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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

772

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1

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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

493

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Theoretical and Computational Chemistry

Working Paper

The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes

Kara Fong, Barbara Sumic, Niamh O'Neill, Christoph Schran, Clare Grey, Angelos Michaelides

Version 1 posted 21 February 2024

643

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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

484

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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

433

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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

1,190

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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

47

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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

613

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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

1,201

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1

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Refine Search

9 results in machine learning potentials in Keywords: machine learning potentials

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

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

The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes

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

∆-Learning for Coarse-Grained Potentials

Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials

Spatially resolved uncertainties for machine learning potentials

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

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

9 results in Keywords: machine learning potentials