OpenMM

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)

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

Probabilistic programming

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)

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)