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Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction rate calculations -- remains largely unexplored. We…

An activated process consists of energy activation and barrier crossing; the former is a prerequisite for the latter. Barrier crossing has been studied extensively, but energy activation has been overlooked due to a lack of means to gauge…

Biological Physics · Physics 2020-07-31 Shanshan Wu , Ao Ma

Boolean network models of strongly connected modules are capable of capturing the high regulatory complexity of many biological gene regulatory circuits. We study numerically the previously introduced basin entropy, a parameter for the…

Disordered Systems and Neural Networks · Physics 2009-11-13 P. Krawitz , I. Shmulevich

In many complex systems, we observe that `interesting behaviour' is often the consequence of a system exploiting the existence of an Information Bottleneck (IB). These bottlenecks can occur at different scales, between individuals or…

Physics and Society · Physics 2023-08-02 Michael Crosscombe , Hiroki Sato

Information Bottleneck (IB) is a generalization of rate-distortion theory that naturally incorporates compression and relevance trade-offs for learning. Though the original IB has been extensively studied, there has not been much…

Machine Learning · Computer Science 2019-10-08 Thanh T. Nguyen , Jaesik Choi

Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it has proven challenging to infer the interaction rules directly from data. In the field of equation…

Quantitative Methods · Quantitative Biology 2023-11-27 Daniel A. Messenger , Graycen E. Wheeler , Xuedong Liu , David M. Bortz

Information bottleneck is an information-theoretic principle of representation learning that aims to learn a maximally compressed representation that preserves as much information about labels as possible. Under this principle, two…

Information Theory · Computer Science 2023-11-08 Yuyan Ni , Yanyan Lan , Ao Liu , Zhiming Ma

The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…

Materials Science · Physics 2025-05-27 Xiao Fu , Jing Xu , Qifan Yang , Xuhe Gong , Jingchen Lian , Liqi Wang , Zibin Wang , Ruijuan Xiao , Hong Li

Quite generally, constraint-based metabolic flux analysis describes the space of viable flux configurations for a metabolic network as a high-dimensional polytope defined by the linear constraints that enforce the balancing of production…

Molecular Networks · Quantitative Biology 2013-09-24 Francesco Alessandro Massucci , Francesc Font-Clos , Andrea De Martino , Isaac Pérez Castillo

Minimum energy path (MEP) search is a vital but often very time-consuming method to predict the transition states of versatile dynamic processes in chemistry, physics, and materials science. In this study, we reveal that the chemical bond…

Materials Science · Physics 2023-07-18 Hongsheng Cai , Guoyuan Liu , Peiqi Qiu , Guangfu Luo

The Replica Exchange Wang-Landau Method is used to estimate the energy landscape of a polymer composed of a simple hydrophobic and polar sequence using the HP protein model. Calculations of state transitions between the energy levels of the…

Soft Condensed Matter · Physics 2023-03-22 Jared McDonald , Michael R. von Spakovsky , William T. Reynolds

In principle, all of the dynamical complexities of many-body systems are encapsulated in the potential energy landscapes on which the atoms move - an observation that suggests that the essentials of the dynamics ought to be determined by…

Soft Condensed Matter · Physics 2007-12-12 Chengju Wang , Richard M. Stratt

In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation? In this paper, we…

Machine Learning · Computer Science 2020-01-08 Tailin Wu , Ian Fischer

Efficient simulation of quantum mechanical problems can be performed in a quantum computer where the interactions of qubits lead to the realization of various problems possessing quantum nature. Spin-Boson Model (SBM) is one of the striking…

The information bottleneck (IB) principle has been adopted to explain deep learning in terms of information compression and prediction, which are balanced by a trade-off hyperparameter. How to optimize the IB principle for better robustness…

Machine Learning · Computer Science 2021-03-04 Penglong Zhai , Shihua Zhang

A numerical model is built, simulating the principles of kinetic gas theory, to predict pressures of molecules in a spherical pressure vessel; the model tracks a single particle and multiplies the force on the spherical walls by a mole of…

Statistical Mechanics · Physics 2026-03-31 Matthew Marko

We show that active transport processes in biological systems can be understood through a local equilibrium description formulated at the mesoscale, the scale to describe stochastic processes. This new approach uses the method established…

Soft Condensed Matter · Physics 2007-05-23 S. Kjelstrup , J. M. Rubi , D. Bedeaux

We present computer simulations of a simple bead-spring model for polymer melts with intramolecular barriers. By systematically tuning the strength of the barriers, we investigate their role on the glass transition. Dynamic observables are…

Soft Condensed Matter · Physics 2015-05-14 Marco Bernabei , Angel J. Moreno , Juan Colmenero

Quantitative studies of cell metabolism are often based on large chemical reaction network models. A steady state approach is suited to analyze phenomena on the timescale of cell growth and circumvents the problem of incomplete experimental…

Molecular Networks · Quantitative Biology 2019-02-20 A. De Martino , D. De Martino , E. Marinari

Ultrafast non-equilibrium dynamics offer a route to study the microscopic interactions that govern macroscopic behavior. In particular, photo-induced phase transitions (PIPTs) in solids provide a test case for how forces, and the resulting…