ArticleCommunications chemistry2020
Data-driven analysis of the number of Lennard-Jones types needed in a force field.
Article in Communications chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Using physical property surrogate models to perform accelerated multi-fidelity optimization of force field parameters.Digital discovery · 2023Article
- Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.Journal of chemical theory and computation · 2022Article
- Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.Journal of chemical information and modeling · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Force fields used in molecular simulations contain numerical parameters, such as Lennard-Jones (LJ) parameters, which are assigned to the atoms in a molecule based on a classification of their chemical environments. The number of classes, or types, should be no more than needed to maximize agreement with experiment, as parsimony avoids overfitting and simplifies parameter optimization. However, types have historically been crafted based largely on chemical intuition, so current force fields may contain more types than needed. In this study, we seek the minimum number of LJ parameter types needed to represent key properties of organic liquids. We find that highly competitive force field accuracy is obtained with minimalist sets of LJ types; e.g. two H types and one type apiece for C, O, and N atoms. We also find that the fitness surface has multiple minima, which can lead to local trapping of the optimizer.
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Registered trials
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