司達博 John Paul Stoppelman助理教授

B.S. in Chemistry, Loras College
Ph.D. in Chemistry, Georgia Institute of Technology
Advisor: Prof. Jesse McDaniel
Postdoctoral Scholar: Academia Sinica
Advisor: Dr. Chao-Ping Hsu

The research in our group is focused on building physics-based and machine learning models for realistic simulation of condensed phase systems. Currently we are interested in the following two projects:

1. Charge transport in condensed phases The transport of charges in condensed phases is ubiquitous in chemistry and biochemistry. The mechanistic role solvation plays on the charge transfer process is hard to model theoretically. Solvent effects can be particularly pronounced when in highly polar charged environments, such as ionic liquids or some enzyme systems. We are developing new reactive molecular dynamics codes that combine neural network potentials with realistic ab initio force fields in order to model such reactions in the condensed phases. The specific systems we are interested in range from excess proton solvation and transport to electron and hole transport in organic semiconductors. These simulations will provide us with further understanding of the effect complex environments play on charge transport processes.

2. Negative thermal expansion (NTE) materials The vast majority of materials undergo positive thermal expansion with increasing temperature. There are a class of materials that experience isotropic NTE over a pronounced temperature range, such as ScF3 and CaZrF6. We are interested in applying neural network potentials to model the NTE process and further understand the physics behind such materials. These materials may allow for the development of new thermally stable materials that allow for reduced stress over elevated temperature ranges.

Google Scholar:
https://scholar.google.com/citations?user=m_fbGCsAAAAJ&hl=en

Selected Publications :
1. Verma A., Stoppelman J.P., McDaniel J.G. “Tuning Water Networks via Ionic Liquid/Water Mixtures”, Int. J. Mol. Sci., 2020, 21(2), 403 
2. Stoppelman J.P., McDaniel J.G. “Proton Transport in [BMIM+][BF4–]/Water Mixtures Near the Percolation Threshold”, J. Phys. Chem. B, 2020, 124, 28, 5957
3. Stoppelman J.P., McDaniel J.G. “Physics-based, Neural Network Force Fields for Reactive Molecular Dynamics: Investigation of Carbene Formation from [EMIM+][OAc−]”, J. Chem. Phys., 2021, 155, 104112
4. Stoppelman J.P., Ng T.T., Nerenberg P.S., Wang L.P. “Development and Validation of AMBER-FB15-Compatible Force Field Parameters for Phosphorylated Amino Acids”, J. Phys. Chem. B, 2021, 125, 43, 11927
5. Stoppelman J.P., McDaniel J.G., Cicerone M.T. “Excitations Follow (or Lead?) Thermodynamic Scaling in Propylene Carbonate”, J. Chem. Phys., 2022, 157, 204506 
6. Stoppelman J.P., McDaniel J.G. “N-Heterocyclic Carbene Formation in the Ionic Liquid [EMIM+][OAc–]: Elucidating Solvation Effects with Reactive Molecular Dynamics Simulations”, J. Phys. Chem. B, 2023 127, 23, 5317–5333
7. Stoppelman J.P., Wilkinson A.P., McDaniel J.G. “Equation of State Predictions for ScF3 and CaZrF6 with Neural Network-Driven Molecular Dynamics”, J. Chem. Phys., 2023 159, 084707
8. Cicerone M.T., Dixon J. Z., Stoppelman J.P., Badilla-Nunez K., McDaniel J.G. “A Case for Dynamic Percolation Underlying Mechanistic Crossovers in the Relaxation of Liquids”, J. Phys. Chem. B 2025 129, 26, 6620-6631