Publications

28.
An Atomistic Study of Reactivity in Solid-State Electrolyte Interphase Formation for Li/Li7P3S11. Bryant Y. Li and Vir Karan and Aaron D. Kaplan and Mingjian Wen and Kristin A. Persson. The Journal of Physical Chemistry C, 129, 2025.
27.
Atomate2: modular workflows for materials science. Alex M. Ganose and Hrushikesh Sahasrabuddhe and Mark Asta and Kevin Beck and Tathagata Biswas and Alexander Bonkowski and Joana Bustamante and Xin Chen and Yuan Chiang and Daryl C. Chrzan and Jacob Clary and Orion A. Cohen and Christina Ertural and Max C. Gallant and Janine George and Sophie Gerits and Rhys E. A. Goodall and Rishabh D. Guha and Geoffroy Hautier and Matthew Horton and T. J. Inizan and Aaron D. Kaplan and Ryan S. Kingsbury and Matthew C. Kuner and Bryant Li and Xavier Linn and Matthew J. McDermott and Rohith Srinivaas Mohanakrishnan and Aakash N. Naik and Jeffrey B. Neaton and Shehan M. Parmar and Kristin A. Persson and Guido Petretto and Thomas A. R. Purcell and Francesco Ricci and Benjamin Rich and Janosh Riebesell and Gian-Marco Rignanese and Andrew S. Rosen and Matthias Scheffler and Jonathan Schmidt and Jimmy-Xuan Shen and Andrei Sobolev and Ravishankar Sundararaman and Cooper Tezak and Victor Trinquet and Joel B. Varley and Derek Vigil-Fowler and Duo Wang and David Waroquiers and Mingjian Wen and Han Yang and Hui Zheng and Jiongzhi Zheng and Zhuoying Zhu and Anubhav Jain. Digital Discovery, 4, 1944--1973, 2025.
26.
Accelerated data-driven materials science with the Materials Project. Horton, Matthew K. and Huck, Patrick and Yang, Ruo Xi and Munro, Jason M. and Dwaraknath, Shyam and Ganose, Alex M. and Kingsbury, Ryan S. and Wen, Mingjian and Shen, Jimmy X. and Mathis, Tyler S. and Kaplan, Aaron D. and Berket, Karlo and Riebesell, Janosh and George, Janine and Rosen, Andrew S. and Spotte-Smith, Evan W. C. and McDermott, Matthew J. and Cohen, Orion A. and Dunn, Alex and Kuner, Matthew C. and Rignanese, Gian-Marco and Petretto, Guido and Waroquiers, David and Griffin, Sinead M. and Neaton, Jeffrey B. and Chrzan, Daryl C. and Asta, Mark and Hautier, Geoffroy and Cholia, Shreyas and Ceder, Gerbrand and Ong, Shyue Ping and Jain, Anubhav and Persson, Kristin A.. Nature Materials, 2025.
25.
Cartesian atomic moment machine learning interatomic potentials. Wen, Mingjian and Huang, Wei-Fan and Dai, Jin and Adhikari, Santosh. npj Computational Materials, 11, 128, 2025.
24.
Highly selective zinc ion removal by the synergism of functional groups and defects from N, S co-doped biochar. Wang, Changlin and Adhikari, Santosh and Li, Yuqi and Wen, Mingjian and Wang, Yang. Separation and Purification Technology, 354, 129446, 2025.
23.
Uncertainty quantification and propagation in atomistic machine learning. Dai, Jin and Adhikari, Santosh and Wen, Mingjian. Reviews in Chemical Engineering, 2024.
22.
Jobflow: Computational Workflows Made Simple. Rosen, Andrew S and Gallant, Max and George, Janine and Riebesell, Janosh and Sahasrabuddhe, Hrushikesh and Shen, Jimmy-Xuan and Wen, Mingjian and Evans, Matthew L and Petretto, Guido and Waroquiers, David and Gian-Marco Rignanese and Kristin A. Persson and Anubhav Jain and Alex M. Ganose. Journal of Open Source Software, 9, 5995, 2024.
21.
An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Wen, Mingjian and Horton, Matthew K. and Munro, Jason M. and Huck, Patrick and Persson, Kristin A.. Digital Discovery, 3, 869--882, 2024.
20.
CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network. Vijay, Sudarshan and Venetos, Maxwell C. and Spotte-Smith, Evan Walter Clark and Kaplan, Aaron D. and Wen, Mingjian and Persson, Kristin A.. Chemical Science, 15, 2923--2936, 2024.
19.
HEPOM: A predictive framework for accelerated Hydrolysis Energy Predictions of Organic Molecules. Guha, Rishabh Debraj and Vargas, Santiago and Spotte-Smith, Evan Walter Clark and Epstein, Alex R and Venetos, Maxwell Christopher and Wen, Mingjian and Kingsbury, Ryan and Blau, Samuel M and Persson, Kristin. AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023.
18.
Machine learning full NMR chemical shift tensors of silicon oxides with equivariant graph neural networks. Venetos, Maxwell C and Wen, Mingjian and Persson, Kristin A. The Journal of Physical Chemistry A, 127, 2388--2398, 2023.
17.
Chemical reaction networks and opportunities for machine learning. Mingjian Wen and Evan Walter Clark Spotte-Smith and Samuel M. Blau and Matthew J. McDermott and 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. Kurniawan, Yonatan and Petrie, Cody L and Transtrum, Mark K and Tadmor, Ellad B and Elliott, Ryan S and Karls, Daniel S and Wen, Mingjian. e-Science, 367--377, 2022.
15.
Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties. Shui, Zeren and Karls, Daniel S and Wen, Mingjian and Nikiforov, Ilia A and Tadmor, Ellad B and Karypis, George. 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. Kurniawan, Yonatan and Petrie, Cody L and Williams Jr, Kinamo J and Transtrum, Mark K and Tadmor, Ellad B and Elliott, Ryan S and Karls, Daniel S and Wen, Mingjian. The Journal of Chemical Physics, 156, 214103, 2022.
13.
Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining. Wen, Mingjian and Blau, Samuel M. and Xie, Xiaowei and Dwaraknath, Shyam and Persson, Kristin A.. Chemical Science, 13, 1446--1458, 2022.
12.
KLIFF: A framework to develop physics-based and machine learning interatomic potentials. Mingjian Wen and Yaser Afshar and 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 and Evan Walter Clark Spotte-Smith and Mingjian Wen and Hetal D. Patel and 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. Spotte-Smith, Evan Walter Clark and Blau, Samuel and Xie, Xiaowei and Patel, Hetal and Wen, Mingjian and Wood, Brandon and Dwaraknath, Shyam and Persson, Kristin. Scientific Data, 8, 203, 2021.
9.
BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules. Wen, Mingjian and Blau, Samuel M and Spotte-Smith, Evan Walter Clark and Dwaraknath, Shyam and Persson, Kristin A. Chemical Science, 12, 1858--1868, 2020.
8.
Uncertainty quantification in molecular simulations with dropout neural network potentials. Wen, Mingjian and Tadmor, Ellad B. npj Computational Materials, 6, 124, 2020.
7.
Hybrid neural network potential for multilayer graphene. Wen, Mingjian and Tadmor, Ellad B. Physical Review B, 100, 195419, 2019.
6.
Dihedral-angle-corrected registry-dependent interlayer potential for multilayer graphene structures. Wen, Mingjian and Carr, Stephen and Fang, Shiang and Kaxiras, Efthimios and Tadmor, Ellad B.. 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 and Sharmila N. Shirodkar and Petr Plech\'a\vc and Efthimios Kaxiras and 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 and Junhao Li and Peter Brommer and Ryan S Elliott and 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 and S M Whalen and 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 and Mingjian Wen and Gang Chen and 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 and Hua Li and Dunji Yu and Gang Chen and Xu Chen. Materials Design, 46, 426--434, 2013.