Publications

28.
An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li7P3S11. Bryant Y. Li, Vir Karan, Aaron D. Kaplan, Mingjian Wen and Kristin A. Persson. The Journal of Physical Chemistry C, 129, 2025.
27.
Atomate2: modular workflows for materials science. Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E.A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith S. Mohanakrishnan, Aakash N. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A.R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu and Anubhav Jain. Digital Discovery, 4, 1944--1973, 2025.
26.
Accelerated data-driven materials science with the Materials Project. Matthew K. Horton, Patrick Huck, Ruo X. Yang, Jason M. Munro, Shyam Dwaraknath, Alex M. Ganose, Ryan S. Kingsbury, Mingjian Wen, Jimmy X. Shen, Tyler S. Mathis, Aaron D. Kaplan, Karlo Berket, Janosh Riebesell, Janine George, Andrew S. Rosen, Evan W.C. Spotte-Smith, Matthew J. McDermott, Orion A. Cohen, Alex Dunn, Matthew C. Kuner, Gian-Marco Rignanese, Guido Petretto, David Waroquiers, Sinead M. Griffin, Jeffrey B. Neaton, Daryl C. Chrzan, Mark Asta, Geoffroy Hautier, Shreyas Cholia, Gerbrand Ceder, Shyue P. Ong, Anubhav Jain and Kristin A. Persson. Nature Materials, 24, 1522–1532, 2025.
25.
Cartesian atomic moment machine learning interatomic potentials. Mingjian Wen, Wei-Fan Huang, Jin Dai and Santosh Adhikari. npj Computational Materials, 11, 128, 2025.
24.
HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions. Rishabh D. Guha, Santiago Vargas, Evan W.C. Spotte-Smith, Alexander R. Epstein, Maxwell Venetos, Ryan Kigsbury, Mingjian Wen, Samuel M. Blau and Kristin A. Persson. Journal of Chemical Information and Modeling, 65, 3963--3975, 2025.
23.
Highly selective zinc ion removal by the synergism of functional groups and defects from N, S co-doped biochar. Changlin Wang, Santosh Adhikari, Yuqi Li, Mingjian Wen and Yang Wang. Separation and Purification Technology, 354, 129446, 2025.
22.
Uncertainty quantification and propagation in atomistic machine learning. Jin Dai, Santosh Adhikari and Mingjian Wen. Reviews in Chemical Engineering, 41, 333-357, 2025.
21.
An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Mingjian Wen, Matthew K. Horton, Jason M. Munro, Patrick Huck and Kristin A. Persson. Digital Discovery, 3, 869--882, 2024.
20.
CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network. Sudarshan Vijay, Maxwell C. Venetos, Evan W.C. Spotte-Smith, Aaron D. Kaplan, Mingjian Wen and Kristin A. Persson. Chemical Science, 15, 2923--2936, 2024.
19.
Jobflow: Computational Workflows Made Simple. Andrew S. Rosen, Max Gallant, Janine George, Janosh Riebesell, Hrushikesh Sahasrabuddhe, Jimmy-Xuan Shen, Mingjian Wen, Matthew L. Evans, Guido Petretto, David Waroquiers, Gian-Marco Rignanese, Kristin A. Persson, Anubhav Jain and Alex M. Ganose. Journal of Open Source Software, 9, 5995, 2024.
18.
Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks. Maxwell C. Venetos, Mingjian Wen and Kristin A. Persson. The Journal of Physical Chemistry A, 127, 2388--2398, 2023.
17.
Chemical reaction networks and opportunities for machine learning. Mingjian Wen, Evan W.C. Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan and Kristin A. Persson. Nature Computational Science, 3, 12--24, 2023.
16.
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling. Yonatan Kurniawan, Cody L. Petrie, Mark K. Transtrum, Ellad B. Tadmor, Ryan S. Elliott, Daniel S. Karls and Mingjian Wen. e-Science, 367--377, 2022.
15.
Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties. Zeren Shui, Daniel S. Karls, Mingjian Wen, Ilia A. Nikiforov, Ellad B. Tadmor and George Karypis. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
14.
Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials. Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams Jr, Mark K. Transtrum, Ellad B. Tadmor, Ryan S. Elliott, Daniel S. Karls and Mingjian Wen. The Journal of Chemical Physics, 156, 214103, 2022.
13.
Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining. Mingjian Wen, Samuel M. Blau, Xiaowei Xie, Shyam Dwaraknath and Kristin A. Persson. Chemical Science, 13, 1446--1458, 2022.
12.
KLIFF: A framework to develop physics-based and machine learning interatomic potentials. Mingjian Wen, Yaser Afshar, Ryan S. Elliott and Ellad B. Tadmor. Computer Physics Communications, 272, 108218, 2022.
11.
Data-Driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network. Xiaowei Xie, Evan W.C. Spotte-Smith, Mingjian Wen, Hetal D. Patel, Samuel M. Blau and Kristin A. Persson. Journal of the American Chemical Society, 143, 13245--13258, 2021.
10.
Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species. Evan W.C. Spotte-Smith, Samuel Blau, Xiaowei Xie, Hetal Patel, Mingjian Wen, Brandon Wood, Shyam Dwaraknath and Kristin Persson. Scientific Data, 8, 203, 2021.
9.
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules. Mingjian Wen, Samuel M. Blau, Evan W.C. Spotte-Smith, Shyam Dwaraknath and Kristin A. Persson. Chemical Science, 12, 1858--1868, 2020.
8.
Uncertainty quantification in molecular simulations with dropout neural network potentials. Mingjian Wen and Ellad B. Tadmor. npj Computational Materials, 6, 124, 2020.
7.
Hybrid neural network potential for multilayer graphene. Mingjian Wen and Ellad B. Tadmor. Physical Review B, 100, 195419, 2019.
6.
Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures. Mingjian Wen, Stephen Carr, Shiang Fang, Efthimios Kaxiras and Ellad B. Tadmor. Physical Review B, 98, 235404, 2018.
5.
A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis. Mingjian Wen, Sharmila N. Shirodkar, Petr Plech\'a\vc, Efthimios Kaxiras, Ryan S. Elliott and Ellad B. Tadmor. Journal of Applied Physics, 122, 244301, 2017.
4.
A KIM-compliantpotfitfor fitting sloppy interatomic potentials: application to the EDIP model for silicon. Mingjian Wen, Junhao Li, Peter Brommer, Ryan S. Elliott, James P. Sethna and Ellad B. Tadmor. Modelling and Simulation Materials Science and Engineering, 25, 014001, 2017.
3.
Interpolation effects in tabulated interatomic potentials. M Wen, S M. Whalen, R S. Elliott and E B. Tadmor. Modelling and Simulation Materials Science and Engineering, 23, 074008, 2015.
2.
Constitutive modeling for the anisotropic uniaxial ratcheting behavior of Zircaloy-4 alloy at room temperature. Hua Li, Mingjian Wen, Gang Chen, Weiwei Yu and Xu Chen. Journal of Nuclear Materials, 443, 152--160, 2013.
1.
Uniaxial ratcheting behavior of Zircaloy-4 tubes at room temperature. Mingjian Wen, Hua Li, Dunji Yu, Gang Chen and Xu Chen. Materials Design, 46, 426--434, 2013.