google-deepmind / flows_for_atomic_solidsLinks
☆53Updated 3 years ago
Alternatives and similar repositories for flows_for_atomic_solids
Users that are interested in flows_for_atomic_solids are comparing it to the libraries listed below
Sorting:
- DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electr…☆63Updated 7 months ago
- E3x is a JAX library for constructing efficient E(3)-equivariant deep learning architectures built on top of Flax.☆109Updated 8 months ago
- Reference implementation of "Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions" (ICLR, 2022) and "Sampling-f…☆31Updated last year
- Annotated implementations of equivariant (graph) neural networks in Jax: EGNN, SEGNN, NequIP.☆40Updated 9 months ago
- jax library for E3 Equivariant Neural Networks☆220Updated 3 months ago
- JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling☆81Updated 5 years ago
- Steerable E(3) GNN in jax☆24Updated 2 years ago
- Arbitrary-order derivatives of popular electronic structure methods, such as Hartree-Fock and coupled cluster theory.☆70Updated last year
- GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine l…☆108Updated last year
- PySCF on IPU☆44Updated last year
- Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)☆120Updated 3 years ago
- Supporting code for "Autoregressive neural-network wavefunctions for ab initio quantum chemistry".☆43Updated 3 years ago
- Exchange correlation functionals translated from libxc to jax☆46Updated 8 months ago
- SE(3) Equivariant Augmented Coupling Flows. NeurIPS 2023.☆24Updated last year
- Code repository for "Finding symmetry breaking order parameters with Euclidean Neural Networks"☆16Updated 4 years ago
- Tools for building equivariant polynomials on reductive Lie groups.☆35Updated 2 years ago
- An implementation of SchNet in JAX and JAX-MD.☆17Updated 3 years ago
- Pytorch differentiable molecular dynamics☆181Updated 3 years ago
- Code used by the "Clifford Group Equivariant Neural Networks" paper.☆88Updated last year
- Equivariant machine learning interatomic potentials in JAX.☆80Updated 2 weeks ago
- Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.☆97Updated 2 months ago
- Implementation of various equivariant models in JAX☆12Updated last year
- ☆12Updated last year
- ESI-DCAFM-TACO-VDSP Summer School on "Machine Learning for Materials Hard and Soft"☆39Updated 3 years ago
- Self-describing sparse tensor data format for atomistic machine learning and beyond☆90Updated this week
- Higher order equivariant graph neural networks for 3D point clouds☆45Updated 2 years ago
- Nomalizing flows for orbita-free DFT☆11Updated last year
- Differentiate all the things!☆162Updated 2 weeks ago
- PySCF with auto-differentiation☆89Updated last week
- A JAX library for Density Functional Theory.☆54Updated 2 weeks ago