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Theoretical and Computational Chemistry

Working Paper

Machine-Learning-Driven Exploration of Surface Reconstructions of Reduced Rutile TiO2

Yonghyuk Lee, Xiaobo Chen, Sabrina Gericke, Meng Li, Dmitri Zakharov, Ashley Head, Judith Yang, Anastassia Alexandrova

Version 1 posted 20 December 2024

0

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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

833

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1

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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

751

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3

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Materials Science

Working Paper

Germanium Distributions in Zeolites Derived from Neural Network Potentials.

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

Version 1 posted 19 June 2024

195

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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

564

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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 2 posted 09 October 2024

1,217

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Theoretical and Computational Chemistry

Working Paper

Improved description of environment and vibronic effects with electrostatically embedded ML potentials

Kirill Zinovjev, Carles Curutchet

Version 2 posted 06 December 2024

176

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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 2 posted 08 July 2024

1,555

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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

736

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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 2 posted 02 August 2024

431

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22 results in machine learning potentials in Keywords: machine learning potentials

Machine-Learning-Driven Exploration of Surface Reconstructions of Reduced Rutile TiO2

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

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

Germanium Distributions in Zeolites Derived from Neural Network Potentials.

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

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

Improved description of environment and vibronic effects with electrostatically embedded ML potentials

Modeling Chemical Processes in Explicit Solvents with Machine Learning Potentials

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

Spatially resolved uncertainties for machine learning potentials

22 results in Keywords: machine learning potentials