Mingjian Wen

Mingjian Wen

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

About

Posts

portfolio

publications

Constitutive modeling for the anisotropic uniaxial ratcheting behavior of Zircaloy-4 alloy at room temperature

Published in J. Nucl. Mater., 2013

Constitutive modeling for the anisotropic uniaxial ratcheting behavior of Zircaloy-4 alloy at room temperature.

Recommended citation: H Li, M Wen, G Chen, W Yu, X Chen, "Constitutive modeling for the anisotropic uniaxial ratcheting behavior of Zircaloy-4 alloy at room temperature." J. Nucl. Mater., 443, 152-160, (2013). https://doi.org/10.1016/j.jnucmat.2013.06.052

A KIM-compliantpotfitfor fitting sloppy interatomic potentials: application to the EDIP model for silicon

Published in Modell. Simul. Mater. Sci. Eng., 2017

A KIM-compliantpotfitfor fitting sloppy interatomic potentials: application to the EDIP model for silicon.

Recommended citation: M Wen, J Li, P Brommer, RS Elliott, JP Sethna, EB Tadmor, "A KIM-compliantpotfitfor fitting sloppy interatomic potentials: application to the EDIP model for silicon." Modell. Simul. Mater. Sci. Eng., 25, 014001, (2017). https://doi.org/10.1088/0965-0393/25/1/014001

A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis

Published in J. Appl. Phys., 2017

A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis.

Recommended citation: M Wen, SN Shirodkar, P Plech{\'{a}}{\v{c}}, E Kaxiras, RS Elliott, EB Tadmor, "A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis." J. Appl. Phys., 122, 244301, (2017). https://doi.org/10.1063/1.5007842

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

Published in Chemical Science, 2020

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.

Recommended citation: M Wen, SM Blau, EW Spotte-Smith, S Dwaraknath, KA Persson, "BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules." Chemical Science, 12, 1858-1868, (2020). https://doi.org/10.1039/D0SC05251E

Data-Driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network

Published in Journal of the American Chemical Society, 2021

Data-Driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network.

Recommended citation: X Xie, EW Spotte-Smith, M Wen, HD Patel, SM Blau, KA Persson, "Data-Driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated reaction network." Journal of the American Chemical Society, 143, 13245-13258, (2021). https://doi.org/10.1021/jacs.1c05807

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

Published in Chemical Science, 2022

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

Recommended citation: M Wen, SM Blau, X Xie, S Dwaraknath, K Persson, "Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining." Chemical Science, 13, 1446-1458, (2022). https://doi.org/10.1039/D1SC06515G

talks

Posters

  • 01/17/2019 “Uncertainty quantification in atomistic simulations with dropout neural network potentials,” U. S. Association for Computational Mechanics Conference, Johns Hopkings University, Baltimore, MD. View poster
  • 12/01/2016 “Stillinger-Weber potential for MoS2: parameterization and sensitivity analysis,” Workshop on Multiscale Mathematical Modeling and Design Realization of Novel 2D Functional Materials, Harvard University, Cambridge, MA. View poster
  • 12/07/2015 “Fitting interatomic models for layered heterostructures using OpenKIM,” Workshop on Multiscale Mathematical Modeling and Design Realization of Novel 2D Functional Materials, Harvard University, Cambridge, MA. View poster

Talks

  • 04/05/2021 “Accurate prediction of bond dissociation energies for molecules of any charge,” ACS Spring Meeting, virtual.
  • 03/02/2020 “Uncertainty quantification in atomistic simulations with dropout neural network potentials,”, APS March Meeting, virtual.
  • 02/18/2019 “Uncertainty quantification in atomistic simulations with dropout neural network potentials,” Workshop on Machine Learning for Computational Fluid and Solid Dynamics, Los Alamos National Labratory, Santa Fe, NM.
  • 10/19/2018 “Machine learning interatomic potentials for multilayer graphene structures,” Conference on Emerging Ideas in Mechanics and Materials Science, University of Minnesota, Minneapolis, MN.
  • 06/07/2018 “Interatomic potential models for 2D heterostructures,” 18th U. S. National Congress for Theoretical and Applied Mechanics (USNC/TAM), Northwestern University, Chicago, IL.
  • 11/21/2017 “Development of interatomic potentials for 2D heterostructures,” AEM Mechanics Research Seminar, University of Minnesota, Minneapolis, MN.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.