzuki_case_1.png differ
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_2.png b/scripts/train_emulators/results/reizman_suzuki_case_2.png
index a788fb78..338b5150 100644
Binary files a/scripts/train_emulators/results/reizman_suzuki_case_2.png and b/scripts/train_emulators/results/reizman_suzuki_case_2.png differ
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_3.png b/scripts/train_emulators/results/reizman_suzuki_case_3.png
index 8c8e9962..4030b34d 100644
Binary files a/scripts/train_emulators/results/reizman_suzuki_case_3.png and b/scripts/train_emulators/results/reizman_suzuki_case_3.png differ
diff --git a/scripts/train_emulators/results/reizman_suzuki_case_4.png b/scripts/train_emulators/results/reizman_suzuki_case_4.png
index e94996a5..180a2bb0 100644
Binary files a/scripts/train_emulators/results/reizman_suzuki_case_4.png and b/scripts/train_emulators/results/reizman_suzuki_case_4.png differ
diff --git a/scripts/train_emulators/results/reizman_suzuki_scores.csv b/scripts/train_emulators/results/reizman_suzuki_scores.csv
index 559a6e4e..b3171674 100644
--- a/scripts/train_emulators/results/reizman_suzuki_scores.csv
+++ b/scripts/train_emulators/results/reizman_suzuki_scores.csv
@@ -1,5 +1,5 @@
 case,avg_fit_time,avg_score_time,avg_val_r2,avg_val_neg_root_mean_squared_error,avg_test_r2,avg_test_neg_root_mean_squared_error
-case_1,8.6258864402771,0.005854988098144531,0.8212155526088756,-11.137054443359375,0.9295815380048114,-7.543151896248529
-case_2,8.79561619758606,0.00565180778503418,0.605790245122981,-5.384605264663696,0.6605449615937999,-4.99218215483449
-case_3,8.240145397186279,0.00576167106628418,0.7760751544224982,-12.907710647583007,0.842104321811395,-11.899007938905271
-case_4,8.307093811035156,0.005579137802124023,0.7043271376927565,-15.66837387084961,0.7326339105287581,-14.055312980735687
+case_1,3.524441385269165,0.0019971370697021485,0.8133778193051648,-11.210040664672851,0.9277257528325263,-7.659243144643234
+case_2,3.584489107131958,0.0018500328063964845,0.5875258046769148,-5.542116260528564,0.6731724135929321,-4.914557483011899
+case_3,3.61135573387146,0.0018646240234375,0.7575032073182122,-13.106094932556152,0.8388203899628657,-12.040626679884866
+case_4,3.5974781036376955,0.001851367950439453,0.6979552061032572,-15.986057090759278,0.7417433519970528,-13.803447416357022
diff --git a/scripts/train_emulators/train_emulators.py b/scripts/train_emulators/train_emulators.py
index 8099c7d0..d9406c85 100644
--- a/scripts/train_emulators/train_emulators.py
+++ b/scripts/train_emulators/train_emulators.py
@@ -102,7 +102,6 @@ def _train_baumgartner(use_descriptors=False, show_plots=False, save_plots=True)
         domain,
         dataset=ds,
         regressor=ANNRegressor,
-        output_variable_names=["yield"],
         descriptors_features=["catalyst", "base"] if use_descriptors else [],
     )
     res = exp.train(
diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling/baumgartner_aniline_cn_crosscoupling.json b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling/baumgartner_aniline_cn_crosscoupling.json
index 5442f966..7ab5f9d1 100644
--- a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling/baumgartner_aniline_cn_crosscoupling.json
+++ b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling/baumgartner_aniline_cn_crosscoupling.json
@@ -1,366 +1,1 @@
-{
-  "domain"           : [
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "catalyst",
-      "description" : "Catalyst type",
-      "units"       : null,
-      "levels"      : [
-        "tBuXPhos",
-        "tBuBrettPhos",
-        "AlPhos"
-      ],
-      "ds"          : {
-        "index"  : [
-          "tBuXPhos",
-          "tBuBrettPhos",
-          "AlPhos"
-        ],
-        "columns": [
-          [
-            "area_cat",
-            "DATA"
-          ],
-          [
-            "M2_cat",
-            "DATA"
-          ]
-        ],
-        "data"   : [
-          [
-            460.7543,
-            67.2057
-          ],
-          [
-            518.8408,
-            89.8738
-          ],
-          [
-            819.933,
-            129.0808
-          ]
-        ]
-      }
-    },
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "base",
-      "description" : "Base",
-      "units"       : null,
-      "levels"      : [
-        "DBU",
-        "BTMG",
-        "TMG",
-        "TEA"
-      ],
-      "ds"          : {
-        "index"  : [
-          "TEA",
-          "TMG",
-          "BTMG",
-          "DBU"
-        ],
-        "columns": [
-          [
-            "area",
-            "DATA"
-          ],
-          [
-            "M2",
-            "DATA"
-          ]
-        ],
-        "data"   : [
-          [
-            162.2992,
-            25.8165
-          ],
-          [
-            165.5447,
-            81.4847
-          ],
-          [
-            227.3523,
-            30.554
-          ],
-          [
-            192.4693,
-            59.8367
-          ]
-        ]
-      }
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "base_equivalents",
-      "description" : "Base equivalents",
-      "units"       : null,
-      "bounds"      : [
-        1.0,
-        2.5
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "temperature",
-      "description" : "Temperature in degrees Celsius (\u00baC)",
-      "units"       : null,
-      "bounds"      : [
-        30.0,
-        100.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "t_res",
-      "description" : "residence time in seconds (s)",
-      "units"       : null,
-      "bounds"      : [
-        60.0,
-        1800.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "yld",
-      "description" : "Yield",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        1.0
-      ]
-    }
-  ],
-  "name"             : "ExperimentalEmulator",
-  "data"             : {
-    "index"  : [],
-    "columns": [
-      [
-        "catalyst",
-        "DATA"
-      ],
-      [
-        "base",
-        "DATA"
-      ],
-      [
-        "base_equivalents",
-        "DATA"
-      ],
-      [
-        "temperature",
-        "DATA"
-      ],
-      [
-        "t_res",
-        "DATA"
-      ],
-      [
-        "yld",
-        "DATA"
-      ],
-      [
-        "computation_t",
-        "METADATA"
-      ],
-      [
-        "experiment_t",
-        "METADATA"
-      ],
-      [
-        "strategy",
-        "METADATA"
-      ]
-    ],
-    "data"   : []
-  },
-  "experiment_params": {
-    "model_name"           : "baumgartner_aniline_cn_crosscoupling",
-    "regressor_name"       : "ANNRegressor",
-    "n_features"           : 10,
-    "n_examples"           : 96,
-    "descriptors_features" : [],
-    "output_variable_names": [
-      "yld"
-    ],
-    "predictors"           : [
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              1.6559957171333333,
-              69.63333333333334,
-              675.2387380961666
-            ],
-            "var_"           : [
-              0.24093575016750415,
-              906.7308888888889,
-              253625.04696145264
-            ],
-            "scale_"         : [
-              0.4908520654611776,
-              30.11197251740392,
-              503.6120004144586
-            ],
-            "n_samples_seen_": 60
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            0.5805532822851092
-          ],
-          "var_"           : [
-            0.1785550681951766
-          ],
-          "scale_"         : [
-            0.42255776906261777
-          ],
-          "n_samples_seen_": 60
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              1.683371605967213,
-              74.62295081967213,
-              715.8182047760656
-            ],
-            "var_"           : [
-              0.24120375329778332,
-              866.5532437516797,
-              223573.2988868711
-            ],
-            "scale_"         : [
-              0.49112498745002103,
-              29.43727643230059,
-              472.83538243967223
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            0.6254036210660563
-          ],
-          "var_"           : [
-            0.16832977557768586
-          ],
-          "scale_"         : [
-            0.4102801184284779
-          ],
-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              1.6615540930327868,
-              69.30163934426228,
-              681.7872910763934
-            ],
-            "var_"           : [
-              0.24927125858022928,
-              924.5657350174685,
-              269346.5438305566
-            ],
-            "scale_"         : [
-              0.49927072674074263,
-              30.40667254103067,
-              518.9860728676219
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            0.5703947171110844
-          ],
-          "var_"           : [
-            0.18538124205880477
-          ],
-          "scale_"         : [
-            0.43055922015305254
-          ],
-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              1.628605248,
-              72.78196721311477,
-              735.1206038822951
-            ],
-            "var_"           : [
-              0.24907596673949953,
-              907.5027895726955,
-              244282.01121683227
-            ],
-            "scale_"         : [
-              0.4990751113204299,
-              30.124786963108892,
-              494.24893648528194
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            0.6131021287750269
-          ],
-          "var_"           : [
-            0.17856050759065348
-          ],
-          "scale_"         : [
-            0.4225642052879698
-          ],
-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              1.6073154240819671,
-              73.0311475409836,
-              743.0736161352461
-            ],
-            "var_"           : [
-              0.2525966705755136,
-              848.4237839290512,
-              251362.03663802444
-            ],
-            "scale_"         : [
-              0.5025899626688873,
-              29.12771504819853,
-              501.3601865306263
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            0.564398883872467
-          ],
-          "var_"           : [
-            0.18049929808199283
-          ],
-          "scale_"         : [
-            0.4248520896523787
-          ],
-          "n_samples_seen_": 61
-        }
-      }
-    ]
-  },
-  "extras"           : []
-}
\ No newline at end of file
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\ No newline at end of file
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diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors.json b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors.json
index 1562b326..c56c0663 100644
--- a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors.json
+++ b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors.json
@@ -1,369 +1,1 @@
-{
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\ No newline at end of file
+{"domain": [{"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "catalyst", "description": "Catalyst type", "units": null, "levels": ["tBuXPhos", "tBuBrettPhos", "AlPhos"], "ds": {"index": ["tBuXPhos", "tBuBrettPhos", "AlPhos"], "columns": [["area_cat", "DATA"], ["M2_cat", "DATA"]], "data": [[460.7543, 67.2057], [518.8408, 89.8738], [819.933, 129.0808]]}}, {"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "base", "description": "Base", "units": null, "levels": ["DBU", "BTMG", "TMG", "TEA"], "ds": {"index": ["TEA", "TMG", "BTMG", "DBU"], "columns": [["area", "DATA"], ["M2", "DATA"]], "data": [[162.2992, 25.8165], [165.5447, 81.4847], [227.3523, 30.554], [192.4693, 59.8367]]}}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "base_equivalents", "description": "Base equivalents", "units": null, "bounds": [1.0, 2.5]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "temperature", "description": "Temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 100.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "t_res", "description": "residence time in seconds (s)", "units": null, "bounds": [60.0, 1800.0]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": true, "name": "yld", "description": "Yield", "units": null, "bounds": [0.0, 1.0]}], "name": "ExperimentalEmulator", "data": {"index": [], "columns": [["catalyst", "DATA"], ["base", "DATA"], ["base_equivalents", "DATA"], ["temperature", "DATA"], ["t_res", "DATA"], ["yld", "DATA"], ["computation_t", "METADATA"], ["experiment_t", "METADATA"], ["strategy", "METADATA"]], "data": []}, "experiment_params": {"model_name": "baumgartner_aniline_cn_crosscoupling_descriptors", "regressor_name": "ANNRegressor", "n_features": 7, "n_examples": 96, "descriptors_features": ["catalyst", "base"], "output_variable_names": ["yld"], "predictors": [{"input_preprocessor": {"num": {"mean_": [1.6559957171333333, 69.63333333333334, 675.2387380961666], "var_": [0.24093575016750415, 906.7308888888889, 253625.04696145264], "scale_": [0.4908520654611776, 30.11197251740392, 503.6120004144586], "n_samples_seen_": 60}}, "output_preprocessor": {"mean_": [0.5805532822851092], "var_": [0.1785550739750158], "scale_": [0.422557775901729], "n_samples_seen_": 60}}, {"input_preprocessor": {"num": {"mean_": [1.683371605967213, 74.62295081967213, 715.8182047760656], "var_": [0.24120375329778332, 866.5532437516797, 223573.2988868711], "scale_": [0.49112498745002103, 29.43727643230059, 472.83538243967223], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [0.6254036210660563], "var_": [0.1683297707171898], "scale_": [0.4102801125050906], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [1.6615540930327868, 69.30163934426228, 681.7872910763934], "var_": [0.24927125858022928, 924.5657350174685, 269346.5438305566], "scale_": [0.49927072674074263, 30.40667254103067, 518.9860728676219], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [0.5703947171110844], "var_": [0.1853812458795208], "scale_": [0.4305592245899753], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [1.628605248, 72.78196721311477, 735.1206038822951], "var_": [0.24907596673949953, 907.5027895726955, 244282.01121683227], "scale_": [0.4990751113204299, 30.124786963108892, 494.24893648528194], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [0.6131021287750269], "var_": [0.17856051178547724], "scale_": [0.422564210251504], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [1.6073154240819671, 73.0311475409836, 743.0736161352461], "var_": [0.2525966705755136, 848.4237839290512, 251362.03663802444], "scale_": [0.5025899626688873, 29.12771504819853, 501.3601865306263], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [0.564398883872467], "var_": [0.18049929995617797], "scale_": [0.42485209185807005], "n_samples_seen_": 61}}], "clip": true}, "extras": []}
\ No newline at end of file
diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_0.pt b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_0.pt
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index c6f7db4c..6eff09f4 100644
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diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_2.pt b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_2.pt
index 7483179f..ee406404 100644
Binary files a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_2.pt and b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_2.pt differ
diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_3.pt b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_3.pt
index 3fb94fdb..9d641a28 100644
Binary files a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_3.pt and b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_3.pt differ
diff --git a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_4.pt b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_4.pt
index 1fab2856..ea81b296 100644
Binary files a/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_4.pt and b/summit/benchmarks/models/baumgartner_aniline_cn_crosscoupling_descriptors/baumgartner_aniline_cn_crosscoupling_descriptors_predictor_4.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1.json b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1.json
index 90b4be1f..2302f010 100644
--- a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1.json
+++ b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1.json
@@ -1,319 +1,1 @@
-{
-  "domain"           : [
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "catalyst",
-      "description" : "Catalyst type - different ligands",
-      "units"       : null,
-      "levels"      : [
-        "P1-L1",
-        "P2-L1",
-        "P1-L2",
-        "P1-L3",
-        "P1-L4",
-        "P1-L5",
-        "P1-L6",
-        "P1-L7"
-      ],
-      "ds"          : null
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "t_res",
-      "description" : "Residence time in seconds (s)",
-      "units"       : null,
-      "bounds"      : [
-        60.0,
-        600.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "temperature",
-      "description" : "Reactor temperature in degrees Celsius (\u00baC)",
-      "units"       : null,
-      "bounds"      : [
-        30.0,
-        110.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "catalyst_loading",
-      "description" : "Catalyst loading in mol%",
-      "units"       : null,
-      "bounds"      : [
-        0.5,
-        2.5
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "ton",
-      "description" : "Turnover number - moles product generated divided by moles catalyst used",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        200.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "yld",
-      "description" : "Yield",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        100.0
-      ]
-    }
-  ],
-  "name"             : "ExperimentalEmulator",
-  "data"             : {
-    "index"  : [],
-    "columns": [
-      [
-        "catalyst",
-        "DATA"
-      ],
-      [
-        "t_res",
-        "DATA"
-      ],
-      [
-        "temperature",
-        "DATA"
-      ],
-      [
-        "catalyst_loading",
-        "DATA"
-      ],
-      [
-        "ton",
-        "DATA"
-      ],
-      [
-        "yld",
-        "DATA"
-      ],
-      [
-        "computation_t",
-        "METADATA"
-      ],
-      [
-        "experiment_t",
-        "METADATA"
-      ],
-      [
-        "strategy",
-        "METADATA"
-      ]
-    ],
-    "data"   : []
-  },
-  "experiment_params": {
-    "model_name"           : "reizman_suzuki_case_1",
-    "regressor_name"       : "ANNRegressor",
-    "n_features"           : 11,
-    "n_examples"           : 96,
-    "descriptors_features" : [],
-    "output_variable_names": [
-      "ton",
-      "yld"
-    ],
-    "predictors"           : [
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              354.02166666666665,
-              93.35666666666665,
-              1.5370833333333331
-            ],
-            "var_"           : [
-              62364.78669722223,
-              790.510788888889,
-              0.630418109722222
-            ],
-            "scale_"         : [
-              249.72942697492067,
-              28.116023703377564,
-              0.7939887340020776
-            ],
-            "n_samples_seen_": 60
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            30.31333336258928,
-            41.88166637221972
-          ],
-          "var_"           : [
-            635.5938398516427,
-            1069.4477598033263
-          ],
-          "scale_"         : [
-            25.210986491044785,
-            32.70241214044197
-          ],
-          "n_samples_seen_": 60
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              326.60819672131146,
-              92.32622950819672,
-              1.5993606557377045
-            ],
-            "var_"           : [
-              61304.89124428917,
-              841.8547218489653,
-              0.6307227223864553
-            ],
-            "scale_"         : [
-              247.5982456405723,
-              29.014732841247483,
-              0.7941805351344587
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            28.045901633676934,
-            42.237704714790716
-          ],
-          "var_"           : [
-            608.6952533265118,
-            1152.2032867646853
-          ],
-          "scale_"         : [
-            24.671750106680957,
-            33.94412006172329
-          ],
-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              331.3311475409836,
-              90.84918032786884,
-              1.631934426229508
-            ],
-            "var_"           : [
-              63160.69001343723,
-              853.8743026068263,
-              0.6774574383230314
-            ],
-            "scale_"         : [
-              251.3179062729857,
-              29.22112767513989,
-              0.8230780268741423
-            ],
-            "n_samples_seen_": 61
-          }
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-        "output_preprocessor": {
-          "mean_"          : [
-            26.267213071223164,
-            38.83934418881526
-          ],
-          "var_"           : [
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-          "scale_"         : [
-            23.41764603921074,
-            33.156471303608235
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-        "input_preprocessor" : {
-          "num": {
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-              245.30338678212968,
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-        "output_preprocessor": {
-          "mean_"          : [
-            30.268852538016976,
-            46.137704603007585
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-          "var_"           : [
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-          "scale_"         : [
-            25.120278233686232,
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-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              348.5508196721312,
-              90.172131147541,
-              1.6511147540983604
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-            "var_"           : [
-              62814.376597688795,
-              855.4459446385379,
-              0.6407999048642837
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-            "scale_"         : [
-              250.6279645165096,
-              29.24800753279679,
-              0.8004997844248828
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-        "output_preprocessor": {
-          "mean_"          : [
-            27.61475420022597,
-            39.5245899352871
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-          "var_"           : [
-            627.0747155828524,
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-          "scale_"         : [
-            25.041459933135936,
-            31.921330985233666
-          ],
-          "n_samples_seen_": 61
-        }
-      }
-    ]
-  },
-  "extras"           : []
-}
\ No newline at end of file
+{"domain": [{"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "catalyst", "description": "Catalyst type - different ligands", "units": null, "levels": ["P1-L1", "P2-L1", "P1-L2", "P1-L3", "P1-L4", "P1-L5", "P1-L6", "P1-L7"], "ds": null}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "t_res", "description": "Residence time in seconds (s)", "units": null, "bounds": [60.0, 600.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "temperature", "description": "Reactor temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 110.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "catalyst_loading", "description": "Catalyst loading in mol%", "units": null, "bounds": [0.5, 2.5]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": false, "name": "ton", "description": "Turnover number - moles product generated divided by moles catalyst used", "units": null, "bounds": [0.0, 200.0]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": true, "name": "yld", "description": "Yield", "units": null, "bounds": [0.0, 100.0]}], "name": "ExperimentalEmulator", "data": {"index": [], "columns": [["catalyst", "DATA"], ["t_res", "DATA"], ["temperature", "DATA"], ["catalyst_loading", "DATA"], ["ton", "DATA"], ["yld", "DATA"], ["computation_t", "METADATA"], ["experiment_t", "METADATA"], ["strategy", "METADATA"]], "data": []}, "experiment_params": {"model_name": "reizman_suzuki_case_1", "regressor_name": "ANNRegressor", "n_features": 11, "n_examples": 96, "descriptors_features": [], "output_variable_names": ["ton", "yld"], "predictors": [{"input_preprocessor": {"num": {"mean_": [354.02166666666665, 93.35666666666665, 1.5370833333333331], "var_": [62364.78669722223, 790.510788888889, 0.630418109722222], "scale_": [249.72942697492067, 28.116023703377564, 0.7939887340020776], "n_samples_seen_": 60}}, "output_preprocessor": {"mean_": [30.31333336258928, 41.88166637221972], "var_": [635.5938182175908, 1069.4478047498576], "scale_": [25.210986061984777, 32.7024128276471], "n_samples_seen_": 60}}, {"input_preprocessor": {"num": {"mean_": [326.60819672131146, 92.32622950819672, 1.5993606557377045], "var_": [61304.89124428917, 841.8547218489653, 0.6307227223864553], "scale_": [247.5982456405723, 29.014732841247483, 0.7941805351344587], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [28.045901633676934, 42.237704714790716], "var_": [608.6952648540826, 1152.203320977485], "scale_": [24.671750340299784, 33.94412056568096], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [331.3311475409836, 90.84918032786884, 1.631934426229508], "var_": [63160.69001343723, 853.8743026068263, 0.6774574383230314], "scale_": [251.3179062729857, 29.22112767513989, 0.8230780268741423], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [26.267213071223164, 38.83934418881526], "var_": [548.3861349094093, 1099.3515577849712], "scale_": [23.417645802031622, 33.1564708282557], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [346.4393442622951, 95.5344262295082, 1.616327868852459], "var_": [60173.75156678312, 662.3563558183281, 0.6452992039774254], "scale_": [245.30338678212968, 25.736284809939605, 0.8033051748728035], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [30.268852538016976, 46.137704603007585], "var_": [631.0283775992513, 1125.9741358297965], "scale_": [25.12027821500493, 33.555538079872846], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [348.5508196721312, 90.172131147541, 1.6511147540983604], "var_": [62814.376597688795, 855.4459446385379, 0.6407999048642837], "scale_": [250.6279645165096, 29.24800753279679, 0.8004997844248828], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [27.61475420022597, 39.5245899352871], "var_": [627.0747027031705, 1018.9713474448904], "scale_": [25.04145967596878, 31.92133060266897], "n_samples_seen_": 61}}], "clip": true}, "extras": []}
\ No newline at end of file
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_0.pt b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_0.pt
index b305b4fd..9904f37a 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_0.pt and b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_0.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_1.pt b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_1.pt
index d7783f8c..ee3170dc 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_1.pt and b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_1.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_2.pt b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_2.pt
index 00f45202..c7a62795 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_2.pt and b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_2.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_3.pt b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_3.pt
index b37c9e16..5849693e 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_3.pt and b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_3.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_4.pt b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_4.pt
index 7aa2cf97..b2558d93 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_4.pt and b/summit/benchmarks/models/reizman_suzuki_case_1/reizman_suzuki_case_1_predictor_4.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2.json b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2.json
index 9ecd6448..d6529607 100644
--- a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2.json
+++ b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2.json
@@ -1,319 +1,1 @@
-{
-  "domain"           : [
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "catalyst",
-      "description" : "Catalyst type - different ligands",
-      "units"       : null,
-      "levels"      : [
-        "P1-L1",
-        "P2-L1",
-        "P1-L2",
-        "P1-L3",
-        "P1-L4",
-        "P1-L5",
-        "P1-L6",
-        "P1-L7"
-      ],
-      "ds"          : null
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "t_res",
-      "description" : "Residence time in seconds (s)",
-      "units"       : null,
-      "bounds"      : [
-        60.0,
-        600.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "temperature",
-      "description" : "Reactor temperature in degrees Celsius (\u00baC)",
-      "units"       : null,
-      "bounds"      : [
-        30.0,
-        110.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "catalyst_loading",
-      "description" : "Catalyst loading in mol%",
-      "units"       : null,
-      "bounds"      : [
-        0.5,
-        2.5
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "ton",
-      "description" : "Turnover number - moles product generated divided by moles catalyst used",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        200.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "yld",
-      "description" : "Yield",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        100.0
-      ]
-    }
-  ],
-  "name"             : "ExperimentalEmulator",
-  "data"             : {
-    "index"  : [],
-    "columns": [
-      [
-        "catalyst",
-        "DATA"
-      ],
-      [
-        "t_res",
-        "DATA"
-      ],
-      [
-        "temperature",
-        "DATA"
-      ],
-      [
-        "catalyst_loading",
-        "DATA"
-      ],
-      [
-        "ton",
-        "DATA"
-      ],
-      [
-        "yld",
-        "DATA"
-      ],
-      [
-        "computation_t",
-        "METADATA"
-      ],
-      [
-        "experiment_t",
-        "METADATA"
-      ],
-      [
-        "strategy",
-        "METADATA"
-      ]
-    ],
-    "data"   : []
-  },
-  "experiment_params": {
-    "model_name"           : "reizman_suzuki_case_2",
-    "regressor_name"       : "ANNRegressor",
-    "n_features"           : 11,
-    "n_examples"           : 96,
-    "descriptors_features" : [],
-    "output_variable_names": [
-      "ton",
-      "yld"
-    ],
-    "predictors"           : [
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              388.87166666666667,
-              85.14333333333335,
-              2.1326333333333336
-            ],
-            "var_"           : [
-              51629.748030555544,
-              1059.4557888888892,
-              0.4159331655555555
-            ],
-            "scale_"         : [
-              227.22180359850051,
-              32.54928246350277,
-              0.6449288065791103
-            ],
-            "n_samples_seen_": 60
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            6.178333353375395,
-            13.89833338521421
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\ No newline at end of file
+{"domain": [{"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "catalyst", "description": "Catalyst type - different ligands", "units": null, "levels": ["P1-L1", "P2-L1", "P1-L2", "P1-L3", "P1-L4", "P1-L5", "P1-L6", "P1-L7"], "ds": null}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "t_res", "description": "Residence time in seconds (s)", "units": null, "bounds": [60.0, 600.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "temperature", "description": "Reactor temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 110.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "catalyst_loading", "description": "Catalyst loading in mol%", "units": null, "bounds": [0.5, 2.5]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": false, "name": "ton", "description": "Turnover number - moles product generated divided by moles catalyst used", "units": null, "bounds": [0.0, 200.0]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": true, "name": "yld", "description": "Yield", "units": null, "bounds": [0.0, 100.0]}], "name": "ExperimentalEmulator", "data": {"index": [], "columns": [["catalyst", "DATA"], ["t_res", "DATA"], ["temperature", "DATA"], ["catalyst_loading", "DATA"], ["ton", "DATA"], ["yld", "DATA"], ["computation_t", "METADATA"], ["experiment_t", "METADATA"], ["strategy", "METADATA"]], "data": []}, "experiment_params": {"model_name": "reizman_suzuki_case_2", "regressor_name": "ANNRegressor", "n_features": 11, "n_examples": 96, "descriptors_features": [], "output_variable_names": ["ton", "yld"], "predictors": [{"input_preprocessor": {"num": {"mean_": [388.87166666666667, 85.14333333333335, 2.1326333333333336], "var_": [51629.748030555544, 1059.4557888888892, 0.4159331655555555], "scale_": [227.22180359850051, 32.54928246350277, 0.6449288065791103], "n_samples_seen_": 60}}, "output_preprocessor": {"mean_": [6.178333353375395, 13.89833338521421], "var_": [38.18736431062086, 204.10316587513861], "scale_": [6.179592568334975, 14.286467928607777], "n_samples_seen_": 60}}, {"input_preprocessor": {"num": {"mean_": [376.4754098360656, 85.4, 2.011622950819673], "var_": [53202.64152647137, 1135.808524590164, 0.5176740053748993], "scale_": [230.65697805718207, 33.70175847919755, 0.7194956604281219], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [5.442622941659122, 11.96721318461856], "var_": [34.40178982112041, 181.4792563569129], "scale_": [5.865303898445537, 13.471423694506566], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [370.2770491803279, 87.06885245901641, 2.053114754098361], "var_": [52169.36930932545, 1016.805751142166, 0.513469937651169], "scale_": [228.40614989383593, 31.88739172685916, 0.7165681667860839], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [5.681967221689029, 12.68852458210265], "var_": [34.88410148884411, 185.96396640884106], "scale_": [5.906276448731816, 13.636860577451142], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [408.0852459016394, 83.91147540983607, 2.1295245901639346], "var_": [49406.179946251, 1074.2885568395593, 0.4012853969363074], "scale_": [222.27500972050592, 32.77634141937686, 0.6334709124626855], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [6.513114772004182, 14.72131149825014], "var_": [38.776222120698904, 216.8098755196079], "scale_": [6.227055654215635, 14.724465203178278], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [365.5311475409836, 89.45245901639345, 2.0956065573770495], "var_": [51459.27230851923, 890.1831496909433, 0.4506949599570008], "scale_": [226.84636278441678, 29.835937218243092, 0.6713381859815519], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [5.616393469396185, 12.532786916758193], "var_": [32.921699516386106, 176.46679505034945], "scale_": [5.737743416743738, 13.28408051203957], "n_samples_seen_": 61}}], "clip": true}, "extras": []}
\ No newline at end of file
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_0.pt b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_0.pt
index 4eceaf8d..9ba1f252 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_0.pt and b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_0.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_1.pt b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_1.pt
index a2595c71..9ad9be75 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_1.pt and b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_1.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_2.pt b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_2.pt
index da4a0965..c6efb7aa 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_2.pt and b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_2.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_3.pt b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_3.pt
index 65f25a09..5d005282 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_3.pt and b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_3.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_4.pt b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_4.pt
index 79c04d6a..c18a4e88 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_4.pt and b/summit/benchmarks/models/reizman_suzuki_case_2/reizman_suzuki_case_2_predictor_4.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3.json b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3.json
index c8d98110..4bb48982 100644
--- a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3.json
+++ b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3.json
@@ -1,319 +1,1 @@
-{
-  "domain"           : [
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "catalyst",
-      "description" : "Catalyst type - different ligands",
-      "units"       : null,
-      "levels"      : [
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-        "P2-L1",
-        "P1-L2",
-        "P1-L3",
-        "P1-L4",
-        "P1-L5",
-        "P1-L6",
-        "P1-L7"
-      ],
-      "ds"          : null
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "t_res",
-      "description" : "Residence time in seconds (s)",
-      "units"       : null,
-      "bounds"      : [
-        60.0,
-        600.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "temperature",
-      "description" : "Reactor temperature in degrees Celsius (\u00baC)",
-      "units"       : null,
-      "bounds"      : [
-        30.0,
-        110.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "catalyst_loading",
-      "description" : "Catalyst loading in mol%",
-      "units"       : null,
-      "bounds"      : [
-        0.5,
-        2.5
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "ton",
-      "description" : "Turnover number - moles product generated divided by moles catalyst used",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        200.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "yld",
-      "description" : "Yield",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        100.0
-      ]
-    }
-  ],
-  "name"             : "ExperimentalEmulator",
-  "data"             : {
-    "index"  : [],
-    "columns": [
-      [
-        "catalyst",
-        "DATA"
-      ],
-      [
-        "t_res",
-        "DATA"
-      ],
-      [
-        "temperature",
-        "DATA"
-      ],
-      [
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-        "DATA"
-      ],
-      [
-        "ton",
-        "DATA"
-      ],
-      [
-        "yld",
-        "DATA"
-      ],
-      [
-        "computation_t",
-        "METADATA"
-      ],
-      [
-        "experiment_t",
-        "METADATA"
-      ],
-      [
-        "strategy",
-        "METADATA"
-      ]
-    ],
-    "data"   : []
-  },
-  "experiment_params": {
-    "model_name"           : "reizman_suzuki_case_3",
-    "regressor_name"       : "ANNRegressor",
-    "n_features"           : 11,
-    "n_examples"           : 96,
-    "descriptors_features" : [],
-    "output_variable_names": [
-      "ton",
-      "yld"
-    ],
-    "predictors"           : [
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-}
\ No newline at end of file
+{"domain": [{"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "catalyst", "description": "Catalyst type - different ligands", "units": null, "levels": ["P1-L1", "P2-L1", "P1-L2", "P1-L3", "P1-L4", "P1-L5", "P1-L6", "P1-L7"], "ds": null}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "t_res", "description": "Residence time in seconds (s)", "units": null, "bounds": [60.0, 600.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "temperature", "description": "Reactor temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 110.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "catalyst_loading", "description": "Catalyst loading in mol%", "units": null, "bounds": [0.5, 2.5]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": false, "name": "ton", "description": "Turnover number - moles product generated divided by moles catalyst used", "units": null, "bounds": [0.0, 200.0]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": true, "name": "yld", "description": "Yield", "units": null, "bounds": [0.0, 100.0]}], "name": "ExperimentalEmulator", "data": {"index": [], "columns": [["catalyst", "DATA"], ["t_res", "DATA"], ["temperature", "DATA"], ["catalyst_loading", "DATA"], ["ton", "DATA"], ["yld", "DATA"], ["computation_t", "METADATA"], ["experiment_t", "METADATA"], ["strategy", "METADATA"]], "data": []}, "experiment_params": {"model_name": "reizman_suzuki_case_3", "regressor_name": "ANNRegressor", "n_features": 11, "n_examples": 96, "descriptors_features": [], "output_variable_names": ["ton", "yld"], "predictors": [{"input_preprocessor": {"num": {"mean_": [251.72833333333335, 97.14833333333334, 1.566916666666667], "var_": [47050.376030555555, 657.3611638888889, 0.4318778097222222], "scale_": [216.91098642197807, 25.63905544065321, 0.6571741091386835], "n_samples_seen_": 60}}, "output_preprocessor": {"mean_": [35.49833326935768, 56.09333371246854], "var_": [681.0344985686879, 1400.3603088536445], "scale_": [26.096637687040985, 37.42138838757381], "n_samples_seen_": 60}}, {"input_preprocessor": {"num": {"mean_": [242.61147540983606, 96.20655737704918, 1.6528688524590167], "var_": [48523.75183552809, 745.9760225745767, 0.4709996221445848], "scale_": [220.28107461951444, 27.312561626009686, 0.6862941221842023], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [34.87868853548511, 57.49016414607158], "var_": [584.9813575083431, 1257.7133534539585], "scale_": [24.186387855741152, 35.4642545875979], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [235.5672131147541, 93.20819672131147, 1.593377049180328], "var_": [46069.8372856759, 826.8020639613006, 0.47456813652244023], "scale_": [214.63885315961764, 28.754166027921947, 0.6888890596623235], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [30.559016146254343, 48.531147868662586], "var_": [645.0237225195818, 1387.2529818416401], "scale_": [25.39731723075455, 37.245845162133726], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [233.6032786885246, 96.62622950819672, 1.6115737704918034], "var_": [43002.469497446924, 662.940295619457, 0.4058188019349637], "scale_": [207.3703679348786, 25.747626990063704, 0.6370390898013746], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [35.72950815079642, 55.47704955007209], "var_": [706.310607027424, 1308.7830977464985], "scale_": [26.576504793283558, 36.17710737118846], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [236.55737704918036, 94.43606557377048, 1.6498032786885244], "var_": [45848.09654393981, 819.1524697661919, 0.4757863875302337], "scale_": [214.12168629996313, 28.6208397809392, 0.6897727071508656], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [33.09672119395166, 53.89836108489115], "var_": [608.3327717911505, 1302.8926392898782], "scale_": [24.664402927927338, 36.09560415465958], "n_samples_seen_": 61}}], "clip": true}, "extras": []}
\ No newline at end of file
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_0.pt b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_0.pt
index eed8dfce..e04acb24 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_0.pt and b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_0.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_1.pt b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_1.pt
index eea459a6..c01319a6 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_1.pt and b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_1.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_2.pt b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_2.pt
index c6a62e0d..95774469 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_2.pt and b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_2.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_3.pt b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_3.pt
index f44d0431..20644509 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_3.pt and b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_3.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_4.pt b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_4.pt
index c755a6cf..96a41ce9 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_4.pt and b/summit/benchmarks/models/reizman_suzuki_case_3/reizman_suzuki_case_3_predictor_4.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4.json b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4.json
index 6b27dab7..3d13219f 100644
--- a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4.json
+++ b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4.json
@@ -1,319 +1,1 @@
-{
-  "domain"           : [
-    {
-      "type"        : "CategoricalVariable",
-      "is_objective": false,
-      "name"        : "catalyst",
-      "description" : "Catalyst type - different ligands",
-      "units"       : null,
-      "levels"      : [
-        "P1-L1",
-        "P2-L1",
-        "P1-L2",
-        "P1-L3",
-        "P1-L4",
-        "P1-L5",
-        "P1-L6",
-        "P1-L7"
-      ],
-      "ds"          : null
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "t_res",
-      "description" : "Residence time in seconds (s)",
-      "units"       : null,
-      "bounds"      : [
-        60.0,
-        600.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "temperature",
-      "description" : "Reactor temperature in degrees Celsius (\u00baC)",
-      "units"       : null,
-      "bounds"      : [
-        30.0,
-        110.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": false,
-      "name"        : "catalyst_loading",
-      "description" : "Catalyst loading in mol%",
-      "units"       : null,
-      "bounds"      : [
-        0.5,
-        2.5
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "ton",
-      "description" : "Turnover number - moles product generated divided by moles catalyst used",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        200.0
-      ]
-    },
-    {
-      "type"        : "ContinuousVariable",
-      "is_objective": true,
-      "name"        : "yld",
-      "description" : "Yield",
-      "units"       : null,
-      "bounds"      : [
-        0.0,
-        100.0
-      ]
-    }
-  ],
-  "name"             : "ExperimentalEmulator",
-  "data"             : {
-    "index"  : [],
-    "columns": [
-      [
-        "catalyst",
-        "DATA"
-      ],
-      [
-        "t_res",
-        "DATA"
-      ],
-      [
-        "temperature",
-        "DATA"
-      ],
-      [
-        "catalyst_loading",
-        "DATA"
-      ],
-      [
-        "ton",
-        "DATA"
-      ],
-      [
-        "yld",
-        "DATA"
-      ],
-      [
-        "computation_t",
-        "METADATA"
-      ],
-      [
-        "experiment_t",
-        "METADATA"
-      ],
-      [
-        "strategy",
-        "METADATA"
-      ]
-    ],
-    "data"   : []
-  },
-  "experiment_params": {
-    "model_name"           : "reizman_suzuki_case_4",
-    "regressor_name"       : "ANNRegressor",
-    "n_features"           : 11,
-    "n_examples"           : 97,
-    "descriptors_features" : [],
-    "output_variable_names": [
-      "ton",
-      "yld"
-    ],
-    "predictors"           : [
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              361.3918032786886,
-              83.57868852459015,
-              1.6783114754098363
-            ],
-            "var_"           : [
-              51894.334195108844,
-              538.3793818865896,
-              0.5491226079011019
-            ],
-            "scale_"         : [
-              227.80327959691195,
-              23.203003725522038,
-              0.7410280749749647
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            46.508196917469384,
-            68.03934463113546
-          ],
-          "var_"           : [
-            965.1315826947083,
-            914.6086297925623
-          ],
-          "scale_"         : [
-            31.066566960234088,
-            30.24249708262468
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-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              328.1016393442623,
-              85.09344262295082,
-              1.6505737704918035
-            ],
-            "var_"           : [
-              48394.58081698467,
-              625.0373340499864,
-              0.5491268675087343
-            ],
-            "scale_"         : [
-              219.98768333019163,
-              25.00074666984941,
-              0.7410309490896682
-            ],
-            "n_samples_seen_": 61
-          }
-        },
-        "output_preprocessor": {
-          "mean_"          : [
-            46.657377005111975,
-            65.44754103485678
-          ],
-          "var_"           : [
-            1047.2663762907757,
-            1006.2438135523288
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-          "scale_"         : [
-            32.361495272789476,
-            31.721346338898176
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-          "n_samples_seen_": 61
-        }
-      },
-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
-              347.89193548387095,
-              83.9274193548387,
-              1.7433870967741936
-            ],
-            "var_"           : [
-              49269.6665478668,
-              637.4981191467223,
-              0.5718076566077004
-            ],
-            "scale_"         : [
-              221.96771510259504,
-              25.24872509943269,
-              0.7561796457242819
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-        "output_preprocessor": {
-          "mean_"          : [
-            41.432258085618095,
-            63.55322592477164
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-          "var_"           : [
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-            1141.7537632219253
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-          "scale_"         : [
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-            33.78984704348224
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-        "output_preprocessor": {
-          "mean_"          : [
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-            65.95000013060147
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-          "var_"           : [
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-          "scale_"         : [
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-            32.041659757284364
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-      {
-        "input_preprocessor" : {
-          "num": {
-            "mean_"          : [
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-              86.98064516129034,
-              1.7053709677419355
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-            "var_"           : [
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-              514.798657648283,
-              0.5502617172216441
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-            "scale_"         : [
-              234.97167624383292,
-              22.68917490012105,
-              0.7417962774385188
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-            "n_samples_seen_": 62
-          }
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-        "output_preprocessor": {
-          "mean_"          : [
-            46.59516116447987,
-            67.53709707745621
-          ],
-          "var_"           : [
-            961.811094079407,
-            888.484573507381
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-          "scale_"         : [
-            31.013079403364753,
-            29.807458353696997
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-          "n_samples_seen_": 62
-        }
-      }
-    ]
-  },
-  "extras"           : []
-}
\ No newline at end of file
+{"domain": [{"type": "CategoricalVariable", "is_objective": false, "maximize": true, "name": "catalyst", "description": "Catalyst type - different ligands", "units": null, "levels": ["P1-L1", "P2-L1", "P1-L2", "P1-L3", "P1-L4", "P1-L5", "P1-L6", "P1-L7"], "ds": null}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "t_res", "description": "Residence time in seconds (s)", "units": null, "bounds": [60.0, 600.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "temperature", "description": "Reactor temperature in degrees Celsius (\u00baC)", "units": null, "bounds": [30.0, 110.0]}, {"type": "ContinuousVariable", "is_objective": false, "maximize": true, "name": "catalyst_loading", "description": "Catalyst loading in mol%", "units": null, "bounds": [0.5, 2.5]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": false, "name": "ton", "description": "Turnover number - moles product generated divided by moles catalyst used", "units": null, "bounds": [0.0, 200.0]}, {"type": "ContinuousVariable", "is_objective": true, "maximize": true, "name": "yld", "description": "Yield", "units": null, "bounds": [0.0, 100.0]}], "name": "ExperimentalEmulator", "data": {"index": [], "columns": [["catalyst", "DATA"], ["t_res", "DATA"], ["temperature", "DATA"], ["catalyst_loading", "DATA"], ["ton", "DATA"], ["yld", "DATA"], ["computation_t", "METADATA"], ["experiment_t", "METADATA"], ["strategy", "METADATA"]], "data": []}, "experiment_params": {"model_name": "reizman_suzuki_case_4", "regressor_name": "ANNRegressor", "n_features": 11, "n_examples": 97, "descriptors_features": [], "output_variable_names": ["ton", "yld"], "predictors": [{"input_preprocessor": {"num": {"mean_": [361.3918032786886, 83.57868852459015, 1.6783114754098363], "var_": [51894.334195108844, 538.3793818865896, 0.5491226079011019], "scale_": [227.80327959691195, 23.203003725522038, 0.7410280749749647], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [46.508196917469384, 68.03934463113546], "var_": [965.1315779665455, 914.6086284249595], "scale_": [31.066566884136805, 30.242497060014067], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [328.1016393442623, 85.09344262295082, 1.6505737704918035], "var_": [48394.58081698467, 625.0373340499864, 0.5491268675087343], "scale_": [219.98768333019163, 25.00074666984941, 0.7410309490896682], "n_samples_seen_": 61}}, "output_preprocessor": {"mean_": [46.657377005111975, 65.44754103485678], "var_": [1047.266364750082, 1006.2438027242844], "scale_": [32.361495094480446, 31.72134616822376], "n_samples_seen_": 61}}, {"input_preprocessor": {"num": {"mean_": [347.89193548387095, 83.9274193548387, 1.7433870967741936], "var_": [49269.6665478668, 637.4981191467223, 0.5718076566077004], "scale_": [221.96771510259504, 25.24872509943269, 0.7561796457242819], "n_samples_seen_": 62}}, "output_preprocessor": {"mean_": [41.432258085618095, 63.55322592477164], "var_": [939.9115443843737, 1141.7537812113583], "scale_": [30.657976847541224, 33.789847309678066], "n_samples_seen_": 62}}, {"input_preprocessor": {"num": {"mean_": [354.1322580645161, 83.4241935483871, 1.6983064516129034], "var_": [49819.123798126966, 571.1295759625391, 0.5281285996357961], "scale_": [223.2019798257331, 23.8983174295292, 0.7267245693079298], "n_samples_seen_": 62}}, "output_preprocessor": {"mean_": [44.380645274635285, 65.95000013060147], "var_": [961.0954281458115, 1026.6679854297238], "scale_": [31.001539125433943, 32.041660154082585], "n_samples_seen_": 62}}, {"input_preprocessor": {"num": {"mean_": [348.53225806451616, 86.98064516129034, 1.7053709677419355], "var_": [55211.68863683663, 514.798657648283, 0.5502617172216441], "scale_": [234.97167624383292, 22.68917490012105, 0.7417962774385188], "n_samples_seen_": 62}}, "output_preprocessor": {"mean_": [46.59516116447987, 67.53709707745621], "var_": [961.8110967767169, 888.4845983394254], "scale_": [31.013079446851403, 29.807458770237783], "n_samples_seen_": 62}}], "clip": true}, "extras": []}
\ No newline at end of file
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_0.pt b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_0.pt
index 4ad51272..c2dadcb7 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_0.pt and b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_0.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_1.pt b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_1.pt
index 71e0dbc6..2bc41c91 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_1.pt and b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_1.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_2.pt b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_2.pt
index 948e3ecc..6433942e 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_2.pt and b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_2.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_3.pt b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_3.pt
index c9ef64bb..6f6bb579 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_3.pt and b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_3.pt differ
diff --git a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_4.pt b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_4.pt
index b4cb6928..39badb71 100644
Binary files a/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_4.pt and b/summit/benchmarks/models/reizman_suzuki_case_4/reizman_suzuki_case_4_predictor_4.pt differ
diff --git a/summit/domain.py b/summit/domain.py
index 48adac6d..b8241a7a 100644
--- a/summit/domain.py
+++ b/summit/domain.py
@@ -85,6 +85,7 @@ def to_dict(self):
         variable_dict = {
             "type": self.__class__.__name__,
             "is_objective": self._is_objective,
+            "maximize": self.maximize,
             "name": self.name,
             "description": self.description,
             "units": self.units,
@@ -238,6 +239,7 @@ def from_dict(variable_dict):
             description=variable_dict["description"],
             bounds=variable_dict["bounds"],
             is_objective=variable_dict["is_objective"],
+            maximize=variable_dict["maximize"]
         )
 
 


Test patch:
diff --git a/summit/tests/test_benchmarks.py b/summit/tests/test_benchmarks.py
index 2470565b..7eba1e78 100644
--- a/summit/tests/test_benchmarks.py
+++ b/summit/tests/test_benchmarks.py
@@ -128,9 +128,9 @@ def test_baumgartner_CC_emulator(use_descriptors, include_cost, show_plots=False
     values = {
         "catalyst": ["tBuXPhos"],
         "base": ["DBU"],
-        "t_res": [328.717801570892],
+        "t_res": [328.7178016],
         "temperature": [30],
-        "base_equivalents": [2.18301549894049],
+        "base_equivalents": [2.183015499],
     }
     conditions = pd.DataFrame(values)
     conditions = DataSet.from_df(conditions)
@@ -138,9 +138,9 @@ def test_baumgartner_CC_emulator(use_descriptors, include_cost, show_plots=False
 
     assert str(results["catalyst"].iloc[0]) == values["catalyst"][0]
     assert str(results["base"].iloc[0]) == values["base"][0]
-    assert float(results["t_res"]) == values["t_res"][0]
-    assert float(results["temperature"]) == values["temperature"][0]
-    assert np.isclose(float(results["yld"]), 0.042832638, atol=0.15)
+    assert float(results["t_res"].iloc[0]) == values["t_res"][0]
+    assert float(results["temperature"].iloc[0]) == values["temperature"][0]
+    assert np.isclose(results["yld"].iloc[0], 0.043, atol=0.2)
 
     # Test serialization
     d = b.to_dict()
diff --git a/summit/tests/test_domain.py b/summit/tests/test_domain.py
index 1f145e26..537011a8 100644
--- a/summit/tests/test_domain.py
+++ b/summit/tests/test_domain.py
@@ -22,9 +22,9 @@ def test_continuous_variable():
         description="reaction temperature",
         bounds=[1, 100],
         is_objective=True,
-        maximize=True,
+        maximize=False,
     )
-    assert var.maximize == True
+    assert var.maximize == False
     assert var.is_objective == True
 
     # Test serialization
@@ -33,7 +33,7 @@ def test_continuous_variable():
         description="reaction temperature",
         bounds=[1, 100],
         is_objective=True,
-        maximize=True,
+        maximize=False,
     )
     ser = var.to_dict()
     new_var = ContinuousVariable.from_dict(ser)