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• W3010834585 abstract "A large fraction of computational science involves simulating the dynamics ofparticles that interact via pairwise or many-body interactions. Thesesimulations, called Molecular Dynamics (MD), span a vast range of subjects fromphysics and materials science to biochemistry and drug discovery. Most MDsoftware involves significant use of handwritten derivatives and code reuseacross C++, FORTRAN, and CUDA. This is reminiscent of the state of machinelearning before automatic differentiation became popular. In this work we bringthe substantial advances in software that have taken place in machine learningto MD with JAX, M.D. (JAX MD). JAX MD is an end-to-end differentiable MDpackage written entirely in Python that can be just-in-time compiled to CPU,GPU, or TPU. JAX MD allows researchers to iterate extremely quickly and letsresearchers easily incorporate machine learning models into their workflows.Finally, since all of the simulation code is written in Python, researchers canhave unprecedented flexibility in setting up experiments without having to editany low-level C++ or CUDA code. In addition to making existing workloadseasier, JAX MD allows researchers to take derivatives through whole-simulationsas well as seamlessly incorporate neural networks into simulations. This paperexplores the architecture of JAX MD and its capabilities through severalvignettes. Code is available at www.github.com/google/jax-md. We also providean interactive Colab notebook that goes through all of the experimentsdiscussed in the paper." @default.
• W3010834585 created "2020-03-23" @default.
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• W3010834585 date "2020-03-04" @default.
• W3010834585 modified "2023-09-26" @default.
• W3010834585 title "JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python" @default.
• W3010834585 hasPublicationYear "2020" @default.
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