Computer Meets Theoretical Physics

Battimelli, Giovanni; Ciccotti, Giovanni; Greco, Pietro

doi:10.1007/978-3-030-39399-1

Cited by 23 publications

(9 citation statements)

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“…Our article is in the line of previous articles published on various historical aspects of molecular simulations [Battimelli,2018;Levesque,2018;Mareschal, 2018]. Let us also mention a recent book which appeared on related topics [Battimelli, 2020].…”

Section: Introductionmentioning

confidence: 86%

The early years of quantum Monte Carlo (1): the ground state

Mareschal

2021

EPJ H

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The first Monte Carlo simulation on a computer was performed at the initiative of John Von Neumann in the years 1947-48. It concerned the direct simulation of the fission reaction : many of the elementary steps, like, for example, the reactive events when a neutron collides with an Uranium nucleus, were modeled as stochastic processes and use was made of (pseudo-)random numbers to that effect. Von Neumann's simulation proposal was described in a letter written to the director of the theory division in Los Alamos, Dr. Richtmyer. The letter has been since declassified ; it is written with so 3 many details that one can figure out precisely what the computer is really doing. The simulation itself took several years to be performed, it involved several people, among whom John Von Neumann and his wife Klara, Nick Metropolis, Stan Ulam and a few others. It was performed on various computers, starting from the historic ENIAC, within a rapidly-evolving hardware architecture as well as programming technique: at some stage, it involved the creation of the storedprogram concept [Haigh, 2014].This work is well documented and has been already analyzed by Peter Galison [Galison, 1996] as symptomatic of the paradigm shift induced by the computer in the physical sciences (the so-called trading zone).Those years of post-war period were years of great intellectual freedom and creativity. It also led to the organisation of several scientific meetings where engineers, chemists, physicists and mathematicians would discuss the possibilities offered by the new electronic machines [Hurd,1985]. Among topics being discussed, the use of statistical sampling and probability theory to produce solutions to partial differential equations and integro-differential equations .

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Section: Introductionmentioning

confidence: 86%

The early years of quantum Monte Carlo (1): the ground state

Mareschal

2021

EPJ H

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“…Atomistic simulation methods such as molecular dynamics or Monte Carlo have emerged as general methods at the intersection of theoretical and computational physics [40]. In this review, we consider atomistic simulation approaches as samplers that yield high-dimensional data sets from some underlying probability distributions.…”

Section: Atomistic Simulationsmentioning

confidence: 99%

Manifold Learning in Atomistic Simulations: A Conceptual Review

Rydzewski¹,

Chen²,

Valsson³

2023

Preprint

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Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Such practice is needed in atomistic simulations of dynamical systems where even thousands of degrees of freedom are sampled. An abundance of such data makes it strenuous to gain insight into a specific physical problem. Our primary aim in this review is to focus on unsupervised machine learning methods that can be used on simulation data to find a low-dimensional manifold providing a collective and informative characterization of the studied process. Such manifolds can be used for sampling longtimescale processes and free-energy estimation. We describe methods that can work on data sets from standard and enhanced sampling atomistic simulations. Compared to recent reviews on manifold learning for atomistic simulations, we consider only methods that construct low-dimensional manifolds based on Markov transition probabilities between high-dimensional samples. We discuss these techniques from a conceptual point of view, including their underlying theoretical frameworks and possible limitations.

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“…Computer simulation is a powerful way of studying complex systems. Molecular dynamics (MD) is a prominent example and it brought new information on protein structure and biomolecule interactions [6,7]. A brief discussion of the basic principles, recent advances, and limitations seems warranted to discuss the relevance of this approach to cell function.…”

Section: The Initial Choice Of Parameters Used To Describe Cell Prope...mentioning

confidence: 99%

Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

Bongrand

2022

CIMB

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An important goal of biological research is to explain and hopefully predict cell behavior from the molecular properties of cellular components. Accordingly, much work was done to build extensive “omic” datasets and develop theoretical methods, including computer simulation and network analysis to process as quantitatively as possible the parameters contained in these resources. Furthermore, substantial effort was made to standardize data presentation and make experimental results accessible to data scientists. However, the power and complexity of current experimental and theoretical tools make it more and more difficult to assess the capacity of gathered parameters to support optimal progress in our understanding of cell function. The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions. Recent experimental studies on important cell functions, such as adhesion and processing of environmental cues for decision-making, support the suggestion that it should be rewarding to complement standard binding properties such as affinity and kinetic constants, or even force dependence, with less frequently used parameters such as conformational flexibility or size of binding molecules.

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Computer Meets Theoretical Physics

Cited by 23 publications

References 0 publications

The early years of quantum Monte Carlo (1): the ground state

The early years of quantum Monte Carlo (1): the ground state

Manifold Learning in Atomistic Simulations: A Conceptual Review

Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

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