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Materials Chemistry

Simulations enabled by machine learning potentials predict sulfide solid electrolyte with covalent network structure and glassy behavior

Gavin Winter, Nathan Walemba, Rafael Gómez-Bombarelli

330

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Theoretical and Computational Chemistry

Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials

Omid Shayestehpour, Stefan Zahn

482

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Materials Science

Nature of the amorphous-amorphous interfaces in solid-state batteries revealed using machine-learned interatomic potentials

Chuhong Wang, Muratahan Aykol, Tim Mueller

849

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2

Citations

Theoretical and Computational Chemistry

Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials

Konstantin Jakob, Karsten Reuter, Johannes T. Margraf

300

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Theoretical and Computational Chemistry

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Zhen Liu, Jessica Vinskus, Yue Fu, Peng Liu, Kevin Noonan, Olexandr Isayev

700

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1

Citations

Theoretical and Computational Chemistry

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

Elena Gelžinytė, Mario Öeren, Matthew D. Segall, Gábor Csányi

597

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Nanoscience

Accelerated Prediction of Atomically Precise Cluster Structures Using On-the-fly Machine Learning

Yunzhe Wang, Shanping Liu, Peter Lile, Sam Norwood, Alberto Hernandez, Sukriti Manna, Tim Mueller

600

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Energy

Computational Design of Double-Layer Cathode Coatings in All-Solid-State Batteries

Chuhong Wang, Koutarou Aoyagi, Tim Mueller

1,276

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Theoretical and Computational Chemistry

Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials

Xujian Wang, Xiongwu Wu, Bernard Brooks, Junmei Wang

800

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Catalysis

Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions

Dylan Anstine, Roman Zubatyuk, Liliana Gallegos, Robert Paton, Olaf Wiest, Benjamin Nebgen, Travis Jones, Gabe Gomes, Sergei Tretiak, Olexandr Isayev

987

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10 results in Machine Learning Interatomic Potential in Keywords: Machine Learning Interatomic Potential

Simulations enabled by machine learning potentials predict sulfide solid electrolyte with covalent network structure and glassy behavior

Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials

Nature of the amorphous-amorphous interfaces in solid-state batteries revealed using machine-learned interatomic potentials

Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

Accelerated Prediction of Atomically Precise Cluster Structures Using On-the-fly Machine Learning

Computational Design of Double-Layer Cathode Coatings in All-Solid-State Batteries

Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials

Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions

10 results in Keywords: Machine Learning Interatomic Potential