Trending repositories for topic materials-science
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
[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.
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
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...
[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.
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
NequIP is a code for building E(3)-equivariant interatomic potentials
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...
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
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.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Curated list of known efforts in materials informatics, i.e. in modern materials science
RadonPy is a Python library to automate physical property calculations for polymer informatics.
SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT
Matbench: Benchmarks for materials science property prediction
Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
[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
RadonPy is a Python library to automate physical property calculations for polymer informatics.
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
Matbench: Benchmarks for materials science property prediction
DScribe is a python package for creating machine learning descriptors for atomistic systems.
NequIP is a code for building E(3)-equivariant interatomic potentials
Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Curated list of known efforts in materials informatics, i.e. in modern materials science
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A deep learning package for many-body potential energy representation and molecular dynamics
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...
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
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
[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
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Matbench: Benchmarks for materials science property prediction
atomate2 is a library of computational materials science workflows
Curated list of known efforts in materials informatics, i.e. in modern materials science
DScribe is a python package for creating machine learning descriptors for atomistic systems.
SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT
datalab is a place to store experimental data and the connections between them.
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
Matbench: Benchmarks for materials science property prediction
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
atomate2 is a library of computational materials science workflows
A high-performance framework for solving phonon and electron Boltzmann equations
Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials
NequIP is a code for building E(3)-equivariant interatomic potentials
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
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
Curated list of known efforts in materials informatics, i.e. in modern materials science
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
atomate2 is a library of computational materials science workflows
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
RadonPy is a Python library to automate physical property calculations for polymer informatics.
SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
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)
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
datalab is a place to store experimental data and the connections between them.
A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
atomate2 is a library of computational materials science workflows
polyGNN is a Python library to automate ML model training for polymer informatics.
RadonPy is a Python library to automate physical property calculations for polymer informatics.
A Python library of algorithms for the baseline correction of experimental data.
[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.
Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive de...