Our group is a part of The Laboratory of AI for Electrochemistry (AI4EC lab).

Our research group specializes in multimodal spectroscopic computational simulations of complex interfaces. We focus on developing and applying advanced algorithms to investigate the intricate relationship between spectral features and microscopic structure.

We aim to provide deeper insights into the microscopic structures at complex interfaces, contributing to advancements in materials science, water science, electrochemistry and related fields.

Last update: 2025-04-18

News

Apr. 18, 2025

Our new work at Xiamen University is now published online in Journal of the American Chemical Society.

In this work, we propose a machine learning-based approach to predict dynamic $^7$Li NMR chemical shifts in LiFSI/DME electrolyte solutions. Additionally, we provide a comprehensive structural analysis to interpret the observed chemical shift behavior in experiments, particularly the abrupt changes in $^7$Li chemical shifts at high concentrations. Using advanced modeling techniques, we quantitatively establish the relationship between molecular structure and NMR spectrum, offering critical insights into solvation structure assignments. Our findings reveal the coexistence of two competing local solvation structures that shift in dominance as electrolyte concentration approaches the concentrated limit, leading to an anomalous reverse of $^7$Li NMR chemical shift in the experiment. This work provides a detailed molecular-level understanding of the intricate solvation structures probed by NMR spectroscopy, leading the way for enhanced electrolyte design.

Link to the paper: Journal of the American Chemical Society

Congratulations to Qi You!!!

Mar. 28, 2025

Our new work at Xiamen University is now published online in Nature Computational Science.

In this work, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pretraining and fine-tuning paradigm. To support the evaluation of nuclear magnetic resonance chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve competitive performance in both liquid-state and solid-state nuclear magnetic resonance datasets, demonstrating its robustness and practical utility in real-world scenarios. Our work helps to advance deep learning applications in analytical and structural chemistry.

Link to paper: Nature Computational Science 5, 292–300 (2025)

Congratulations to Fanjie!!!

Mar. 11, 2025

My last work at Temple University is now published online in Physical Review X. It is also featured news in Viewpoint in Physics Magazine “Shedding Light on Water Wires” by Prof. Davide Donadio and Prof. Giulia Galli.

In this work, by combining molecular-dynamics simulations and calculations of optical spectra based on many-body perturbation theory, we identified a clear spectroscopic fingerprint of molecular wires. Specifically, we showed that the “charge-transfer exciton” peak at ~8 eV in the UV absorption spectrum of water stems from collective excitations in hydrogen-bonded water wires. A charge-transfer exciton is a special type of electronic excitation, where an electron on one water molecule interacts with a positively charged “hole” localized on another molecule. The strength of the hydrogen bond directly influences the degree of charge transfer between molecules, with stronger hydrogen bonds facilitating greater charge separation and providing stronger optical absorption.

Link to paper: Phys. Rev. X 15, 011048 (2025)

The Viewpoint: Shedding Light on Water Wires, Physics 18, 54 (2025)

Sept. 25, 2024

My first work as last author at Xiamen University is now published online in J. Chem. Phys.. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, SFG) of solid-water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment-polarizability correlation function and the surface-specific velocity-velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for the complicated solid-water systems more rapidly and at a lower computational cost with the aid of ML.

Our paper is in the special collection: “Festschrift in honor of Yuen-Ron Shen”, and highlighted as JCP Editor’s Pick.

Link to paper: J. Chem. Phys., 2024, 161, 124702.

Mar. 14, 2024

My work at Xiamen University is now published online in Angew. Chem. Int. Ed.. In this work, we developed a machine learning spectroscopic methods, together with experimental collegues from Prof. Mischa Bonn’s department in Max Planck Institute for Polymer Research, examined water molecular structure at a freely suspended graphene/water interface, finding that the SFG response mainly arises from the topmost 1–2 water layers, with minimal contribution from the graphene itself. Graphene weakly interacts with the interfacial water by lowering the vibrational frequency of the dangling O−H group, with a very slight impact on the hydrogen-bonded O−H group.

Link to paper: Angew. Chem. Int. Ed., 2024, 63, e202319503.

Mar. 8, 2024

I gave an invited talk in the APS March Meeting at Minneapolis. It is about “Many-body effects in the X-ray absorption spectra of liquid water”.

Jan. 8, 2024

I official joined Xiamen University as a junior professor.