Trending repositories for topic materials-science
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
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...
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
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
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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...
[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
A deep learning package for many-body potential energy representation and molecular dynamics
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
Curated list of known efforts in materials informatics, i.e. in modern materials science
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
[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.
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
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...
Curated list of known efforts in materials informatics, i.e. in modern materials science
A deep learning package for many-body potential energy representation and molecular dynamics
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
NequIP is a code for building E(3)-equivariant interatomic potentials
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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...
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Matbench: Benchmarks for materials science property prediction
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
[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
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning
Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials
Matbench: Benchmarks for materials science property prediction
SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT
DMFTwDFT: An open-source code combining Dynamical Mean Field Theory with various Density Functional Theory packages
A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning
3D model exchange format with physical material properties for virtual development, test and validation of automated driving.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
Reaction Network is a Python package for predicting likely inorganic chemical reaction pathways using graph theoretical methods. Project led by @mattmcdermott (formerly at Berkeley Lab).
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Codes for text-mined solid-state reactions dataset
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
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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.
Curated list of known efforts in materials informatics, i.e. in modern materials science
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)
atomate2 is a library of computational materials science workflows
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.
Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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.
Matbench: Benchmarks for materials science property prediction