About

Lee-Ping Wang is a professor specializing in physical chemistry at the University of California, Davis. He completed his B.A. in Physics at U.C. Berkeley from 2002 to 2006, followed by a Ph.D. in Physical Chemistry at MIT from 2006 to 2011. After his doctoral studies, he conducted postdoctoral research in physical chemistry at Stanford University from 2011 to 2015. He joined U.C. Davis as an assistant professor in physical chemistry in 2015 and was promoted to associate professor in 2020. His group focuses on theoretical chemistry, with research interests that include bio-molecular simulations, quantum chemistry calculations, and the development of computational methods to study chemical reactions and protein conformational changes. Professor Wang's work involves the use of ab initio nanoreactors to develop mechanistic knowledge of chemical processes, such as C-C bond formation and enzyme catalytic reactions, as well as the simulation of molecular dynamics to improve mass spectrometry libraries. He also contributes to the development of software tools for exploring potential energy surfaces, supporting advances in theoretical and computational chemistry.

Research topics

• Computer Science
• Chemistry
• Physics
• Quantum mechanics
• Physical chemistry
• Statistical physics
• Classical mechanics
• Computational chemistry
• Mathematics
• Computational science
• Geometry
• Materials science
• Chemical physics

Selected publications

• CCDC 2286391: Experimental Crystal Structure Determination

  The Cambridge Structural Database · 2026-04-23 · 1 citations

  datasetOpen access

  An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

  PublisherDOI

• leeping/supercool-analysis: Version 1.0 Release

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-09

  otherOpen access1st authorCorresponding

  Release to accompany publication of the paper: Lee-Ping Wang, Margaret L. Berrens and Davide Donadio, "Direct observation of liquid–liquid phase coexistence in deeply supercooled water using an accurate polarizable multipole model", PNAS 2025 (in press). For questions, please contact leeping@ucdavis.edu .

  PublisherDOI

• Direct observation of liquid–liquid phase coexistence in deeply supercooled water using an accurate polarizable multipole model

  Proceedings of the National Academy of Sciences · 2026-01-23

  articleOpen access1st authorCorresponding

  Liquid water can be supercooled up to about 50 K below the melting point before undergoing homogeneous ice nucleation. Based on experimental thermodynamic observations and computer simulations, it was hypothesized that below this temperature and at pressures of several kbar, water undergoes a liquid-liquid phase transition (LLPT) and the transition line ends at a second critical point. However, challenges in experiments and simulations at such deep cooling leave doubts about the nature of the LLPT and the existence of the critical point. Here, we use molecular dynamics simulations with a highly accurate and computationally efficient polarizable water model to establish the character of the LLPT and identify the location of the second critical point. Our microsecond-long simulations provide direct evidence of a well-defined moving interface between low-density and high-density water at conditions near the phase boundary. This provides decisive proof of a first-order transition between two liquid phases with distinct free energy basins separated by a barrier, taking a major step toward resolving this long-standing debate. These results offer new perspectives on supercooled water under pressure simulated with an accurate and realistic model suitable for studies of water in confined geological and biological environments.

  PublisherOA PDFDOI

• CCDC 2286392: Experimental Crystal Structure Determination

  The Cambridge Structural Database · 2026-04-23 · 1 citations

  datasetOpen access

  An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

  PublisherDOI

• leeping/supercool-analysis: Version 1.0 Release

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-09

  otherOpen access1st authorCorresponding

  Release to accompany publication of the paper: Lee-Ping Wang, Margaret L. Berrens and Davide Donadio, "Direct observation of liquid–liquid phase coexistence in deeply supercooled water using an accurate polarizable multipole model", PNAS 2025 (in press). For questions, please contact leeping@ucdavis.edu .

  PublisherDOI

• shehan807/mdtraj: Molecular Aggregation Analysis

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-16

  otherOpen access

  The basic use case requires a user-defined criteria dictionary of distance and/or angle cutoffs, from which edges between molecular nodes are formed. The md.compute_aggregates() outputs the edge list, which can be quickly processed by networkx in md.compute_aggregate_metrics() to obtain aggregate sizes and graph diameters. 1. Two new functions in mdtraj/geometry: md.compute_aggregates(): Find connected molecules based on distance/angle criteria md.compute_aggregate_metrics(): Compute aggregate sizes and graph diameters 2. Three pytests in tests/test_aggregate.py 3. Two examples in examples/aggregation_analysis.ipynb: Ice Crystal Structures, where HOH molecules form a single aggregate (for validation) Ionic Liquids & Ionic Liquid Crystals, where increased alkyl chain length leads to increased aggregate size Dependencies: md.compute_aggregate_metrics() requires the optional NetworkX dependency the examples require GenIce2 and py3Dmol

  PublisherDOI

• Reparameterizing a Lipid Force Field Using Small-Angle X-ray Scattering to Improve Predictions of Multicomponent Membranes under Organic Solvent Stress

  The Journal of Physical Chemistry B · 2026-03-10 · 1 citations

  article

  Understanding and predicting properties of lipid-bilayer membranes are essential to elucidating organismal physiology and pathophysiology. Therefore, substantial efforts have been undertaken to derive accurate molecular-mechanics force fields (FF) to allow simulation of their properties; however, much of these past efforts focused on tuning force fields to accurately reproduce the properties of model single-component membranes and not multicomponent or stressed membranes. Here, we tuned the CHARMM36 FF for a simple 2-component model of a Gram-positive bacterium. This updated force field is parametrized against a single condensed-phase property, the small-angle X-ray scattering (SAXS) intensities of the 2-component lipid system, using a version of ForceBalance implemented previously (named ForceBalance-SAS) with SAXS and small-angle neutron scattering (SANS) intensities as optimization targets. After tuning, we observe improved agreement with the experimental SAXS of the membrane under 1-butanol and tetrahydrofuran solvent stresses, with a factor of 10.0 and 7.5 reduction, respectively, in χ2, the measure of the discrepancy between experimental and computed SAXS intensities. Furthermore, this reparametrized force field yielded improved agreement between experimental and simulated membrane thicknesses for the pure lipid systems. However, we note limitations in transferability and diagnose the source of such limitations, particularly the need for SANS in addition to SAXS.

  PublisherDOI

• shehan807/mdtraj: Molecular Aggregation Analysis

  Open MIND · 2026-05-16

  otherOpen access

  The basic use case requires a user-defined criteria dictionary of distance and/or angle cutoffs, from which edges between molecular nodes are formed. The md.compute_aggregates() outputs the edge list, which can be quickly processed by networkx in md.compute_aggregate_metrics() to obtain aggregate sizes and graph diameters. 1. Two new functions in mdtraj/geometry: md.compute_aggregates(): Find connected molecules based on distance/angle criteria md.compute_aggregate_metrics(): Compute aggregate sizes and graph diameters 2. Three pytests in tests/test_aggregate.py 3. Two examples in examples/aggregation_analysis.ipynb: Ice Crystal Structures, where HOH molecules form a single aggregate (for validation) Ionic Liquids & Ionic Liquid Crystals, where increased alkyl chain length leads to increased aggregate size Dependencies: md.compute_aggregate_metrics() requires the optional NetworkX dependency the examples require GenIce2 and py3Dmol

  PublisherDOI

• openmm/openmm: OpenMM 8.5.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-20

  otherOpen access

  This release adds the following new features. The energy minimizer has been rewritten to run entirely on the GPU. This can make energy minimization dramatically faster. PythonForce is a new class that can be used to compute forces and energies with Python code. The most important application is machine learning potentials; it allows us to support a much larger range of ML potentials than was possible before. It can also be used for arbitrary other forces, but it has relatively high overhead. If a force can be implemented with the existing custom forces, that is usually a better choice. LCPO has been added as another option for computing the surface energy term in implicit solvent simulations. It is more accurate than the default ACE approximation, but also slower. We have made internal changes needed to support the next generation OpenFF force fields. There is a new API to query what devices are available before starting a simulation. When loading Gromacs files, one additional type of vsite is supported.

  PublisherDOI

• Breaking C–C (sp ³ –sp ³ ) σ bonds in 1,4-diazabicyclo[2.2.2]octane (DABCO) by oxidation with antimony pentachloride

  Organic Chemistry Frontiers · 2026-01-01

  article

  The C–C σ bond cleavage of 1,4-diazabicyclooctane[2.2.2] (DABCO) has been facilitated using antimony pentachloride, characterized by EPR and SC-XRD, and explained using in silico methods.

  PublisherDOI

Frequent coauthors

• John D. Chodera

  42 shared

• David L. Mobley

  University of California, Irvine

  41 shared

• Simon Boothroyd

  35 shared

• Michael K. Gilson

  34 shared

• Michael R. Shirts

  University of Colorado Boulder

  32 shared

• Hyesu Jang

  University of California, Davis

  30 shared

• Christopher I. Bayly

  27 shared

• Todd J. Martı́nez

  Stanford University

  26 shared

Labs

• L-P. Wang Theoretical Chemistry GroupPI

Education

• Ph.D., Physical Chemistry, Chemistry

  Massachusetts Institute of Technology

  2011

• B.A., Physics

  University of California Berkeley

  2006

Awards & honors

• ACS-PRF Doctoral New Investigator (2017)
• Promoted to Associate Professor (2020)