ArticleProceedings of the National Academy of Sciences of the United States of America2016
Data-driven parameterization of the generalized Langevin equation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Learning the diffusion of nanoparticles in liquid phase TEM via physics-informed generative AI.Nature communications · 2025Article
- Ab initio generalized Langevin equation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties.JACS Au · 2024Article
- Accurate estimates of dynamical statistics using memory.The Journal of chemical physics · 2024Article
- Comparison of Friction Parametrization from Dynamics and Material Properties for a Coarse-Grained Polymer Melt.The journal of physical chemistry. B · 2023Article
- Pair-Reaction Dynamics in Water: Competition of Memory, Potential Shape, and Inertial Effects.The journal of physical chemistry. B · 2022Review
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- Likelihood-based non-Markovian models from molecular dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Optimal renormalization of multiscale systems.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Introducing Memory in Coarse-Grained Molecular Simulations.The journal of physical chemistry. B · 2021Article
- Dynamically consistent coarse-grain simulation model of chemically specific polymer melts via friction parameterization.The Journal of chemical physics · 2021Article
- Data-Driven Model Reduction for Stochastic Burgers Equations.Entropy (Basel, Switzerland) · 2020Article
- Negative friction memory induces persistent motion.The European physical journal. E, Soft matter · 2020Article
- Data-driven molecular modeling with the generalized Langevin equation.Journal of computational physics · 2020Article
- Unraveling how the Gly526Ser mutation arrests prostaglandin formation from arachidonic acid catalyzed by cyclooxygenase-2: a combined molecular dynamics and QM/MM study.RSC advances · 2020Article
- Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
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3 authors.
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Abstract
We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.
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