Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Chiavazzo Eliodoro; Covino Roberto; Coifman Ronald R.Gear C. WilliamGeorgiou Anastasia S.Hummer GerhardKevrekidis Ioannis G.

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Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

机译：Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

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We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

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《Proceedings of the National Academy of Sciences of the United States of America.》 |2017年第28期|E5494-E5503|共10页
作者
Chiavazzo Eliodoro; Covino Roberto; Coifman Ronald R.Gear C. WilliamGeorgiou Anastasia S.Hummer GerhardKevrekidis Ioannis G.;
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作者单位

Max Planck Inst Biophys, Dept Theoret Biophys, D-60438 Frankfurt, Germany;

Yale Univ, Dept Math, Program Appl Math, New Haven, CT 06510 USA;

Princeton Univ, Dept Chem & Biol Engn, Princeton, NJ 08544 USAPolitecn Torino, Energy Dept, I-10129 Turin, Italy;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
free-energy surface; model reduction; machine learning; protein folding; enhanced sampling methods;

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

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