Jiawei Zhang

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Nature Communications · 2020 · 286 citations

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characteriz…

MolGpka: A Web Server for Small Molecule p<i>K</i>_a Prediction Using a Graph-Convolutional Neural Network

Journal of Chemical Information and Modeling · 2021 · 242 citations

during the ligand design process. The MolGpKa server is freely available to researchers and can be accessed at https://xundrug.cn/molgpka.

On the design space between molecular mechanics and machine learning force fields

Applied Physics Reviews · 2025-04-02 · 25 citations

A force field as accurate as quantum mechanics (QMs) and as fast as molecular mechanics (MMs), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor in this direction, where differentiable neural functions are parametrized to fit ab initio energies and f…

Transition State Searching Accelerated by Neural Network Potential

Journal of Chemical Information and Modeling · 2025-02-20 · 9 citations

Understanding transition states is pivotal in the design of efficient chemical processes and catalysts. However, identifying transition states is challenging due to the resource-intensive and iterative nature of current computational methods. This study integrates neural network potentials with physical models to enhance the transition state prediction. Different neural network potentials and transition states locating algorithms are benchmarked. By combining NequIP with the energy-weighted Clim…

Exploring Optimized Organic Fluorophore Search through Experimental Data-Driven Adaptive β-VAE

JACS Au · 2025-06-30 · 7 citations

Designing organic fluorescent molecules with tailored optical properties has been a long-standing challenge. Recently, statistical models have opened new avenues for tackling this problem. Inverse design has attracted considerable attention in organic materials science; however, most existing approaches focus on arbitrary design or theoretical properties. Here, we introduce a strategy that enables the direct optimization of specific experimental properties during the inverse design process. Our…