Repository: esa/NIDN (language: python)

Issue:
Find a way to store the best physical (i.e. unnormalised) model
Merge #1 first.

- Only saving model if not renormalized
- Added some logging


Validated it in debug

![image](https://user-images.githubusercontent.com/2656685/128314020-21ce73d5-e19e-426d-996b-6f0092c39b63.png)

Make the code for the three spectrum plots more compact (almost all the lines are identical)
First merge #1 #2 #3

- Cleaned up plot_spectra
- Added test to validate cfg
- Improved validate_config
- Small type fixes in default_cfg


Discussion / hints:

Fix patch:
diff --git a/nidn/materials/__init__.py b/nidn/materials/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nidn/materials/data/aluminium_nitride.csv b/nidn/materials/data/aluminium_nitride.csv
new file mode 100644
index 0000000..acc3809
--- /dev/null
+++ b/nidn/materials/data/aluminium_nitride.csv
@@ -0,0 +1,1452 @@
+Wavelength	n	k
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+11.90476	0.88774	1.38357
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+14.12429	2.29211	2.19244
+14.20455	2.35028	2.18559
+14.28571	2.40723	2.17626
\ No newline at end of file
diff --git a/nidn/materials/data/aluminium_oxide.csv b/nidn/materials/data/aluminium_oxide.csv
new file mode 100644
index 0000000..5c6f9c7
--- /dev/null
+++ b/nidn/materials/data/aluminium_oxide.csv
@@ -0,0 +1,613 @@
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\ No newline at end of file
diff --git a/nidn/materials/data/germanium.csv b/nidn/materials/data/germanium.csv
new file mode 100644
index 0000000..e244f26
--- /dev/null
+++ b/nidn/materials/data/germanium.csv
@@ -0,0 +1,629 @@
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+10.98	3.95895	0
+11	3.95894	0
\ No newline at end of file
diff --git a/nidn/materials/data/magnesium.csv b/nidn/materials/data/magnesium.csv
new file mode 100644
index 0000000..50839fa
--- /dev/null
+++ b/nidn/materials/data/magnesium.csv
@@ -0,0 +1,141 @@
+Wavelength	n	k
+0.0000248	1	5.38e-11
+0.000031	1	1.35e-10
+0.00004133	1	4.8e-10
+0.00006199	1.001	2.04e-9
+0.000124	1	3.33e-8
+0.000155	1	8.27e-8
+0.0002066	0.9999	2.75e-7
+0.00031	0.9998	0.00000119
+0.0006199	0.9999	0.0000175
+0.0008266	1	0.0000476
+0.0009537	1	0.000015
+0.000984	1	0.00000815
+0.0009919	0.9999	0.00000709
+0.001033	1	0.00000894
+0.00124	0.9999	0.0000186
+0.00155	0.9996	0.0000441
+0.002066	0.999	0.000139
+0.00248	0.9987	0.000266
+0.002755	0.9984	0.000375
+0.0031	0.9977	0.000542
+0.003542	0.9974	0.000811
+0.004133	0.9963	0.00125
+0.004959	0.9948	0.00215
+0.006199	0.9923	0.00407
+0.006888	0.9911	0.0053
+0.007749	0.9891	0.00711
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+0.01127	0.9829	0.0193
+0.01181	0.9815	0.0225
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+0.01305	0.9838	0.031
+0.01378	0.986	0.033
+0.01393	0.9867	0.0337
+0.01409	0.987	0.0334
+0.01425	0.987	0.0334
+0.01442	0.987	0.0336
+0.01476	0.988	0.0345
+0.01512	0.988	0.0352
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+0.01631	0.984	0.0414
+0.01675	0.986	0.0475
+0.01722	0.992	0.0526
+0.01771	1	0.0533
+0.01823	1.005	0.0503
+0.01879	1.008	0.0458
+0.01937	1.008	0.0432
+0.02	1.008	0.0414
+0.02066	1.008	0.0409
+0.02101	1.008	0.041
+0.02138	1.009	0.0408
+0.02175	1.01	0.0409
+0.02214	1.011	0.0418
+0.02234	1.012	0.0412
+0.02254	1.013	0.0406
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+0.025	1.05	0.0338
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+0.0252	1.04	0.00492
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+0.02556	1.023	0.00496
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+0.02695	1.004	0.00597
+0.02818	0.997	0.00686
+0.02952	0.989	0.00736
+0.031	0.982	0.00884
+0.03263	0.975	0.0102
+0.03444	0.967	0.0114
+0.03647	0.959	0.0141
+0.03875	0.95	0.0154
+0.04133	0.939	0.0169
+0.04428	0.924	0.0196
+0.04769	0.906	0.0252
+0.05166	0.885	0.033
+0.05636	0.861	0.0454
+0.06199	0.833	0.0592
+0.06888	0.798	0.0731
+0.07749	0.741	0.0792
+0.08266	0.692	0.0916
+0.08856	0.644	0.111
+0.09537	0.577	0.127
+0.1033	0.477	0.145
+0.1078	0.404	0.158
+0.1127	0.293	0.176
+0.1181	0.11	0.296
+0.124	0.0723	0.499
+0.1305	0.0691	0.647
+0.1378	0.0717	0.787
+0.1459	0.0858	0.926
+0.155	0.115	1.06
+0.1653	0.155	1.19
+0.1698	0.171	1.23
+0.1746	0.184	1.27
+0.1797	0.194	1.31
+0.1851	0.199	1.35
+0.1907	0.197	1.39
+0.1968	0.187	1.44
+0.2033	0.166	1.49
+0.2101	0.14	1.56
+0.2175	0.121	1.64
+0.2214	0.112	1.74
+0.248	0.111	2
+0.31	0.132	2.63
+0.4133	0.184	3.66
+0.6199	0.382	5.74
+0.8266	0.791	7.96
+1.24	2.997	12.8
+1.378	4.776	14.5
+1.55	8.269	15.5
+1.771	11.2	10.2
+1.907	8.809	8.98
+2.066	6.056	9.33
+2.254	4.194	12.2
+2.48	4.509	14.7
+2.755	5.056	16.8
+3.1	6.069	19.5
+3.542	7.519	22.2
+4.133	9.516	25.1
+4.959	12.3	28.8
+6.199	15.2	32.6
+8.266	18.9	37.6
+12.4	23.8	48.8
+24.8	30.3	60.2
\ No newline at end of file
diff --git a/nidn/materials/data/readme.txt b/nidn/materials/data/readme.txt
new file mode 100644
index 0000000..767732a
--- /dev/null
+++ b/nidn/materials/data/readme.txt
@@ -0,0 +1,17 @@
+
+
+
+Ag, silver: Hageman et al. 1974, 2.48e-6 - 248
+Ge, germanium: Amotchkina et al, 2020, 0.4 - 11
+Mg, magnesium: Hageman et al. 1974, 2.48e-6 - 248
+Si3N4, silicon nitride: Kischkat et al, 2021, 1.54 - 14.29 
+AlN, aluminium nitride: Kischkat et al, 2021, 1.54 - 14.29 
+Al2O3, aluminium oxide: Querry, 1985, 0.21 - 55.6 (o)
+SiO2, silicon oxide:
+    Thin film: Kischkat et al, 2012, 1.54 - 7
+    Fused silica: Popova et al, 1972, 7 - 50
+Ta2O5, tantalum pentoxide:  
+    Bulk: Rodriguez-deMarcos et al, 2016: 0.03 - 1.51
+    Nanocrystalline film: Bright et al, 2013: 1.52 - 1000 (cut off at 40)
+TiO2, titanium oxide: Siefke et al, 2016, 0.120 - 125 (only took 0.2 - 40)
+ZnO, zinc oxide (pellet): Querry, 1985, 0.21 - 55.6
\ No newline at end of file
diff --git a/nidn/materials/data/silicon_nitride.csv b/nidn/materials/data/silicon_nitride.csv
new file mode 100644
index 0000000..8ff16d4
--- /dev/null
+++ b/nidn/materials/data/silicon_nitride.csv
@@ -0,0 +1,1452 @@
+Wavelength	n	k
+1.53846	2.46306	0.00003
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\ No newline at end of file
diff --git a/nidn/materials/data/silicon_oxide.csv b/nidn/materials/data/silicon_oxide.csv
new file mode 100644
index 0000000..86fb949
--- /dev/null
+++ b/nidn/materials/data/silicon_oxide.csv
@@ -0,0 +1,1469 @@
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\ No newline at end of file
diff --git a/nidn/materials/data/silver.csv b/nidn/materials/data/silver.csv
new file mode 100644
index 0000000..bd3eeef
--- /dev/null
+++ b/nidn/materials/data/silver.csv
@@ -0,0 +1,149 @@
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+12.4	9.936	90.2
+24.8	36.7	173
+49.59	118	306
+124	309	506
+248	531	689
\ No newline at end of file
diff --git a/nidn/materials/data/tantalum_pentoxide.csv b/nidn/materials/data/tantalum_pentoxide.csv
new file mode 100644
index 0000000..47f6455
--- /dev/null
+++ b/nidn/materials/data/tantalum_pentoxide.csv
@@ -0,0 +1,429 @@
+Wavelength	n	k
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+38.4	2.3593	3.9055
+38.989	2.4695	4.0944
+39.588	2.6058	4.2833
+40.195	2.77	4.469
\ No newline at end of file
diff --git a/nidn/materials/data/titanium_oxide.csv b/nidn/materials/data/titanium_oxide.csv
new file mode 100644
index 0000000..454edfa
--- /dev/null
+++ b/nidn/materials/data/titanium_oxide.csv
@@ -0,0 +1,922 @@
+Wavelength	n	k
+0.200602492	1.919332825	1.273978672
+0.201760583	1.92481223	1.278812136
+0.202925359	1.930641143	1.283915961
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\ No newline at end of file
diff --git a/nidn/materials/data/zinc_oxide.csv b/nidn/materials/data/zinc_oxide.csv
new file mode 100644
index 0000000..c300cce
--- /dev/null
+++ b/nidn/materials/data/zinc_oxide.csv
@@ -0,0 +1,612 @@
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+3.1447	1.729	0.032
+3.1546	1.731	0.032
+3.1646	1.73	0.031
+3.1746	1.728	0.031
+3.1847	1.73	0.032
+3.1949	1.732	0.032
+3.2051	1.729	0.031
+3.2154	1.729	0.032
+3.2258	1.733	0.033
+3.2362	1.733	0.032
+3.2468	1.73	0.032
+3.2573	1.733	0.033
+3.268	1.735	0.033
+3.2787	1.736	0.032
+3.2895	1.735	0.032
+3.3003	1.733	0.032
+3.3113	1.737	0.033
+3.3223	1.738	0.032
+3.3333	1.736	0.032
+3.3445	1.734	0.031
+3.3557	1.733	0.032
+3.367	1.735	0.032
+3.3784	1.735	0.032
+3.3898	1.735	0.032
+3.4014	1.732	0.032
+3.413	1.731	0.032
+3.4247	1.731	0.033
+3.4364	1.735	0.033
+3.4483	1.734	0.033
+3.4602	1.737	0.033
+3.4722	1.736	0.033
+3.4843	1.734	0.033
+3.4965	1.732	0.033
+3.5088	1.734	0.034
+3.5211	1.736	0.035
+3.5336	1.74	0.035
+3.5461	1.738	0.033
+3.5587	1.736	0.034
+3.5714	1.741	0.035
+3.5842	1.74	0.033
+3.5971	1.737	0.034
+3.6101	1.741	0.034
+3.6232	1.74	0.034
+3.6364	1.743	0.034
+3.6496	1.739	0.033
+3.663	1.74	0.035
+3.6765	1.741	0.034
+3.69	1.74	0.034
+3.7037	1.739	0.034
+3.7175	1.74	0.035
+3.7313	1.743	0.034
+3.7453	1.74	0.034
+3.7594	1.741	0.035
+3.7736	1.742	0.034
+3.7879	1.744	0.034
+3.8023	1.743	0.033
+3.8168	1.74	0.034
+3.8314	1.743	0.034
+3.8462	1.745	0.034
+3.861	1.742	0.033
+3.876	1.74	0.034
+3.8911	1.742	0.034
+3.9063	1.744	0.034
+3.9216	1.745	0.034
+3.937	1.745	0.034
+3.9526	1.744	0.033
+3.9683	1.744	0.033
+3.9841	1.742	0.033
+4	1.743	0.034
+4.0161	1.745	0.033
+4.0323	1.744	0.032
+4.0486	1.742	0.032
+4.065	1.742	0.033
+4.0816	1.743	0.033
+4.0984	1.743	0.033
+4.1152	1.744	0.033
+4.1322	1.744	0.032
+4.1494	1.742	0.032
+4.1667	1.741	0.032
+4.1841	1.744	0.033
+4.2017	1.741	0.031
+4.2194	1.742	0.032
+4.2373	1.741	0.031
+4.2553	1.739	0.032
+4.2735	1.738	0.032
+4.2918	1.739	0.033
+4.3103	1.741	0.032
+4.329	1.74	0.032
+4.3478	1.738	0.032
+4.3668	1.739	0.032
+4.386	1.738	0.032
+4.4053	1.738	0.032
+4.4248	1.738	0.032
+4.4444	1.737	0.031
+4.4643	1.736	0.032
+4.4843	1.736	0.032
+4.5045	1.734	0.032
+4.5249	1.737	0.032
+4.5455	1.736	0.032
+4.5662	1.737	0.032
+4.5872	1.737	0.032
+4.6083	1.735	0.032
+4.6296	1.735	0.032
+4.6512	1.735	0.031
+4.6729	1.732	0.031
+4.6948	1.733	0.032
+4.717	1.73	0.032
+4.7393	1.731	0.033
+4.7619	1.732	0.033
+4.7847	1.732	0.032
+4.8077	1.732	0.033
+4.8309	1.733	0.032
+4.8544	1.73	0.033
+4.878	1.731	0.033
+4.902	1.73	0.033
+4.9261	1.729	0.033
+4.9505	1.729	0.034
+4.9751	1.729	0.034
+5	1.73	0.036
+5.0251	1.734	0.036
+5.0505	1.734	0.034
+5.0761	1.734	0.033
+5.102	1.733	0.033
+5.1282	1.731	0.033
+5.1546	1.731	0.033
+5.1813	1.731	0.033
+5.2083	1.73	0.033
+5.2356	1.731	0.033
+5.2632	1.73	0.032
+5.291	1.73	0.032
+5.3191	1.729	0.032
+5.3476	1.729	0.031
+5.3763	1.726	0.031
+5.4054	1.727	0.032
+5.4348	1.729	0.032
+5.4645	1.727	0.03
+5.4945	1.725	0.03
+5.5249	1.726	0.03
+5.5556	1.724	0.03
+5.5866	1.724	0.03
+5.618	1.723	0.029
+5.6497	1.722	0.028
+5.6818	1.718	0.027
+5.7143	1.713	0.028
+5.7471	1.713	0.03
+5.7803	1.713	0.031
+5.814	1.713	0.032
+5.848	1.714	0.032
+5.8824	1.716	0.031
+5.9172	1.712	0.03
+5.9524	1.713	0.031
+5.988	1.713	0.03
+6.0241	1.71	0.031
+6.0606	1.712	0.031
+6.0976	1.709	0.028
+6.135	1.706	0.029
+6.1728	1.706	0.03
+6.2112	1.705	0.03
+6.25	1.702	0.03
+6.2893	1.701	0.031
+6.3291	1.701	0.031
+6.3694	1.699	0.031
+6.4103	1.702	0.033
+6.4516	1.702	0.03
+6.4935	1.699	0.03
+6.5359	1.695	0.03
+6.5789	1.695	0.03
+6.6225	1.694	0.03
+6.6667	1.691	0.031
+6.7114	1.692	0.031
+6.7568	1.688	0.03
+6.8027	1.685	0.031
+6.8493	1.684	0.031
+6.8966	1.681	0.032
+6.9444	1.683	0.034
+6.993	1.682	0.034
+7.0423	1.682	0.034
+7.0922	1.677	0.034
+7.1429	1.679	0.036
+7.1942	1.677	0.033
+7.2464	1.673	0.034
+7.2993	1.67	0.037
+7.4074	1.673	0.038
+7.4627	1.671	0.035
+7.5188	1.667	0.036
+7.5758	1.666	0.037
+7.6336	1.665	0.038
+7.6923	1.663	0.037
+7.7519	1.661	0.037
+7.8125	1.659	0.037
+7.874	1.657	0.037
+7.9365	1.655	0.039
+8	1.654	0.037
+8.0645	1.649	0.04
+8.1301	1.651	0.042
+8.1967	1.649	0.039
+8.2645	1.644	0.039
+8.3333	1.643	0.04
+8.4034	1.64	0.04
+8.4746	1.638	0.041
+8.547	1.635	0.039
+8.6207	1.63	0.04
+8.6957	1.627	0.041
+8.7719	1.624	0.043
+8.8496	1.621	0.043
+8.9286	1.618	0.044
+9.009	1.615	0.044
+9.0909	1.61	0.045
+9.1743	1.608	0.048
+9.2593	1.605	0.049
+9.3458	1.601	0.049
+9.434	1.597	0.05
+9.5238	1.592	0.052
+9.6154	1.588	0.054
+9.7087	1.586	0.058
+9.8039	1.584	0.057
+9.901	1.578	0.058
+10	1.574	0.06
+10.101	1.574	0.062
+10.2041	1.569	0.059
+10.3093	1.562	0.06
+10.4167	1.559	0.061
+10.5263	1.554	0.059
+10.6383	1.546	0.061
+10.7527	1.541	0.061
+10.8696	1.533	0.062
+10.989	1.528	0.066
+11.1111	1.523	0.065
+11.236	1.515	0.067
+11.3636	1.51	0.069
+11.4943	1.504	0.069
+11.6279	1.499	0.069
+11.7647	1.491	0.067
+11.9048	1.484	0.066
+12.0482	1.473	0.062
+12.1951	1.46	0.062
+12.3457	1.448	0.062
+12.5	1.436	0.063
+12.6582	1.422	0.062
+12.8205	1.408	0.063
+12.987	1.392	0.062
+13.1579	1.375	0.062
+13.3333	1.354	0.064
+13.5135	1.335	0.069
+13.6986	1.317	0.071
+13.8889	1.296	0.073
+14.0845	1.272	0.073
+14.2857	1.246	0.076
+14.4928	1.215	0.075
+14.7059	1.183	0.081
+14.9254	1.15	0.083
+15.1515	1.109	0.084
+15.3846	1.063	0.086
+15.625	1.004	0.089
+15.873	0.936	0.097
+16.129	0.859	0.11
+16.3934	0.765	0.125
+16.6667	0.631	0.152
+16.9492	0.462	0.25
+17.2414	0.331	0.421
+17.5439	0.258	0.621
+17.8571	0.235	0.818
+18.1818	0.236	1.001
+18.5185	0.254	1.179
+18.8679	0.286	1.35
+19.2308	0.328	1.522
+19.6078	0.384	1.693
+20	0.451	1.861
+20.4082	0.521	2.026
+20.8333	0.587	2.199
+21.2766	0.644	2.4
+21.7391	0.704	2.659
+22.2222	0.797	3.017
+22.7273	1.021	3.51
+23.2558	1.509	4.071
+23.8095	2.434	4.628
+24.3902	3.94	4.458
+25	4.884	3.404
+25.641	5.09	2.304
+26.3158	4.985	1.505
+27.027	4.644	0.838
+27.7778	4.223	0.533
+28.5714	3.91	0.366
+29.4118	3.646	0.301
+30.303	3.484	0.289
+31.25	3.376	0.256
+32.2581	3.283	0.222
+33.3333	3.193	0.182
+34.4828	3.105	0.162
+35.7143	3.036	0.147
+37.037	2.972	0.133
+38.4615	2.915	0.127
+40	2.864	0.127
+41.6667	2.83	0.135
+43.4783	2.803	0.132
+45.4545	2.775	0.129
+47.619	2.754	0.124
+50	2.725	0.108
+52.6316	2.697	0.109
+55.5556	2.664	0.121
\ No newline at end of file
diff --git a/nidn/materials/material_collection.py b/nidn/materials/material_collection.py
new file mode 100644
index 0000000..59b58d8
--- /dev/null
+++ b/nidn/materials/material_collection.py
@@ -0,0 +1,80 @@
+import pandas
+import torch
+import os, sys
+from glob import glob
+
+from loguru import logger
+
+
+class MaterialCollection:
+    """Database of materials included with NIDN"""
+
+    epsilon_matrix = None
+    material_names = []
+    N_materials = 0
+
+    def __init__(self, target_frequencies):
+        """Initalizes the MaterialCollection. Loads all materials from the data folder.
+
+        Args:
+            target_frequencies (list): Frequencies we are targeting. Closest ones in the data will be used.
+        """
+        logger.trace("Initializing material collection")
+        self.epsilon_matrix = None
+        self.material_names = []
+        self.target_frequencies = target_frequencies
+        self.materials_folder = os.path.dirname(__file__) + "/data/"
+        self._load_materials_folder()
+
+    def _load_materials_folder(self):
+        """Loads all csv files from folder "data"
+        and sets up the EPS_MATRIX and MATERIAL_NAMES
+        """
+        logger.trace(f"Loading materials from folder: {self.materials_folder}")
+
+        # Load materials data
+        files = glob(self.materials_folder + "/*.csv")
+        logger.debug("Found the following files")
+        logger.debug(files)
+
+        eps_list = []
+        for file in files:
+            # Load material epsilon (permittivity)
+            eps_list.append(self._load_material_data(file))
+
+            # Remember file name as material name
+            file = file.replace("\\", "/")
+            self.material_names.append(file.split("/")[-1].split(".csv")[0])
+
+        # Create a single tensor of all materials
+        logger.trace("Creating material tensor")
+        self.epsilon_matrix = torch.stack(eps_list).squeeze()  # .cuda()
+        self.N_materials = len(self.material_names)
+
+    def _load_material_data(self, name):
+        """Loads the passed wavelength,n,k data from the passed csv file for the closest frequencies and returns epsilon (permittivity).
+
+        Args:
+            name (str): Path to csv
+
+        Returns:
+            torch.tensor: Epsilon for the material (permittivity)
+        """
+        logger.trace(f"Loading material {name}")
+        csv_data = pandas.read_csv(name, delimiter="\t")
+
+        eps = []
+        # Compute epsilon from n and k
+        # see e.g. https://www.tf.uni-kiel.de/matwis/amat/elmat_en/kap_3/backbone/r3_7_2.html
+        logger.trace("Computing epsilon from n and k for each target frequency")
+        for freq in self.target_frequencies:
+            wl = 1.0 / freq
+            result_index = csv_data["Wavelength"].sub(wl).abs().idxmin()
+            entry = csv_data.iloc[result_index]
+            real = entry.n * entry.n - entry.k * entry.k
+            imag = 2 * entry.n * entry.k
+            eps.append([real + imag * 1.0j])
+
+        eps = torch.tensor(eps)
+        logger.debug(f"Epsilon for material {name} is: {eps}")
+        return eps
diff --git a/nidn/plots/plot_model_grid.py b/nidn/plots/plot_model_grid.py
index 5e97b5e..cb64d50 100644
--- a/nidn/plots/plot_model_grid.py
+++ b/nidn/plots/plot_model_grid.py
@@ -12,7 +12,7 @@ def plot_model_grid(model, run_cfg):
         run_cfg (dict): The run configuration.
     """
     Nx, Ny, N_layers = run_cfg.Nx, run_cfg.Ny, run_cfg.N_layers
-    eps = model_to_eps_grid(model, run_cfg)
+    eps, _ = model_to_eps_grid(model, run_cfg)
     x = torch.linspace(-1, 1, Nx)
     y = torch.linspace(-1, 1, Ny)
     z = torch.linspace(-1, 1, N_layers)
diff --git a/nidn/plots/plot_model_grid_per_freq.py b/nidn/plots/plot_model_grid_per_freq.py
index 35954b8..7f329cb 100644
--- a/nidn/plots/plot_model_grid_per_freq.py
+++ b/nidn/plots/plot_model_grid_per_freq.py
@@ -14,7 +14,8 @@ def plot_model_grid_per_freq(model, run_cfg, freq_idx=[0, 1, 2, 3]):
         freq_idx (list of int): Which of the frequency indices in target_frequencies we want to plot. Defaults to [0, 1, 2, 3].
     """
     Nx, Ny, N_layers = run_cfg.Nx, run_cfg.Ny, run_cfg.N_layers
-    eps = model_to_eps_grid(model, run_cfg)
+    eps, _ = model_to_eps_grid(model, run_cfg)
+
     x = torch.linspace(-1, 1, Nx)
     y = torch.linspace(-1, 1, Ny)
     z = torch.linspace(-1, 1, N_layers)
diff --git a/nidn/plots/plot_spectra.py b/nidn/plots/plot_spectra.py
index e85b815..e9f2589 100644
--- a/nidn/plots/plot_spectra.py
+++ b/nidn/plots/plot_spectra.py
@@ -38,7 +38,7 @@ def plot_spectra(model, run_cfg, target_R_spectrum, target_T_spectrum):
     """
 
     # Create epsilon grid from the model
-    eps = model_to_eps_grid(model, run_cfg)
+    eps, _ = model_to_eps_grid(model, run_cfg)
 
     # Compute the spectra for the given epsilon values
     prod_R_spectrum, prod_T_spectrum = compute_spectrum(eps, run_cfg)
diff --git a/nidn/training/model/init_network.py b/nidn/training/model/init_network.py
index ca83c00..1fed861 100644
--- a/nidn/training/model/init_network.py
+++ b/nidn/training/model/init_network.py
@@ -13,10 +13,11 @@ def init_network(
     Returns:
         torch model: Initialized model
     """
-    logger.debug("Initializing model..."+run_cfg.model_type)
+    logger.debug("Initializing model..." + run_cfg.model_type)
     if run_cfg.model_type == "nerf":
         return NERF(
             in_features=run_cfg.encoding_dim,
+            out_features=run_cfg.out_features,
             n_neurons=run_cfg.n_neurons,
             activation=nn.Sigmoid(),
             skip=[4],
diff --git a/nidn/training/model/model_to_eps_grid.py b/nidn/training/model/model_to_eps_grid.py
index 701e8c2..e23a3a8 100644
--- a/nidn/training/model/model_to_eps_grid.py
+++ b/nidn/training/model/model_to_eps_grid.py
@@ -2,28 +2,27 @@
 from dotmap import DotMap
 
 
-def model_to_eps_grid(model, run_cfg: DotMap):
-    """Computes a 4D grid of epsilons for the given model.
+def _eval_model(model, Nx_undersampled, Ny_undersampled, N_layers, target_frequencies):
+    """Evaluates the model on the grid.
 
     Args:
-        model (torch.model): Trained neural network model. Should map one 4D input to a [real,imag] epsilon.
-        run_cfg (DotMap): Configuration for the run.
-
-    Returns:
-        [torch.tensor]: Resulting 4D [real,imag] epsilon grid
+        model (torch.model): Trained neural network model. Should map one 4D input.
+        Nx_undersampled (int): Number of grid points in x direction. Potentially unesampled if eps_oversampling > 1.
+        Ny_undersampled (int): Number of grid points in y direction. Potentially unesampled if eps_oversampling > 1.
+        N_layers (int): Number of layers in the model.
+        target_frequencies (list): Target frequencies.
     """
-
     # Get the grid ticks
-    x = torch.linspace(-1, 1, run_cfg.Nx)
-    y = torch.linspace(-1, 1, run_cfg.Ny)
-    z = torch.linspace(-1, 1, run_cfg.N_layers)
+    x = torch.linspace(-1, 1, Nx_undersampled)
+    y = torch.linspace(-1, 1, Ny_undersampled)
+    z = torch.linspace(-1, 1, N_layers)
 
     # Linearly spaced frequency points
     #     freq = torch.linspace(-1,1,len(run_cfg.target_frequencies))
 
     # Logspaced frequency points
     # Normalize to max val = 1
-    freq = torch.tensor(run_cfg.target_frequencies / max(run_cfg.target_frequencies))
+    freq = torch.tensor(target_frequencies / max(target_frequencies))
     # Transform to -1 to 1
     freq = (freq * 2) - 1
 
@@ -37,11 +36,39 @@ def model_to_eps_grid(model, run_cfg: DotMap):
     )
 
     # Compute model output at each grid point
-    out = model(nn_inputs)
+    return model(nn_inputs)
+
+
+def _regression_model_to_eps_grid(model, run_cfg: DotMap):
+    """Computes a 4D grid of epsilons for a regression model.
+
+    Args:
+        model (torch.model): Trained neural network model. Should map one 4D input to a [real,imag] epsilon.
+        run_cfg (DotMap): Configuration for the run.
+
+    Returns:
+        [torch.tensor]: Resulting 4D [real,imag] epsilon grid
+    """
+
+    Nx_undersampled = run_cfg.Nx // run_cfg.eps_oversampling
+    Ny_undersampled = run_cfg.Ny // run_cfg.eps_oversampling
+
+    # Evaluate the model at the grid points
+    out = _eval_model(
+        model,
+        Nx_undersampled,
+        Ny_undersampled,
+        run_cfg.N_layers,
+        run_cfg.target_frequencies,
+    )
+
+    print(out.shape)
 
     # Reshape the output to have a 4D tensor again
     # Note we ouput real and imaginary parts separatly, hence 2*N_freq
-    out = out.reshape(run_cfg.Nx, run_cfg.Ny, run_cfg.N_layers, 2 * run_cfg.N_freq)
+    out = out.reshape(
+        Nx_undersampled, Ny_undersampled, run_cfg.N_layers, 2 * run_cfg.N_freq
+    )
 
     # Oversample the created eps grid
     out = torch.repeat_interleave(out, run_cfg.eps_oversampling, dim=0)
@@ -66,3 +93,76 @@ def model_to_eps_grid(model, run_cfg: DotMap):
     )
 
     return eps
+
+
+def _classification_model_to_eps_grid(model, run_cfg: DotMap):
+    """Computes a 4D grid of epsilons for a classification model.
+
+    Args:
+        model (torch.model): Trained neural network model. Should map one 4D input to a material index.
+        run_cfg (DotMap): Configuration for the run.
+
+    Returns:
+        [torch.tensor]: Resulting 4D [real,imag] epsilon grid
+    """
+
+    Nx_undersampled = run_cfg.Nx // run_cfg.eps_oversampling
+    Ny_undersampled = run_cfg.Ny // run_cfg.eps_oversampling
+
+    # Evaluate the model at the grid points
+    out = _eval_model(
+        model,
+        Nx_undersampled,
+        Ny_undersampled,
+        run_cfg.N_layers,
+        run_cfg.target_frequencies,
+    )
+
+    # Reshape the output to have a 4D tensor again
+    # Note we output a likelihood for each material
+    out = out.reshape(
+        Nx_undersampled,
+        Ny_undersampled,
+        run_cfg.N_layers,
+        run_cfg.N_freq,
+        run_cfg.N_materials,
+    )
+
+    # Oversample the created grid
+    out = torch.repeat_interleave(out, run_cfg.eps_oversampling, dim=0)
+    out = torch.repeat_interleave(out, run_cfg.eps_oversampling, dim=1)
+
+    # We take mean over the frequency dimension in determining the fitting
+    out = torch.mean(out, dim=3)
+
+    #     material_id = torch.softmax(out,dim=-1)
+    # Apply softmax to get a probability distribution
+    beta = 1000
+    # Softmax with a high beta to push towards 1
+    material_id = (beta ** out) / (beta ** out).sum(dim=1).unsqueeze(1)
+
+    material_id = torch.divide(material_id, material_id.sum(-1).unsqueeze(-1))
+
+    # Convert material probabilities to epsilons
+    eps = (material_id.unsqueeze(-1) * run_cfg.material_collection.epsilon_matrix).sum(
+        -2
+    )
+
+    return eps, material_id
+
+
+def model_to_eps_grid(model, run_cfg: DotMap):
+    """Computes a 4D grid of epsilons for the given model.
+
+    Args:
+        model (torch.model): Trained neural network model. Should map one 4D input to a [real,imag] epsilon.
+        run_cfg (DotMap): Configuration for the run.
+
+    Returns:
+        torch.tensor, torch.tensor: Resulting 4D [real,imag] epsilon grid and tensor of the material_ids (None for regression)
+    """
+    if run_cfg.type == "classification":
+        return _classification_model_to_eps_grid(model, run_cfg)
+    elif run_cfg.type == "regression":
+        # For consistent returns we return a tuple with None
+        return _regression_model_to_eps_grid(model, run_cfg), None
diff --git a/nidn/training/run_training.py b/nidn/training/run_training.py
index 5c38329..2e92ccc 100644
--- a/nidn/training/run_training.py
+++ b/nidn/training/run_training.py
@@ -7,37 +7,38 @@
 from copy import deepcopy
 from loguru import logger
 
-from ..utils.fix_random_seeds import fix_random_seeds
+from .losses.spectrum_loss import _spectrum_loss_fn
+from ..materials.material_collection import MaterialCollection
 from .model.init_network import init_network
+from .model.model_to_eps_grid import model_to_eps_grid
 from ..trcwa.compute_spectrum import compute_spectrum
 from ..trcwa.compute_target_frequencies import compute_target_frequencies
-from .model.model_to_eps_grid import model_to_eps_grid
-from .losses.spectrum_loss import _spectrum_loss_fn
+from ..utils.fix_random_seeds import fix_random_seeds
 from .utils.validate_config import _validate_config
 
 
-def run_training(
-    run_cfg: DotMap,
-    target_reflectance_spectrum: npt.NDArray,
-    target_transmittance_spectrum: npt.NDArray,
-    model=None,
-):
-    """Runs a training run with the passed config, target reflectance and transmittance spectra. Optionally a model can be passed to continue training.
 
+def _init_training(run_cfg: DotMap, model):
+    """Initializes additional parameters required for training.
     Args:
         run_cfg (DotMap): Run configuration.
-        target_reflectance_spectrum (np.array): Target reflectance spectrum.
-        target_transmittance_spectrum (np.array): Target transmittance spectrum.
-        model (torch.model, optional): Model to continue training. If None, a new model will be created according to the run configuration. Defaults to None.
+        model (torch.model, optional): Model to continue training. If None, a new model will be created according to the run configuration.
 
     Returns:
-        torch.model,DotMap: The best model achieved in the training run, and the loss results of the training run.
+        DotMap, torch.model, torch.opt, torch.scheduler: Run config with additional entries, model, optimizer, scheduler
     """
-    logger.trace("Initializing training...")
 
     # Validate config
     _validate_config(run_cfg)
 
+    if run_cfg.type == "classification":
+        run_cfg.material_collection = MaterialCollection(run_cfg.target_frequencies)
+        run_cfg.N_materials = run_cfg.material_collection.N_materials
+        # If classification, the model outputs a likelihood for the presence of each material.
+        run_cfg.out_features = run_cfg.N_materials
+    else:
+        run_cfg.out_features = 2  # For regression, the model outputs epsilon directly.
+
     # Fix random seed for reproducibility
     fix_random_seeds(run_cfg.seed)
 
@@ -61,6 +62,31 @@ def run_training(
         optimizer, factor=0.75, patience=200, min_lr=1e-6, verbose=True
     )
 
+    return run_cfg, model, optimizer, scheduler
+
+
+def run_training(
+    run_cfg: DotMap,
+    target_reflectance_spectrum: npt.NDArray,
+    target_transmittance_spectrum: npt.NDArray,
+    model=None,
+):
+    """Runs a training run with the passed config, target reflectance and transmittance spectra. Optionally a model can be passed to continue training.
+
+    Args:
+        run_cfg (DotMap): Run configuration.
+        target_reflectance_spectrum (np.array): Target reflectance spectrum.
+        target_transmittance_spectrum (np.array): Target transmittance spectrum.
+        model (torch.model, optional): Model to continue training. If None, a new model will be created according to the run configuration. Defaults to None.
+
+    Returns:
+        torch.model,DotMap: The best model achieved in the training run, and the loss results of the training run.
+    """
+    logger.trace("Initializing training...")
+
+    # Initialize training parameters, model and optimizer etc.
+    run_cfg, model, optimizer, scheduler = _init_training(run_cfg, model)
+
     results = DotMap()
 
     # When a new network is created we init empty training logs
@@ -78,10 +104,23 @@ def run_training(
         torch.cuda.empty_cache()
 
         # Compute the epsilon values from the model
-        eps_grid = model_to_eps_grid(model, run_cfg)
+        eps_grid, material_ids = model_to_eps_grid(model, run_cfg)
 
         # Compute the spectrum using TRCWA for this grid
-        produced_R_spectrum, produced_T_spectrum = compute_spectrum(eps_grid, run_cfg)
+        try:
+            produced_R_spectrum, produced_T_spectrum = compute_spectrum(
+                eps_grid, run_cfg
+            )
+        except ValueError:
+            logger.warning(
+                "ValueError encountered in compute_spectrum. This likely means the LR was too high. Reloading best model, and reducing LR."
+            )
+            model.load_state_dict(best_model_state_dict)
+            logger.info(
+                "Setting LR to {}".format(optimizer.param_groups[0]["lr"] * 0.5)
+            )
+            optimizer.param_groups[0]["lr"] *= 0.5
+            continue
 
         # Compute loss between target spectrum and
         # the one from the current network structure
diff --git a/nidn/training/utils/nerf.py b/nidn/training/utils/nerf.py
index 4f58a66..0f5ab59 100644
--- a/nidn/training/utils/nerf.py
+++ b/nidn/training/utils/nerf.py
@@ -25,6 +25,7 @@ class NERF(nn.Module):
     def __init__(
         self,
         in_features,
+        out_features,
         skip=[],
         n_neurons=100,
         activation=nn.Sigmoid(),
@@ -43,7 +44,7 @@ def __init__(
             else:
                 self.net.append(NERFLayer(n_neurons, n_neurons))
 
-        self.net.append(NERFLayer(n_neurons, 1, activation=activation))
+        self.net.append(NERFLayer(n_neurons, out_features, activation=activation))
 
     def forward(self, x):
         # save for skip connection
diff --git a/nidn/training/utils/validate_config.py b/nidn/training/utils/validate_config.py
index 98899b2..327b970 100644
--- a/nidn/training/utils/validate_config.py
+++ b/nidn/training/utils/validate_config.py
@@ -23,7 +23,6 @@ def _validate_config(cfg: DotMap):
         "N_freq",
         "model_type",
         "encoding_dim",
-        "out_features",
         "siren_omega",
         "n_neurons",
         "hidden_layers",
@@ -31,6 +30,7 @@ def _validate_config(cfg: DotMap):
         "iterations",
         "L",
         "absorption_loss",
+        "type",
     ]
 
     for key in required_keys:
@@ -44,7 +44,6 @@ def _validate_config(cfg: DotMap):
         "N_layers",
         "N_freq",
         "encoding_dim",
-        "out_features",
         "n_neurons",
         "eps_oversampling",
         "seed",
@@ -58,9 +57,7 @@ def _validate_config(cfg: DotMap):
         "siren_omega",
     ]
     boolean_keys = []
-    string_keys = [
-        "model_type",
-    ]
+    string_keys = ["model_type", "type"]
     for key in integer_keys:
         if not isinstance(cfg[key], int):
             raise ValueError(f"{key} must be an integer")
@@ -106,3 +103,6 @@ def _validate_config(cfg: DotMap):
         raise ValueError(f"Nx must be a multiple of eps_oversampling")
     if not cfg.Ny % cfg.eps_oversampling == 0:
         raise ValueError(f"Ny must be a multiple of eps_oversampling")
+
+    if cfg.type != "classification" and cfg.type != "regression":
+        raise ValueError(f"type must be either 'classification' or 'regression'")
diff --git a/nidn/utils/print_cfg.py b/nidn/utils/print_cfg.py
index dd05c9a..7d1e456 100644
--- a/nidn/utils/print_cfg.py
+++ b/nidn/utils/print_cfg.py
@@ -1,6 +1,8 @@
 from dotmap import DotMap
 import numpy as np
 
+from ..materials.material_collection import MaterialCollection
+
 
 def print_cfg(cfg: DotMap):
     """Prints the config in a more readable way.
@@ -15,6 +17,8 @@ def print_cfg(cfg: DotMap):
             print()
             print(f"{key}: {value}")
             idx = 0
+        elif isinstance(value, MaterialCollection):
+            print()
         else:
             if idx % 3 == 2:
                 print(f"{key:<20}: {value:<20}")
diff --git a/nidn/utils/resources/default_config.toml b/nidn/utils/resources/default_config.toml
index 655d117..bdb88fc 100644
--- a/nidn/utils/resources/default_config.toml
+++ b/nidn/utils/resources/default_config.toml
@@ -3,6 +3,7 @@ seed = 42
 model_type = "siren"
 iterations = 2000
 learning_rate = 6.5e-5
+type = "classification" # "classification" or "regression"
 
 # Loss
 L = 0.5
@@ -12,7 +13,6 @@ absorption_loss = false
 n_neurons = 100
 hidden_layers = 9
 encoding_dim = 4
-out_features = 2
 siren_omega = 1.0
 
 # Epsilon Properties
diff --git a/notebooks/Training.ipynb b/notebooks/Training.ipynb
index 45306d6..7edde8f 100644
--- a/notebooks/Training.ipynb
+++ b/notebooks/Training.ipynb
@@ -24,7 +24,7 @@
     "# Set up some logging\n",
     "from loguru import logger\n",
     "logger.remove()\n",
-    "logger.add(sys.stderr, format=\"{level} {message}\", level=\"DEBUG\");"
+    "logger.add(sys.stderr, format=\"{level} {message}\", level=\"INFO\");"
    ]
   },
   {
@@ -40,17 +40,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dd459f38",
+   "id": "26ccf8c2",
    "metadata": {},
    "outputs": [],
    "source": [
     "cfg.Nx = 9\n",
     "cfg.Ny = 9\n",
-    "cfg.N_layers = 2\n",
-    "cfg.iterations = 50\n",
+    "cfg.N_layers = 3\n",
+    "cfg.eps_oversampling = 3\n",
+    "cfg.iterations = 100\n",
     "nidn.print_cfg(cfg)\n",
     "physical_freqs, normalized_freqs = nidn.get_frequency_points(cfg)\n",
-    "print(\"Physical frequencies are:\",physical_freqs)"
+    "print(\"Physical frequencies are:\")\n",
+    "print(physical_freqs)"
    ]
   },
   {
@@ -61,7 +63,8 @@
    "outputs": [],
    "source": [
     "target_R_spectrum =  [1.0]*6+[0.0]*14\n",
-    "target_T_spectrum =  [0.0]*6+[0.0]*14"
+    "target_T_spectrum =  [0.0]*6+[0.0]*14\n",
+    "model = None # initialized just to pass"
    ]
   },
   {
@@ -73,7 +76,7 @@
    },
    "outputs": [],
    "source": [
-    "model, results = nidn.run_training(cfg,target_R_spectrum,target_T_spectrum)"
+    "model, results = nidn.run_training(cfg,target_R_spectrum,target_T_spectrum, model=model)"
    ]
   },
   {
@@ -109,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bae8066f",
+   "id": "c9188a5e",
    "metadata": {},
    "outputs": [],
    "source": []


Test patch:
diff --git a/nidn/tests/material_collection_test.py b/nidn/tests/material_collection_test.py
new file mode 100644
index 0000000..a1cba87
--- /dev/null
+++ b/nidn/tests/material_collection_test.py
@@ -0,0 +1,16 @@
+from ..materials.material_collection import MaterialCollection
+
+
+def test_material_collection_init():
+    """Tests if the material collection can be initialized successfully"""
+    target_frequencies = [1.0, 0.1, 0.01]
+    mc = MaterialCollection(target_frequencies)
+    assert len(mc.material_names) > 0
+    assert mc.target_frequencies == target_frequencies
+    assert mc.epsilon_matrix is not None
+    assert mc.epsilon_matrix.shape[0] == len(mc.material_names)
+    assert mc.epsilon_matrix.shape[1] == len(target_frequencies)
+
+
+if __name__ == "__main__":
+    test_material_collection_init()
diff --git a/nidn/tests/validate_default_cfg_test.py b/nidn/tests/validate_default_cfg_test.py
index 07f24dc..bfc4547 100644
--- a/nidn/tests/validate_default_cfg_test.py
+++ b/nidn/tests/validate_default_cfg_test.py
@@ -6,3 +6,7 @@ def test_default_config():
     """Tests whether the default config is valid"""
     cfg = load_default_cfg()
     _validate_config(cfg)
+
+
+if __name__ == "__main__":
+    test_default_config()