Statistics for topic materials-science

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT

Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.

Deep and Machine Learning for Microscopy

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT

Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.

Deep and Machine Learning for Microscopy

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

NequIP is a code for building E(3)-equivariant interatomic potentials

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Pro...

A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

DScribe is a python package for creating machine learning descriptors for atomistic systems.

Python package to analyse the structural dynamics of perovskites

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT

A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

Python package to aid materials design and informatics

Matbench: Benchmarks for materials science property prediction

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

A deep learning package for many-body potential energy representation and molecular dynamics

NequIP is a code for building E(3)-equivariant interatomic potentials

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Pro...

Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT

MACE-MP models

datalab is a place to store experimental data and the connections between them.

Düsseldorf Advanced Material Simulation Kit (Read-only mirror)

Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"

MACE-MP models

Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Pro...

A deep learning package for many-body potential energy representation and molecular dynamics

NequIP is a code for building E(3)-equivariant interatomic potentials

Graph deep learning library for materials

Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."

MACE-MP models

Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials

SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT

Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)