2017/12/07 by Hythem Sidky, Jonathan K. Whitmer
Biochemistry, Genetics and Molecular Biology · Materials Science · Mathematics · Physics and Astronomy · #Adaptive sampling #Artificial intelligence #Artificial neural network #Bayesian optimization #Bayesian probability #Computer science #Energy (signal processing) #Free energy principle #Hyperparameter #Machine Learning in Materials Science #Machine learning #Mathematics #Model Reduction and Neural Networks #Overfitting #Protein Structure and Dynamics #cond-mat.stat-mech #physics.chem-ph #physics.comp-ph
paper · pdf · doi:10.1063/1.5018708
10 pages, 7 figures
arxiv created 2017/12/07 · openalex publication_date 2018/03/12 · arxiv updated 2018/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.