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Software

Software

Examples of experimental orchestration, optimization, information management, and other software.

Workflow Orchestration

Experimental orchestration software for autonomously controlling laboratory experiments.

Experimental Science

General Purpose

Communication Protocols/Server Frameworks

See also @sgbaird's lab-automation list and Awesome Workflow Repositories.

Optimization

Open-source and proprietary optimization software for iteratively suggesting next experiments (i.e., adaptive experimentation).

Open-source

  • Adaptive Experimentation Platform (Ax) is a user-friendly, modular, and actively developed general-purpose Bayesian optimization platform with support for simple and advanced optimization tasks such as noisy, multi-objective, multi-task, multi-fidelity, batch, high-dimensional, linearly constrained, nonlinearly constrained, mixed continuous/discrete/categorical, and contextual Bayesian optimization.

  • BoTorch is the backbone that makes up the Ax platform and allows for greater customization and specialized algorithms such as risk-averse Bayesian optimization and constraint active search.

  • Dragonfly is an open source python library for scalable Bayesian optimization with multi-objective and multi-fidelity support.

  • RayTune offers experiment execution and hyperparameter tuning at any scale with many supported search algorithms and trial schedulers under a common interface.

  • Aspuru-Guzik Group

  • Atlas is a Python package that offers Bayesian optimization tailored towards real-world experimental science problems: mixed parameters, multi-objective, noisy, constrained, multi-fidelity, and meta-learning optimization along with search space expansion/contraction. [WIP]

  • Chimera is a hierarchy-based multi-objective optimization scalarizing function.

  • Gryffin enables Bayesian optimization of continuous and categorical variables with support for physicochemical descriptors and batch optimization.

  • Gemini is a scalable multi-fidelity Bayesian optimization technique and is supported by Gryffin.

  • Golem is an algorithm that helps identify optimal solutions that are robust to input uncertainty (i.e., robust optimization).

  • Phoenics is a linear-scaling Bayesian optimization algorithm with support for batch and periodic parameter optimization.

  • Anubis is a Bayesian optimization algorithm that models unknown feasibility constraints and incorporates it into the acquisition function

  • BoFire is a Bayesian Optimization Framework Intended for Real Experiments (under development) with support for advanced optimization tasks such as mixed variables, multiple objectives, and generic constraints.

  • NIMS-OS is a Python package (+GUI) for

workflow orchestration and multi-objective optimization software that supports BLOX, PDC,

random exploration, and a multi-objective variant of PHYSBO.

  • SLAMD - A web app leveraging data-driven design for cementitious materials via a digital lab twin, complemented by a (materials-agnostic) AI optimization feature. Features UI to interactively explore design spaces. The web app uses Python and Javascript.

  • Summit is a set of tools for optimizing chemical processes with a wide variety of design of experiments (DoE) and adaptive design methods along with benchmarks.

  • GPax is a Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX that take advantage of prior physical knowledge and different data modalities for active learning and Bayesian optimization. It supports "deep kernel learning", structured probabilistic mean functions, hypothesis learning workflows, multitask, multifidelity, heteroscedastic, and vector BO and emphasizes user friendliness.

  • Bayesian Back End (BayBE) is a open-source toolbox by Merck KGaA for Bayesian optimization, featuring custom encodings, chemical knowledge integration, hybrid spaces, transfer learning, simulation tools, and robust, serializable code for real-world experimental campaigns.

Proprietary

Research Data Management

i.e., Electronic Lab Notebooks (ELNs) and Laboratory Information Management Systems (LIMS). You may also be interested in this list of solutions by Labii, of course keeping in mind that the list is compiled by a specific ELN company. See also this ELN Finder tool.

Open-source

Proprietary

Other

  • Benchmarking

  • Olympus is a benchmarking framework based primarily on data collected from experimental self-driving lab setups.

  • Functional Data Analysis

  • Amplitude-Phase-Distance is a Riemannian differential geometry toolbox to compute a `shape' distance between electromagnetic spectra, scattering, or diffraction profiles.

  • autophasemap is a clustering and phase mapping toolbox using the amplitude phase distance for autonomous construction of phasemaps from spectra-like data.

  • Instrument-specific drivers

  • Chemspyd [code] [paper] is a Python API for the Chemspeed AutoSuite software.