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Universal generative physics simulation platform integrating rigid body, FEM, MPM, particle (PBD/SPH), and cloth solvers with explicit multi-solver coupling in a single differentiable scene, GPU-graph-accelerated massively parallel simulation, a photorealistic renderer (Nyx), and a cross-platform compiler (CUDA/ROCm/Metal/Vulkan/CPU); scales from laptops to datacenter GPUs for physics simulation, synthetic data generation, and physical-AI/robotics research (30K+ stars, Apache 2.0, actively maintained, now officially supported by Genesis AI)

Computational fluid dynamics in JAX, enabling differentiable Navier-Stokes simulations with automatic differentiation for ML-accelerated CFD research, supporting turbulence modeling, convection-diffusion, and complex boundary conditions on CPUs and GPUs (Google Research, 947+ stars)

High-performance, GPU-native molecular dynamics package supporting empirical potentials and neuroevolution potentials (NEPs) with a built-in `nep` training executable, achieving DFT-level accuracy at orders-of-magnitude lower training cost than competing MLIPs; widely adopted for heat transport (NEP/HNEP), lattice dynamics, mechanical properties, shock compression, and path-integral MD, with CUDA and ROCm support and extensive tutorials (849+ stars, GPL-3.0, Comput. Phys. Commun. 2017 / Phys. Rev. B 2021, actively maintained)

GPU-accelerated differentiable physics simulation engine built on NVIDIA Warp, supporting rigid/soft body, cloth, and gradient-based optimization for scientific ML, initiated by Disney Research, DeepMind, and NVIDIA (Linux Foundation, Apache 2.0, 2025)

Cross-platform library for differentiable programming of quantum computers with automatic differentiation, enabling hybrid quantum-classical machine learning for quantum chemistry, quantum physics, and NISQ algorithm research (Xanadu, 3k+ stars)

Molecular dynamics analysis

Probabilistic programming

Graph neural network library for PyTorch enabling molecular modeling, materials discovery, protein interaction networks, and scientific knowledge graph learning (23.7k+ stars)

High-performance molecular simulation toolkit

Python framework for writing high-performance GPU simulation and graphics kernels with first-class automatic differentiation, enabling differentiable physics, molecular dynamics, soft-body and cloth simulation, robotics, and CFD adjoints compiled to CUDA; serves as the underlying engine for differentiable simulation projects like Newton and integrates with PyTorch, JAX, and OpenUSD (NVIDIA, 7.1K+ stars, Apache 2.0)

Minimalist, batteries-included repository for training video world models with diffusion-forcing, supporting long-horizon rollouts, 3D point-cloud generation, and model-predictive control with pretrained checkpoints (Simchowitz Lab, 700+ stars, MIT License, 2026)

PyTorch-native atomistic simulation engine for the machine-learned interatomic potential (MLIP) era, enabling batched molecular dynamics and structural relaxation with automatic GPU memory management; supports MACE, Fairchem, SevenNet, ORB, MatterSim and other popular MLIPs with up to 100x speedup over ASE (Radical AI, AI for Science 2026, 468+ stars, MIT License)

Open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives, enabling quantum algorithm development for quantum chemistry, materials science, and optimization research (IBM, 7.4K+ stars, Apache 2.0)

Deep learning package for many-body potential energy representation and molecular dynamics, achieving quantum-mechanical accuracy with classical MD efficiency (DeepModeling, Gordon Bell Prize 2020, 1.9k+ stars)

End-to-end molecular dynamics engine built on PyTorch, enabling differentiable simulations with neural network potentials and GPU acceleration for machine learning-accelerated molecular dynamics (MIT License, 707+ stars)

Pretrained machine-learned force field for (bio)molecular simulations combining the fast SO3krates neural network for semi-local interactions with universal pairwise force fields for short-range repulsion, long-range electrostatics, and dispersion interactions; supports geometry optimization, NVT/NPT/NVE MD, fine-tuning, ASE calculator, and JAX-MD integration (JACS 2025, 218+ stars, MIT License)

Euclidean neural networks for arbitrary point transformations enabling E(3)-equivariant deep learning, foundational library for building geometry-aware neural networks in molecular dynamics, materials science, and physics

Deep Graph Library for scalable deep learning on graphs, powering molecular modeling, materials discovery, protein interaction networks, and scientific knowledge graph learning across PyTorch, TensorFlow, and MXNet backends (14K+ stars)

Microsoft's AI-powered ab initio biomolecular dynamics simulation achieving quantum-mechanical accuracy for proteins with 10,000+ atoms, orders of magnitude faster than DFT using protein fragmentation and ML force fields (Nature 2024)