Uncertainty quantification in molecular simulations with dropout neural network potentials von Wen, Mingjian, Tadmor, Ellad B.

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Quantum chemical calculations of lithium-ion battery electrolyte and interphase species von Spotte-Smith, Evan Walter Clark, Blau, Samuel M., Xie, Xiaowei, Patel, Hetal D., Wen, Mingjian, Wood, Brandon, Dwaraknath, Shyam, Persson, Kristin Aslaug

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Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials von Kurniawan, Yonatan, Petrie, Cody L., Williams, Kinamo J., Transtrum, Mark K., Tadmor, Ellad B., Elliott, Ryan S., Karls, Daniel S., Wen, Mingjian

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Hybrid neural network potential for multilayer graphene von Wen, Mingjian, Tadmor, Ellad B.

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Constitutive modeling for the anisotropic uniaxial ratcheting behavior of Zircaloy-4 alloy at room temperature von Li, Hua, Wen, Mingjian, Chen, Gang, Yu, Weiwei, Chen, Xu

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KLIFF: A framework to develop physics-based and machine learning interatomic potentials von Wen, Mingjian, Afshar, Yaser, Elliott, Ryan S., Tadmor, Ellad B.

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Highly selective zinc ion removal by the synergism of functional groups and defects from N, S co-doped biochar von Wang, Changlin, Adhikari, Santosh, Li, Yuqi, Wen, Mingjian, Wang, Yang

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Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks von Venetos, Maxwell C., Wen, Mingjian, Persson, Kristin A.

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BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules von Wen, Mingjian, Blau, Samuel M, Spotte-Smith, Evan Walter Clark, Dwaraknath, Shyam, Persson, Kristin A

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Hybrid neural network potential for multilayer graphene von Wen, Mingjian, Tadmor, Ellad B

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Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining von Wen, Mingjian, Blau, Samuel M, Xie, Xiaowei, Dwaraknath, Shyam, Persson, Kristin A

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Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network von Xie, Xiaowei, Clark Spotte-Smith, Evan Walter, Wen, Mingjian, Patel, Hetal D, Blau, Samuel M, Persson, Kristin A

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predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network von Vijay, Sudarshan, Venetos, Maxwell C, Spotte-Smith, Evan Walter Clark, Kaplan, Aaron D, Wen, Mingjian, Persson, Kristin A

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A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis von Wen, Mingjian, Shirodkar, Sharmila N., Plecháč, Petr, Kaxiras, Efthimios, Elliott, Ryan S., Tadmor, Ellad B.

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Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures von Wen, Mingjian, Carr, Stephen, Fang, Shiang, Kaxiras, Efthimios, Tadmor, Ellad B.

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CoeffNet : predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network von Vijay, Sudarshan, Venetos, Maxwell C, Spotte-Smith, Evan Walter Clark, Kaplan, Aaron D, Wen, Mingjian, Persson, Kristin A

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Readout electronics of a muon beam monitor for the phase-α of the COMET experiment von Qin, Zhizhen, Feng, Changqing, Teng, Yao, Xu, Yu, Ning, Yunsong, Tang, Jian, Wen, Mingjian, Ge, Ke, Liu, Shubin

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Chemical reaction networks and opportunities for machine learning von Wen, Mingjian, Spotte-Smith, Evan Walter Clark, Blau, Samuel M, McDermott, Matthew J, Krishnapriyan, Aditi S, Persson, Kristin A

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An equivariant graph neural network for the elasticity tensors of all seven crystal systems von Wen, Mingjian, Horton, Matthew K, Munro, Jason M, Huck, Patrick, Persson, Kristin A

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A KIM-compliant potfit for fitting sloppy interatomic potentials: application to the EDIP model for silicon von Wen, Mingjian, Li, Junhao, Brommer, Peter, Elliott, Ryan S, Sethna, James P, Tadmor, Ellad B

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