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Nanoscience

Working Paper

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods

Antti Pihlajamaki, Joonas Hamalainen, Joakim Linja, Paavo Nieminen, Sami Malola, Tommi Karkkainen, Hannu Hakkinen

Version 1 posted 21 February 2020

580

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Catalysis

Working Paper

Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-based Grand-Canonical Monte Carlo

Jiayan Xu, Wenbo Xie, Yulan Han, Peijun Hu

Version 1 posted 08 August 2022

319

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Catalysis

Working Paper

Screening Cu-Zeolites for Methane Activation using Curriculum-based Training

Jiawei Guo, Tyler Sours, Sam Holton, Chenghan Sun, Ambarish Kulkarni

Version 3 posted 09 October 2023

625

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Theoretical and Computational Chemistry

Working Paper

Electrostatic Embedding of Machine Learning Potentials

Kirill Zinovjev

Version 3 posted 07 September 2022

623

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1

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Physical Chemistry

Working Paper

WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets

Max Pinheiro Jr, Shuang Zhang, Pavlo O. Dral, Mario Barbatti

Version 1 posted 19 September 2022

376

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Materials Science

Working Paper

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Tyler Sours, Ambarish Kulkarni

Version 2 posted 23 December 2022

322

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Physical Chemistry

Working Paper

Hydrogen Absorption Promoted by Surface Melting: Guidelines for High-Pressure Superhydride Synthesis

Ryuhei Sato, Lewis Conway, Di Zhang, Chris Pickard, Kazuto Akagi, Kartik Sau, Li Hao, Shin-ichi Orimo

Version 3 posted 17 September 2024

442

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Theoretical and Computational Chemistry

Working Paper

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-based Potentials

Frédéric Célerse, Matthew Wodrich, Sergi Vela, Simone Gallarati, Raimon Fabregat, Veronika Juraskova, Clémence Corminboeuf

Version 1 posted 06 December 2023

115

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Theoretical and Computational Chemistry

Working Paper

Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis

Changxi Yang, Chenyu Wu, Wenbo Xie, Daiqian Xie, Peijun Hu

Version 1 posted 14 October 2024

309

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Theoretical and Computational Chemistry

Working Paper

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine learning interatomic potential simulations

Omid Shayestehpour, Stefan Zahn

Version 1 posted 09 August 2024

775

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18 results in Machine learning potential in Keywords: Machine learning potential

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods

Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-based Grand-Canonical Monte Carlo

Screening Cu-Zeolites for Methane Activation using Curriculum-based Training

Electrostatic Embedding of Machine Learning Potentials

WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials

Hydrogen Absorption Promoted by Surface Melting: Guidelines for High-Pressure Superhydride Synthesis

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-based Potentials

Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine learning interatomic potential simulations

18 results in Keywords: Machine learning potential