Radical AI has announced the release of TorchSim, a next-generation atomistic simulation engine built natively in PyTorch and designed for the MLIP (machine-learned interatomic potentials) era. Positioned as a major technical shift in materials science, TorchSim promises to accelerate molecular simulations by orders of magnitude compared to traditional frameworks such as ASE (Atomic Simulation Environment) and DFT (Density Functional Theory).
TorchSim is open-source and designed to support modern materials workflows that leverage machine learning models like MACE, Fairchem, and SevenNet. It includes support for classical interaction potentials such as Lennard-Jones and Morse, as well as integration schemes like NVE, and NVT Langevin. The engine features automatic…