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Physical Chemistry

Working Paper

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Mingjian Wen, Samuel M. Blau, Xiaowei Xie, Shyam Dwaraknath, Kristin A. Persson

Version 2 posted 11 January 2022

785

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Materials Chemistry

Working Paper

CoeffNet: Predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network

Sudarshan Vijay, Maxwell Venetos, Evan Spotte-Smith, Aaron Kaplan, Mingjian Wen, Kristin Persson

Version 1 posted 21 August 2023

424

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Theoretical and Computational Chemistry

Working Paper

BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

Mingjian Wen, Samuel Blau, Evan Spotte-Smith, Shyam Dwaraknath, Kristin Persson

Version 2 posted 23 September 2020

788

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Materials Science

Working Paper

Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks

Maxwell Venetos, Mingjian Wen, Kristin Persson

Version 1 posted 26 October 2022

453

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Theoretical and Computational Chemistry

Working Paper

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

Evan Walter Clark Spotte-Smith, Samuel Blau, Xiaowei Xie, Hetal Patel, Mingjian Wen, Brandon Wood, Shyam Dwaraknath, Kristin Persson

Version 3 posted 21 May 2021

1,328

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1

Citations

Theoretical and Computational Chemistry

Working Paper

HEPOM: Using Graph Neural Networks for the accelerated predictions of Hydrolysis Free Energies in different pH conditions.

Rishabh D. Guha, Santiago Vargas, Evan Walter Clark Spotte-Smith, Alexander Rizzolo Epstein, Maxwell Venetos, Ryan Kingsbury, Mingjian Wen, Samuel Blau, Kristin A. Persson

Version 1 posted 28 November 2024

227

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Materials Science

Working Paper

Atomate2: Modular workflows for materials science

Alex Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl Chrzan, Jacob Clary, Orion Cohen, Christina Ertural, Max Gallant, Janine George, Sophie Gerits, Rhys Goodall, Rishabh Guha, Geoffroy Hautier, Matthew Horton, Aaron Kaplan, Ryan Kingsbury, Matthew Kuner, Bryant Li, Xavier Linn, Matthew McDermott, Rohith Srinivaas Mohanakrishnan, Aakash Naik, Jeffrey Neaton, Kristin Persson, Guido Petretto, Thomas Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu, Anubhav Jain

Version 1 posted 22 January 2025

1,116

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1

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Refine Search

7 results in Mingjian Wen in Authors: Mingjian Wen

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

CoeffNet: Predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network

BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules

Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks

Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

HEPOM: Using Graph Neural Networks for the accelerated predictions of Hydrolysis Free Energies in different pH conditions.

Atomate2: Modular workflows for materials science

7 results in Authors: Mingjian Wen