Package: self-driving-lab-demo
If you're reading this on GitHub, navigate tothe documentationfor tutorials, APIs, and moreself-driving-lab-demoSoftware and instructions for setting up and running an autonomous (self-driving) laboratory optics demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.DemosThis repository covers three teaching and prototyping demos for self-driving laboratories in the fields of optics (light-mixing), chemistry (liquid-mixing), and solid-state materials science (solid-mixing).CLSLab:LightWhite paper[postprint]Build instructions manuscriptYouTube build instructionsPurchase*Self-driving labs are the future; however, the capital and expertise required can be daunting. We introduce the idea of an experimental optimization task for less than $100, a square foot of desk space, and an hour of total setup time from the shopping cart to the first "autonomous drive." For our first demo, we use optics rather than chemistry; after all, light is easier to move than matter. While not strictly materials-based, importantly, several core principles of a self-driving materials discovery lab are retained in this cross-domain example:sending commands to hardware to adjust physical parametersreceiving measured objective propertiesdecision-making via active learningutilizing cloud-based simulationsThe demo is accessible, extensible, modular, and repeatable, making it an ideal candidate for both low-cost experimental adaptive design prototyping and learning the principles of self-driving laboratories in a low-risk setting.SummaryUnassembledAssembledUsersIan FosterTonio BuonassisiBen BlaiszikUniversity instructors utilizing CLSLab-Light during Spring 2023: 4 (~40 kits in total)At-cost CommercializationGroupGets round 1: funded and fulfilled (19 kits)GroupGets round 2: funded and fulfilled (20 kits)GroupGets round 3: In-progress (as of 2023-09-08)*CLSLab:Light isstocked in the GroupGets Store. It has a higher GroupGets fee (only GroupGets sees the extra profit). If you don't want to wait for new rounds and you'd rather order a pre-built kit, this is the best option right now.CLSLab:LiquidBill of materialsWe extend the light-mixing demo to a color-matching materials optimization problem using dilute colored dyes. This optimization task costs less than 300 USD, requires less than three square feet of desk space, and less than three hours of total setup time from the shopping cart to the first “autonomous drive.” The demo is modular and extensible; additional peristaltic pump channels can be added, the dye reservoirs can be increased, and chemically sensitive parts can be replaced with chemically resistant ones.SummarySchematicAssembledCLSLab:SolidThere are few to no examples of a low-cost demo platform involving the handling of solid-state materials (i.e., powders, pellets). For this demo, we propose using red, yellow, and blue powdered wax as a replacement for the liquid colored dyes. The demo is more expensive due to the need for robotics. The demo involves using tealight candle holders, transferring them to a rotating stage via a robotic arm, dispensing a combination of powders, melting the wax via an incandescent light bulb, measuring a discrete color spectrum, and moving the completed sample to a separate sample storage area.See AlsoJournal of Brief Ideas submissionHackaday project pageAdafruit Forum: Developing a closed-loop feedback system via DotStar
LEDsBasic UsageI recommend going throughthe introductory Colab notebook, but here is a shorter version of how an optimization comparison can be run between grid search, random search, and Bayesian optimization using a free public demo.Basic Installationpipinstallself-driving-lab-demoClient Setup for Public Test Demofromself_driving_lab_demoimport(SelfDrivingLabDemoLight,# SelfDrivingLabDemoLiquid,mqtt_observe_sensor_data,get_paho_client,)PICO_ID="test"sensor_topic=f"sdl-demo/picow/{PICO_ID}/as7341/"# to match with Pico W code# instantiate client once and reuse to avoid opening too many connectionsclient=get_paho_client(sensor_topic)sdl=SelfDrivingLabDemoLight(autoload=True,# perform target data experiment automatically, default is Falseobserve_sensor_data_fn=mqtt_observe_sensor_data,# defaultobserve_sensor_data_kwargs=dict(pico_id=PICO_ID,client=client),simulation=False,# default)Optimization Comparisonfromself_driving_lab_demo.utils.searchimport(grid_search,random_search,ax_bayesian_optimization,)num_iter=27grid,grid_data=grid_search(sdl,num_iter)random_inputs,random_data=random_search(sdl,num_iter)best_parameters,values,experiment,model=ax_bayesian_optimization(sdl,num_iter)Visualizationimportplotly.expressaspximportpandasaspd# gridgrid_input_df=pd.DataFrame(grid)grid_output_df=pd.DataFrame(grid_data)[["frechet"]]grid_df=pd.concat([grid_input_df,grid_output_df],axis=1)grid_df["best_so_far"]=grid_df["frechet"].cummin()# randomrandom_input_df=pd.DataFrame(random_inputs,columns=["R","G","B"])random_output_df=pd.DataFrame(random_data)[["frechet"]]random_df=pd.concat([random_input_df,random_output_df],axis=1)random_df["best_so_far"]=random_df["frechet"].cummin()# bayestrials=list(experiment.trials.values())bayes_input_df=pd.DataFrame([t.arm.parametersfortintrials])bayes_output_df=pd.Series([t.objective_meanfortintrials],name="frechet").to_frame()bayes_df=pd.concat([bayes_input_df,bayes_output_df],axis=1)bayes_df["best_so_far"]=bayes_df["frechet"].cummin()# concatenationgrid_df["type"]="grid"random_df["type"]="random"bayes_df["type"]="bayesian"df=pd.concat([grid_df,random_df,bayes_df],axis=0)# plottingpx.line(df,x=df.index,y="best_so_far",color="type").update_layout(xaxis_title="iteration",yaxis_title="Best error so far",)Example OutputAdvanced InstallationPyPIcondacreate-nself-driving-lab-demopython=3.10.*condaactivateself-driving-lab-demopipinstallself-driving-lab-demoLocalIn order to set up the necessary environment:review and uncomment what you need inenvironment.ymland create an environmentself-driving-lab-demowith the help ofconda:conda env create -f environment.ymlactivate the new environment with:conda activate self-driving-lab-demoNOTE:The conda environment will have self-driving-lab-demo installed in editable mode.
Some changes, e.g. insetup.cfg, might require you to runpip install -e .again.Optional and needed only once aftergit clone:install severalpre-commitgit hooks with:pre-commitinstall# You might also want to run `pre-commit autoupdate`and checkout the configuration under.pre-commit-config.yaml.
The-n, --no-verifyflag ofgit commitcan be used to deactivate pre-commit hooks temporarily.installnbstripoutgit hooks to remove the output cells of committed notebooks with:nbstripout--install--attributesnotebooks/.gitattributesThis is useful to avoid large diffs due to plots in your notebooks.
A simplenbstripout --uninstallwill revert these changes.Then take a look into thescriptsandnotebooksfolders.Dependency Management & ReproducibilityAlways keep your abstract (unpinned) dependencies updated inenvironment.ymland eventually
insetup.cfgif you want to ship and install your package viapiplater on.Create concrete dependencies asenvironment.lock.ymlfor the exact reproduction of your
environment with:condaenvexport-nself-driving-lab-demo-fenvironment.lock.ymlFor multi-OS development, consider using--no-buildsduring the export.Update your current environment with respect to a newenvironment.lock.ymlusing:condaenvupdate-fenvironment.lock.yml--pruneProject Organization├── AUTHORS.md              <- List of developers and maintainers.
├── CHANGELOG.md            <- Changelog to keep track of new features and fixes.
├── CONTRIBUTING.md         <- Guidelines for contributing to this project.
├── Dockerfile              <- Build a docker container with `docker build .`.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yml         <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── pyproject.toml          <- Build configuration. Don't change! Use `pip install -e .`
│                              to install for development or to build `tox -e build`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- [DEPRECATED] Use `python setup.py develop` to install for
│                              development or `python setup.py bdist_wheel` to build.
├── src
│   └── self_driving_lab_demo <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `pytest`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.NoteThis project has been set up usingPyScaffold4.2.3.post1.dev10+g7a0f254 and thedsproject extension0.7.2.post1.dev3+g948a662.