[upstream-sync] README.md from ai-boost/awesome-ai-for-science@c7a67cd2 [catalogue]

2026-08-23 04:44:30 +00:00
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 title: "Readme"
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 #### Materials Discovery
 - [GNoME](https://github.com/google-deepmind/materials_discovery) - DeepMind's graph neural network for materials exploration, discovering 2.2M new crystal structures (380K most stable) equivalent to 800 years of traditional research, with 520K+ materials dataset open-sourced (Nature 2023)
 - [FAIRChem (OMat24)](https://github.com/FAIR-Chem/fairchem) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
+- [Skala 1.1 (Microsoft Research, 2026)](https://github.com/microsoft/skala) - Neural network-based exchange-correlation functional for density functional theory (DFT) that surpasses state-of-the-art hybrid functionals in accuracy for main-group thermochemistry, kinetics, and non-covalent interactions at semi-local DFT cost; includes PySCF/GPU4PySCF/ASE bindings and C++/Fortran integrations (248+ stars, MIT License)
 - [All-atom Diffusion Transformers (ADiT)](https://github.com/facebookresearch/all-atom-diffusion-transformer) - Unified latent diffusion transformer that jointly generates periodic crystals and non-periodic molecules, scaling to 500M parameters with SOTA results on QM9, MP20, and GEOM-DRUGS (Meta FAIR, ICML 2025, 310+ stars)
 - [JARVIS](https://github.com/usnistgov/jarvis) - NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)
 - [NVIDIA ALCHEMI Toolkit](https://github.com/NVIDIA/nvalchemi-toolkit) - Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)
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 - [Neural ODEs](https://github.com/rtqichen/torchdiffeq) - Differential equations with neural networks
 - [Physics-Informed Neural Networks](https://github.com/maziarraissi/PINNs) - Physics-constrained ML
 - [EquiformerV2](https://github.com/atomicarchitects/equiformer_v2) - Improved equivariant Transformer for 3D atomic graphs (ICLR2024)
+- [EquiformerV3](https://github.com/atomicarchitects/equiformer_v3) - Scaling efficient, expressive, and general SE(3)-equivariant graph attention transformers for atomic systems and machine-learned interatomic potentials (MIT License, 2026)
 - [Equiformer](https://github.com/atomicarchitects/equiformer) - Equivariant graph attention Transformer (ICLR2023)
 - [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
 - [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)