Materials Discovery

Academic platform for managing and exploring materials research data, with notebook-based analysis through the NOMAD AI Toolkit.

Materials Discovery · Scientific Data

Academic materials data platform for exploring computed structures and properties and preparing materials-discovery research.

Materials Discovery · Scientific Data

MatGL is a materials graph learning library for developing and using property predictors and machine learning potentials.

Computational Chemistry · Materials Discovery

A backend-agnostic Phonopy Skill for planning finite-displacement calculations, assembling force data, and guiding band, DOS, and thermal analysis with explicit scientific assumptions.

Materials Discovery

An ASE routing Skill in the computational-chemistry-agent-skills collection that separates workflow preparation from calculator configuration and delegates execution elsewhere.

Computational Chemistry · Materials Discovery

A K-Dense collection Skill with instructions and a local helper for pycalphad TDB equilibrium calculations, exporting molar phase fractions, compositions, and numerical validation records.

Materials Discovery

Pymatgen Skill (K-Dense) provides agent instructions and Python helpers for validating materials structures, examining symmetry, planning conversions, building local phase diagrams, and bounding Materials Project queries.

Materials Discovery

ChemAgent (AI4Chem) is a research framework for chemistry and materials tool use, linked to the CheMatAgent paper on tree-search planning, tool execution and ChemToolBench-based training.

Computational Chemistry · Materials Discovery

SciAgents is a research multi-agent framework that uses scientific knowledge graphs, LLMs and retrieval tools to develop and critique hypotheses for bio-inspired materials research.

Literature & Research · Materials Discovery

ChatMOF is a code-available research system that uses language-model-guided tools to retrieve MOF data, predict properties and generate structures from natural-language requests.

Materials Discovery

ChemGraph is a Python agent framework that connects natural-language chemistry requests to molecular construction, simulations, analysis, and reporting, with CLI, Python, Streamlit, and MCP interfaces.

Computational Chemistry · Materials Discovery

A third-party MCP server that connects assistant clients to Materials Project through mp_api, with tools for material searches, crystal structures, electronic properties, and other materials data.

Materials Discovery · Scientific Data

ChemML is a Python suite for chemical and materials data analysis, mining, and modeling, with a modular design and documented work on graph neural networks, AutoML, and explainability.

Molecular Property Prediction · Materials Discovery

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

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