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ChemFit, a Python framework, streamlines parameter optimization in computational chemistry by enabling concurrent evaluation of simulation-based objective functions. It offers abstractions for heterogeneous objective terms and controls concurrency across objective components and parameter guesses. The framework's versatility is demonstrated through Lennard-Jones parameter determination for liquid Argon and polarizable force-field parameterization for H2O, showcasing scalable and reproducible parameter fitting.
Stop wrestling with cumbersome simulation engine interfaces: ChemFit offers a Pythonic, massively concurrent framework for parameter optimization in computational chemistry.
Parameter optimization in computational chemistry and physics often involves objective functions that are expensive to evaluate, noisy, non-differentiable, or composed of heterogeneous contributions originating from separate simulations. Gradient-free and black box optimization algorithms are powerful tools which are particularly well-suited to solving such optimization problems. However, interfacing simulation engines and parameter optimization libraries can be cumbersome, especially if simulations are expensive and need to be run concurrently. Here, we introduce ChemFit, a flexible Python framework for the definition, composition, and massively concurrent evaluation of simulation-based objective functions, which is designed to operate in conjunction with these algorithms. This framework provides abstractions for heterogeneous objective terms, file-based and in-memory quantity evaluation, and explicit control over concurrency across both objective components and parameter guesses. We demonstrate the versatility of ChemFit for different applications such as: (i) determination of Lennard-Jones parameters for liquid Argon from experimental density data over a range in temperature and pressure, using molecular-dynamics simulations, and (ii) the parameterization of a polarizable force-field for H2O against the structure of small ice clusters obtained from density functional theory calculations. These examples illustrate how ChemFit enables scalable, reproducible, and optimizer-agnostic parameter fitting.
