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Theoretical and Computational Chemistry

Working Paper

∆-Learning for Coarse-Grained Potentials

Aditi Khot, Brett Savoie

Version 1 posted 27 March 2023

463

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Theoretical and Computational Chemistry

Working Paper

Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach

Sander Vandenhaute, Maarten Cools-Ceuppens, Simon DeKeyser, Toon Verstraelen, Veronique Van Speybroeck

Version 1 posted 04 November 2022

780

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1

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Materials Science

Working Paper

Gaussian Approximation Potentials for Accurate Thermal Properties of Two-Dimensional Materials

Tuğbey Kocabaş, Murat Keceli, Álvaro Vázquez-Mayagoitia, Cem Sevik

Version 1 posted 25 January 2023

500

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Materials Science

Working Paper

Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential.

Indranil Saha, Andreas Erlebach, Petr Nachtigall, Christopher J. Heard, Lukáš Grajciar

Version 3 posted 12 July 2023

629

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2

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Theoretical and Computational Chemistry

Working Paper

emle-engine: a flexible electrostatic machine learning embedding package for multiscale molecular dynamics simulations

Kirill Zinovjev, Lester Hedges, Rubén Montagud Andreu, Christopher Woods, Iñaki Tuñón, Marc W. van der Kamp

Version 2 posted 27 February 2024

584

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Theoretical and Computational Chemistry

Working Paper

Modelling ligand exchange in metal complexes with machine learning potentials

Veronika Juraskova, Gers Tusha, Hanwen Zhang, Lars Schäfer, Fernanda Duarte

Version 1 posted 28 June 2024

144

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Theoretical and Computational Chemistry

Working Paper

Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials

Hanwen Zhang, Veronika Juraskova, Fernanda Duarte

Version 1 posted 19 July 2023

1,279

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Theoretical and Computational Chemistry

Working Paper

Spatially resolved uncertainties for machine learning potentials

Esther Heid, Johannes Schörghuber, Ralf Wanzenböck, Georg. K. H. Madsen

Version 1 posted 02 May 2024

202

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Theoretical and Computational Chemistry

Working Paper

How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?

Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf

Version 1 posted 01 April 2022

1,225

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1

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Theoretical and Computational Chemistry

Working Paper

Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials

Simone Perego, Luigi Bonati

Version 1 posted 06 June 2024

388

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Refine Search

12 results in machine learning potentials in Keywords: machine learning potentials

∆-Learning for Coarse-Grained Potentials

Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach

Gaussian Approximation Potentials for Accurate Thermal Properties of Two-Dimensional Materials

Quantifying the effect of Si/Al ratio on proton solvation and water diffusion in H-FAU using reactive neural network potential.

emle-engine: a flexible electrostatic machine learning embedding package for multiscale molecular dynamics simulations

Modelling ligand exchange in metal complexes with machine learning potentials

Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials

Spatially resolved uncertainties for machine learning potentials

How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?

Data-efficient modeling of catalytic reactions via enhanced sampling and on-the-fly learning of machine learning potentials

12 results in Keywords: machine learning potentials