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Due to the time-scale limitations of all-atom simulation of proteins, there has been substantial interest in coarse-grained approaches. Some methods, like "Resolution Exchange," [E. Lyman et al., Phys. Rev. Lett. 96, 028105 (2006)] can…

Biological Physics · Physics 2007-05-23 F. Marty Ytreberg , Svetlana Kh. Aroutiounian , Daniel M. Zuckerman

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can…

Machine Learning · Computer Science 2023-10-11 Jingbang Chen , Yian Wang , Xingwei Qu , Shuangjia Zheng , Yaodong Yang , Hao Dong , Jie Fu

Optimization problems with the objective function in the form of weighted sum and linear equality constraints are considered. Given that the number of local cost functions can be large as well as the number of constraints, a stochastic…

Optimization and Control · Mathematics 2026-05-26 Nataša Krejić , Nataša Krklec Jerinkić , Sanja Rapajić , Luka Rutešić

Equilibrating proteins and other biomacromolecules is cardinal for molecular dynamics simulation of such biological systems in which they perform free dynamics without any externally-applied mechanical constraint, until thermodynamic…

Soft Condensed Matter · Physics 2021-02-11 Ashkan Shekaari , Mahmoud Jafari

Interatomic-force measurements are regularly performed using frequency-modulation atomic force microscopy. This requires conversion of the observed shift in the resonant frequency of a force-sensing cantilever, to the actual force…

Applied Physics · Physics 2020-12-02 John Elie Sader

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of…

Stochastic policies (also known as relaxed controls) are widely used in continuous-time reinforcement learning algorithms. However, executing a stochastic policy and evaluating its performance in a continuous-time environment remain open…

Machine Learning · Computer Science 2025-10-03 Yanwei Jia , Du Ouyang , Yufei Zhang

A variant of the parallel tempering method is proposed in terms of a stochastic switching process for the coupled dynamics of replica configuration and temperature permutation. This formulation is shown to facilitate the analysis of the…

Chemical Physics · Physics 2017-12-20 Jianfeng Lu , Eric Vanden-Eijnden

In this work, the development and implementation of the effective stochastic potential (ESP) method is presented to perform efficient conformational sampling of molecules. The overarching goal of this work is to alleviate the computational…

Chemical Physics · Physics 2018-08-01 Jeremy A. Scher , Michael G. Bayne , Amogh Srihari , Shikha Nangia , Arindam Chakraborty

A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed miniature force-sensing forceps that can…

Robotics · Computer Science 2024-11-05 Tangyou Liu , Xiaoyi Wang , Jay Katupitiya , Jiaole Wang , Liao Wu

We propose experimentally feasible ways to probe universal features of absorbing phase transitions from two different approaches, both based on numerical validations. On one hand, we numerically study a probability distribution of…

Statistical Mechanics · Physics 2018-12-20 Keiichi Tamai , Masaki Sano

One of the most intriguing results of single molecule experiments on proteins and nucleic acids is the discovery of functional heterogeneity: the observation that complex cellular machines exhibit multiple, biologically active…

Biomolecules · Quantitative Biology 2022-10-12 Michael Hinczewski , Changbong Hyeon , D. Thirumalai

We propose a training-free conditional sampling method for flow matching models based on importance sampling. Because a na\"ive application of importance sampling suffers from weight degeneracy in high-dimensional settings, we modify and…

Machine Learning · Statistics 2026-02-16 Qianqian Qu , Jun S. Liu

Transition path sampling (TPS) is a powerful technique for investigating rare transitions, especially when the mechanism is unknown and one does not have access to the reaction coordinate. Straightforward application of TPS does not…

Chemical Physics · Physics 2019-07-11 Z. Faidon Brotzakis , Peter G. Bolhuis

In spite of decades of research, much remains to be discovered about folding: the detailed structure of the initial (unfolded) state, vestigial folding instructions remaining only in the unfolded state, the interaction of the molecule with…

Biological Physics · Physics 2018-11-26 Walter A. Simmons

Stochastic resetting, the procedure of stopping and re-initializing random processes, has recently emerged as a powerful tool for accelerating processes ranging from queuing systems to molecular simulations. However, its usefulness is…

Statistical Mechanics · Physics 2025-03-18 Tommer D. Keidar , Ofir Blumer , Barak Hirshberg , Shlomi Reuveni

We propose a simple, scalable algorithm for using stochastic interpolants to sample from unnormalized densities and for fine-tuning generative models. The approach, Tilt Matching, arises from a dynamical equation relating the flow matching…

Machine Learning · Statistics 2025-12-29 Peter Potaptchik , Cheuk-Kit Lee , Michael S. Albergo

Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy. Lots of techniques for model compression, such as pruning, quantization, and low-rank approximation, have been suggested…

Machine Learning · Computer Science 2018-10-31 Dongsoo Lee , Parichay Kapoor , Byeongwook Kim

The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by…

All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are highly heterogeneous and contain…

Chemical Physics · Physics 2025-05-06 Jaya Krishna Koneru , Korey M. Reid , Paul Robustelli