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IDAES is a Python process systems engineering toolkit for simulation-based design, analysis and optimization of advanced energy systems, with solver extensions and separately distributed examples.

Process Optimization · Chemical Manufacturing

Phoenics combines Bayesian optimization with Bayesian kernel density estimation to recommend experimental or computational parameters, supporting sequential, batch and multi-objective workflows.

Lab Automation · Process Optimization

Gryffin is a Python Bayesian optimization framework for categorical and mixed experimental design spaces, with batch recommendations and physicochemical descriptor support.

Lab Automation · Process Optimization

Olympus is a Python framework for benchmarking noisy optimization and experiment planning, with mixed parameter domains, experimental benchmarks, analytical test functions and a shared planner interface.

Lab Automation · Process Optimization

BoFire is a Python framework for experimental design and Bayesian optimization, supporting mixed variables, constraints, molecular representations and iterative candidate selection.

Lab Automation · Process Optimization

Summit is a Python toolkit for chemical reaction optimisation, combining optimisation strategies, simulated reaction benchmarks and closed-loop workflows for evaluating experimental conditions.

Lab Automation · Process Optimization

BayBE is a Python toolbox for Bayesian experimental design that recommends parameter configurations from defined search spaces, objectives and measurements, including chemistry-aware and multi-target workflows.

Lab Automation · Process Optimization

DeePKS-kit is a Python toolkit for training quantum-chemistry energy functionals, testing post-HF models, and running self-consistent calculations through the DeePHF and DeePKS schemes.

Quantum Chemistry

DeePTB is a Python package for deep-learning electronic-structure models, combining environment-dependent tight binding with equivariant Hamiltonian, density-matrix and overlap-matrix representations.

Quantum Chemistry · Materials Discovery

DP-GEN is a Python concurrent-learning platform that coordinates molecular simulation, first-principles calculations and DeePMD-kit workflows to generate interatomic potential models.

Computational Chemistry · Materials Discovery

QML is an archived Python toolkit for quantum machine learning with Fortran-backed representation, kernel and solver modules. Further development has moved to qmllib.

Molecular Property Prediction · Quantum Chemistry

DScribe is a Python library that converts atomic structures into numerical descriptors for machine learning, visualization and similarity analysis, with batch processing and atomic-position derivatives.

Molecular Property Prediction · Materials Discovery

CGCNN implements crystal graph convolutional neural networks for learning material properties from crystal structures, with custom-data training and prediction using pre-trained models.

Materials Discovery

CrabNet implements an attention-based model for predicting material properties from composition alone, with companion guidance on basic use and model interpretability.

Materials Discovery

ALIGNN provides atomistic graph neural networks for materials property prediction, with training workflows, pretrained predictors and ALIGNN-FF force fields for structural optimization.

Materials Discovery

M3GNet is an archived materials graph neural network implementation with three-body interactions, a pretrained interatomic potential, and workflows for crystal relaxation, molecular dynamics and model training.

Computational Chemistry · Materials Discovery

CHGNet is a pretrained, charge-informed neural network potential for crystal structures, predicting energies, forces, stresses and magnetic moments for relaxation and molecular dynamics workflows.

Computational Chemistry · Materials Discovery

MEGNet is a deprecated TensorFlow graph-network implementation with pretrained molecular and crystal property models, training utilities, and transferable elemental embeddings.

Molecular Property Prediction · Materials Discovery

MODNet is a Python framework for supervised materials-property prediction from composition or crystal structure, with joint learning for multiple targets and two documented pretrained models.

Materials Discovery

matminer is a Python library for materials-science data mining, bringing together community datasets, data retrieval methods and featurizers with citation support for research workflows.

Materials Discovery