Papers & Code

Papers & preprints

1. Nature Catal.

   Desolvated cations promote CO2 electroreduction through partial covalent interactions

   Yu Shan, Maximilian Jaugstetter, Julian Feijóo, and 14 more authors

   Nature Catalysis, Sep 2026

   Yu Shan, Maximilian Jaugstetter, Julian Feijóo and Yani Guan contributed equally.

   Abs DOI PDF

   Cations play a critical role in electrochemical CO2 reduction, yet the mechanisms underlying their influence remain incompletely understood owing to limited knowledge of their solvation structures and interfacial interactions under operating conditions. Here we combine surface-sensitive total electron yield X-ray absorption spectroscopy, surface-enhanced infrared absorption spectroscopy and density functional theory calculations to determine the solvation environment of caesium ions near a ligand-modified silver nanocatalyst under CO2-reducing conditions. We find that desolvated Cs+ ions, confined between a detached ligand layer and the silver surface, enable CO2 activation at potentials as high as 0.4 V versus reversible hydrogen electrode—reducing the overpotential for CO2-to-CO conversion by 250 mV compared with a bare Ag film. Our results further reveal partial covalent character in Cs–intermediate interactions, which directly accounts for the enhanced catalytic activity. These findings provide multimodal spectroscopic evidence that cations can modulate reaction kinetics beyond purely electrostatic effects.

2. Ag Amination

   

   Electrochemical Amination of Acetone using Ag as Cathode: Mechanism and Role of Pb Impurities for Hydrogen Transfer

   Yani Guan, Justus Kümper, Angelina Cuomo, and 6 more authors

   Catalysis Science & Technology, Sep 2026

   DOI
3. MU-PFF MD

   

   High-throughput Molecular Dynamics Simulation on an AI-powered Platform

   Dengpan Dong, Yani Guan, Shuang Luo, and 5 more authors

   ChemRxiv preprint, Sep 2026

   MU-PFF: a machine-learned many-body polarizable force field plus automated system building and trajectory analysis, taking an electrolyte formulation from SMILES to predicted properties without manual parameterization.

   Abs DOI

   We present an end-to-end, AI-powered platform for high-throughput molecular dynamics (MD) simulation of liquid electrolytes and complex formulations. At its core is MU-PFF, a many-body polarizable force field whose functional form follows the well-validated exp-6 polarizable model, but whose parameters are produced entirely by an automated machine-learning pipeline rather than by hand. We assemble a quantum-chemistry dataset of roughly one million molecules and molecular complexes and train a single message-passing neural network to predict the complete parameter set – partial charges, atomic polarizabilities, bonded (bond, bend, torsion) terms, and the repulsion-dispersion parameters – directly from molecular structure. Parameterization that traditionally takes weeks of expert effort per chemistry is reduced to a single forward pass, yielding a universal polarizable force field that transfers across solvents, salts and additives. Coupled to automated system building and trajectory analysis, the platform reproduces experimental electrolyte properties with high fidelity: density error below 0.02 g/cm3, lithium-electrolyte ionic conductivity of approximately 1.5 mS/cm in quantitative agreement with measurement, and salt/additive solubility predicted to within 10%. We further demonstrate extended capabilities, including additive solubility by thermodynamic integration and electric-double-layer simulation under applied potential.

4. OCSR VLM

   

   Real Data Closes Synthetic-to-Real Gap in Optical Chemical Structure Recognition

   Yani Guan, Dengpan Dong, Zi Wei, and 4 more authors

   arXiv preprint arXiv:2608.09100, Sep 2026

   * Equal contribution. Vision-language models for image-to-SMILES: real labeled depictions, not more synthetic data, are what close the synthetic-to-real gap.

   Abs DOI arXiv

   Millions of chemical structures appear in patents and papers only as drawings. Optical chemical structure recognition (OCSR) appears nearly solved on synthetic images, but remains difficult on real documents: the starting recognizer Qwen2.5-VL-7B reaches over 91% accuracy on synthetic images and below 16% on the ACS, CLEF-IP and USPTO benchmarks. We fine-tuned 21 recognizers on mixtures of synthetically rendered structures and labeled real depictions from patents, journal figures and hand-drawn collections, varying the VLM base, the fraction of real training data, and the vision-tower adaptation strategy. Adding labeled real training images makes the largest difference: for Qwen2.5-VL, ACS exact match rises from 0.15 with no real data to 0.37 at 9.5% real data and 0.46 at 50.2%. A vision LoRA does not help Qwen at all (+0.00, paired p = 1.00), substantially helps InternVL3-8B (+22.8 to +34.6 pt) and modestly helps GLM-4.1V-9B, so its value depends on the base model. The strongest model reaches 0.96 exact match on clean renders and 0.49, 0.65, 0.84 and 0.76 on ACS, CLEF-IP, UOB and USPTO. Differences between bases are largest without real data (0.21) and shrink at 70% real data (0.06), while their ranking also changes – base model and real-data mixture must therefore be selected together.

5. VERDICT

   VERDICT: Agreement Beats Pixel-Space Verification in Real-Document OCSR

   Yani Guan, Dengpan Dong, Shuang Luo, and 6 more authors

   arXiv preprint arXiv:2608.22183, Sep 2026

   Label-free verification for image-to-SMILES at scale: agreement among four architecturally distinct recognizers reaches AUROC 0.916, while re-rendering a prediction and comparing pixels barely beats chance at 0.547.

   Abs DOI arXiv

   Optical Chemical Structure Recognition (OCSR) converts 2D molecular depictions in the published literature into SMILES, and is increasingly important for constructing large-scale chemical training datasets. Automation at that scale requires identifying unreliable predictions in the absence of ground truth. Three families of label-free signals were compared on 263 ACS journal depictions with verified ground truth: model confidence, re-rendering similarity, and agreement among recognizers. Pixel-space re-rendering performed little better than chance (AUROC 0.547, 95% CI [0.465,0.629]), and an oracle-tuned threshold on it reduced correct labels per image from 0.745 to 0.205. Agreement among four architecturally distinct recognizers instead reached an AUROC of 0.916 ([0.880,0.952]). The two-of-four rule accepted 81.7% of images at 88.8% precision, the three-of-four rule 52.1% at 98.5%. The same pattern held on CLEF-IP, UOB, and USPTO. This distinction is obscured on synthetic benchmarks, where re-rendered predictions naturally resemble their inputs. A substance filter removed 2,193 false agreements on wildcards and R-group fragments, after which the three-of-four rule rejected all 68 generic depictions. VERDICT was then applied to PMC Open Access, producing 6,146 structure labels for 4,833 molecules; chemist adjudication of 400 released labels in two independent samples yielded precisions of 0.995 for the three-of-four tier and 0.958 for the two-of-four tier. VERDICT therefore enables validated labels for multimodal molecular databases linking structure images, machine-readable representations, and source-publication information. In SES AI’s Molecular Universe platform, VERDICT further serves as an image-based interface for searching and retrieving molecular records.

6. EDLC

   

   Probing the Electric Double-Layer Capacitance to Understand the Reaction Environment in Conditions of Electrochemical Amination of Acetone

   Yani Guan, Justus Kümper, Simran Kumari, and 6 more authors

   ACS Applied Materials & Interfaces, Sep 2025

   DOI
7. DFT-KMC

   First-principles-based kinetic monte carlo model of hydrogen evolution reaction under realistic conditions: solvent, hydrogen coverage and electric field effects

   Yuhong Luo, Yani Guan, Guihua Liu, and 3 more authors

   ACS Catalysis, Sep 2024

   DOI

8. 

   Metallic Impurities in Electrolysis: Catalytic Effect of Pb Traces in Reductive Amination and Acetone Reduction

   Justus Kümper, Sonja D Mürtz, Yani Guan, and 5 more authors

   Angewandte Chemie International Edition, Sep 2024

   DOI
9. Metallische Verunreinigungen in der Elektrolyse: Katalytische Wirkung von Pb-Spuren in der reduktiven Aminierung und Acetonreduktion

   Justus Kümper, Sonja D Mürtz, Yani Guan, and 5 more authors

   Angewandte Chemie, Sep 2024

   German edition of the Angewandte Chemie International Edition article of the same title.

   DOI
10. Cu Dissolution

    

    Origin of copper dissolution under electrocatalytic reduction conditions involving amines

    Yani Guan, Justus Kümper, Sonja D Mürtz, and 4 more authors

    Chemical Science, Sep 2024

    DOI
11. ML for Catalysis

    

    Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives

    Yani Guan, Donovan Chaffart, Guihua Liu, and 5 more authors

    Chemical Engineering Science, Sep 2022

    DOI
12. Li-S Battery

    Ordered macroporous V-doped ZnO framework impregnated with microporous carbon nanocages as multifunctional sulfur reservoir in lithium-sulfur batteries

    Ximeng Zhao, Yani Guan, Xiaohang Du, and 3 more authors

    Chemical Engineering Journal, Sep 2022

    DOI
13. A conductive and ordered macroporous structure design of titanium oxide-based catalytic cathode for lithium–sulfur batteries

    Fan Liu, Yani Guan, Xiaohang Du, and 3 more authors

    Nanotechnology, Sep 2021

    DOI
14. Zn-Air Battery

    A macroporous titanium oxynitride-supported bifunctional oxygen electrocatalyst for zinc–air batteries

    Weixing Niu, Yani Guan, Yuhong Luo, and 2 more authors

    Catalysis Science & Technology, Sep 2021

    DOI
15. CO2 DFT

    

    Insights on the catalytic active site for CO2 reduction on copper-based catalyst: a DFT study

    Yani Guan, Wei Suo, Zisheng Zhang, and 3 more authors

    Molecular Catalysis, Sep 2021

    DOI
16. Nanoscale melamine-based porous organic frameworks as host material for efficient polysulfides chemisorption in lithium–sulfur batteries

    Miao Sun, Haifeng Ji, Yani Guan, and 5 more authors

    Nanotechnology, Sep 2020

    DOI
17. Bifunc. Catalyst

    

    Surface-engineered cobalt nitride composite as efficient bifunctional oxygen electrocatalyst

    Yani Guan, Guihua Liu, Jingde Li, and 2 more authors

    Nanotechnology, Sep 2019

    DOI

Conference

1. Origin of Copper Dissolution in Electrocatalytic Reduction Conditions Involving Amines

   Yani Guan and Philippe Sautet

   In Electrochemical Society Meeting Abstracts prime2024, 2024

   DOI

Thesis

1. Modeling the Electrocatalytic Interface in the Conditions of Electrochemical Amination of Acetone with Methylamine as Nitrogen Source

   Yani Guan

   University of California, Los Angeles, 2024

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