Publications

⁺Denotes corresponding author

• M. S. Chen⁺, S. Sacanna, G. M. Hocky⁺, "Sampling Free Energy Landscapes of Ionic Colloidal Crystal Systems using Machine-Learned Proxy Collective Variables." arXiv:2608.09714 (submitted)

• B. B. Hansen, M. S. Chen, G. Souza, K. Glynn, A. Robledo, E. Pelegano-Titmuss, K. Hightower, M. Muñoz, R. Pandian, C. Burda, A. Sanida, M. Dadmun, B. E. Gurkan, S. G. Greenbaum, M. E. Tuckerman, J. R. Sangoro, "Nonlinear Quantum Effects Drive Grotthuss Proton Conduction in Structured Electrolytes." Adv. Sci. e77781 (2026)
• P. Hollmer, N. Smina, J. P. Marquardt, M. S. Chen, S. Sacanna, G. M. Hocky, "PACSim: a Flexible Framework for Polymer-Attenuated Colloidal Self-Assembly Simulations." Phys. Rev. E. 114, 035423 (2026)
• N. I. Hausman, J. Kelly, M. S. Chen, F. Hu, A. Lee, A. Montoya-Castillo, G. S. Schlau-Cohen, T. E. Markland, "Streamlining analysis and design of two-dimensional electronic spectroscopy using machine learning." J. Chem. Phys. 165, 074109 (2026)
• A. Snider, M. S. Chen, T. E. Markland, C. M. Isborn, "Efficient simulation of optical spectra via machine learning and physical decomposition of environmental effects." J. Chem. Phys. 164, 134102 (2026)
• M. Muñoz, M. S. Chen, G. Souza, T. Simunovic, V. Khokhar, P. Qian, J. Wainright, R. Savinell, A. Parnell, S. Parnell, R. C. Kilbride, T. A. Zawodzinski, M. Dadmun, S. G. Greenbaum, J. Rodríguez-López, M. E. Tuckerman, B. Gurkan, “Structured Electrolytes Facilitate Grotthuss-type Transport for Enhanced Proton Coupled Electron Transfer Reactions.” Proc. Natl. Acad. Sci. 123 (1) e2530367122 (2026)
• M. S. Chen⁺, A. Robledo, C. Schafer, K. Y. Han, C. Clementi, M. E. Tuckerman⁺, “Machine Learning-Accelerated Path Integral Molecular Dynamics Simulations of Reactive Organic Electrolytes.” J. Chem. Phys. 163, 144110 (2025)
• Y. Wang, K. Takaba, M. S. Chen, M. Wieder, Y. Xu, J. Z. H. Zhang, K. Yu, X. Wang, L. Zhang, D. J. Cole, J. A. Rackers, J. G. Greener, P. Eastman, S. Martiniani, M. E. Tuckerman, “On the design space between molecular mechanics and machine learning force fields.” Appl. Phys. Rev., 12, 021304 (2025)
• S. Zang, S. Paul, C. W. Leung, M. S. Chen, T. Hueckel, G. M. Hocky, S. Sacanna, “Direct observation and control of non-classical crystallization pathways in binary colloidal systems.” Nat. Commun., 16, 3645 (2025)
• J. Kelly, F. Hu, A. Damiani, M. S. Chen, A. Snider, M. Son, A. Lee, P. Gupta, A. Montoya-Castillo, T.J. Zuehlsdorff, G. Schlau-Cohen, C.M. Isborn, T.E. Markland, “Two-Dimensional Electronic Spectroscopy in the Condensed Phase Using Equivariant Transformer Accelerated Molecular Dynamics Simulations.” J. Phys. Chem. Lett., 6, 22, 5561–5569 (2025)
• F. Hu, M. S. Chen, G. M. Rotskoff, M. W. Kanan, T. E. Markland, “Accurate and Efficient Structure Elucidation from Routine One-Dimensional NMR Spectra Using Multitask Machine Learning.” ACS Cent. Sci., 10, 11, 2162–2170 (2024)
• Khan, P. Vaish, Y. Pang, N. Kowshik, M. S. Chen, C. H. Batton, G. M. Rotskoff, J. W. Mullinax, B. K. Clark, B. M. Rubenstein, N. M. Tubman, “Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning.” arXiv:2406.08554 (2024)
• M. S. Chen, Y. Mao, P. Gupta, A. Snider, A. Montoya-Castillo, T. J. Zuehlsdorff, C. M. Isborn, T. E. Markland. “Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure.” J. Phys. Chem. Lett., 14, 29, 6610 (2023)
• M. S. Chen, J. Lee, H. Ye, T. C. Berkelbach, D. R. Reichman, T. E. Markland. “Data-efficient machine learning potentials from transfer learning of periodic correlated electronic structure methods: liquid water at AFQMC, CCSD, and CCSD(T) accuracy.” J. Chem. Theory Comput. 19, 14, 4510 (2023)
• Montoya-Castillo, M. S. Chen, S. L. Raj, K. A. Jung, K. S. Kjaer, T. Morawietz, K. J. Gaffney, T. B. van Driel and T. E. Markland. “Optically induced anisotropy in time-resolved scattering: Imaging molecular scale structure and dynamics in disordered media with experiment and theory.” Phys. Rev. Lett. 129, 056001 (2022)
• Z. Huang, M. S. Chen, C. P. Woroch, T. E. Markland, M. W. Kanan. “A Framework for Automated Structure Elucidation from Routine NMR Spectra.” Chem. Sci., 12, 15329 (2021)
• M. S. Chen, T. Morawietz, H. Mori, T. E. Markland, N. Artrith. “AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials.” J. Chem. Phys. 155, 074801 (2021)
• M. S. Chen, T. J. Zuehlsdorff, T. Morawietz, C. M. Isborn, T. E. Markland, “Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments.” J. Phys. Chem. Lett. 11, 7559-7568 (2020)
• S. Ghosal, M. S. Chen, J. Wagner, Z. Wang, S. Wall, “Molecular identification of polymers and anthropogenic particles extracted from oceanic water and fish stomach – A Raman micro-spectroscopy study.” Environmental Pollution. 223, 1113-1124 (2018)