Research
Atomistic Simulations and Artificial Intelligence for Materials Design
Our approach —
atomistic simulations × artificial intelligence
A closed loop between physics and data.
Our research starts from atomistic multi-scale simulations. We use density functional theory (DFT) and molecular dynamics to resolve how materials work at the level of electrons and atoms. Adsorption, charge transfer, diffusion, and bond breaking are computed on realistic surfaces, and grand-canonical DFT (GC-DFT) with microkinetic modeling carries them to reaction rates at working potentials.
Artificial intelligence extends what physics alone cannot reach. Machine-learned interatomic potentials bring near-DFT accuracy to large-scale dynamics. Graph neural networks and descriptor models predict properties across wide chemical spaces. Large language models read the literature, gathering experimental data and synthesis conditions at scale. Active learning and Bayesian optimization steer expensive calculations toward the most informative candidates.
Together they form a closed design loop: simulate, learn, propose, and validate. This loop turns millions of candidate compositions into a handful of the most promising materials, before a single experiment is run.
Selected papers
- Sustainable Low-Valent Iridium Active Sites in Rhenium Doped Perovskite Iridates for Efficient Acidic Oxygen Evolution Reaction via Hybridized Reaction PathwaysAngewandte Chemie-International Edition
- Active Learning-Driven Multi-Objective Design of High-Entropy Alloy Catalysts for Saline Water ElectrolysisChemical Engineering Journal, 2026
- Multiscale Modeling of Agglomerated Ceria Nanoparticles: Interface Stability and Oxygen Vacancy FormationFrontiers in Chemistry, 2019
Research map
Where our research connects
Explore the ontology of our papers and ask it in plain language
Research · 01
Electrocatalysis for green hydrogen
Catalysts and membranes for water, seawater, and beyond.
Green hydrogen is only as good as the electrocatalyst it runs on. We design HER and OER catalysts from first principles, from high-entropy alloys and single-atom catalysts to layered double hydroxides and metal/metal-oxide hybrid catalysts. Active learning lets us navigate composition spaces far too large for brute-force calculation, optimizing activity, selectivity, and stability at the same time.
Real electrolytes are not ideal. In saline and seawater electrolysis, chlorine evolution competes with oxygen evolution, and durability is decided under operating potentials. We treat these constraints explicitly with grand-canonical DFT and Pourbaix analysis under realistic pH and potential.
We also look beyond the catalyst. Molecular dynamics and machine-learned force fields of ion-solvating polymer membranes show how ions move through alkaline electrolyzers. This links membrane chemistry to cell performance.
Selected papers
- Sustainable Low-Valent Iridium Active Sites in Rhenium Doped Perovskite Iridates for Efficient Acidic Oxygen Evolution Reaction via Hybridized Reaction PathwaysAngewandte Chemie-International Edition
- Upcycled Polymer Waste-Derived N-Doped Carbon Supports Enabling Durable PtCo Catalysts for PEM Fuel CellsChemical Engineering Journal, 2026
- Breaking the Water Dissociation–Hydrogen Adsorption Trade-off through W Single-Atom Engineering for Alkaline Hydrogen EvolutionApplied Catalysis B: Environment and Energy, 2026

Research · 02
Sustainable chemical conversion
Electrifying chemistry, from biomass and nitrate to value-added products.
Electrochemistry can turn waste streams into resources. We compute the reaction mechanisms behind these conversions: which bonds break, on which sites, and why.
For biomass valorization, we study how lignin-derived molecules adsorb and cleave on catalyst surfaces, aiming at selective C–C and C–O bond scission driven by light and electricity. For the nitrogen cycle, we screen alloy catalysts that reduce nitrate in wastewater to ammonia. For a carbon-neutral chemical industry, we design catalysts for CO2 capture and utilization.
Every thread follows the same pattern. Mechanism first, then descriptors, then machine-guided search.
Selected papers
- Diverting the Use of Reduced TiO2 from a Carrier of Electron–Hole Pairs to a Reservoir of Brönsted–Lewis Acidic Pairs for Sustainable, Non-Optical H2O2 HomolysisJournal of Materials Chemistry A, 2026
- Roles of Alkaline-Earth Metals and Electron Density of Al3+ in Octanol Dehydration for Linear Alpha-Olefin ProductionJournal of Materials Chemistry A, 2026
- Durable and Efficient Solar CO2-to-CO Conversion Enabled by Single-Atom Ag on Zn3(PO4)2 Cathode Coupled with a GaAs/PtSA@NiOOH PhotoanodeAdvanced Energy Materials, 2025

Research · 03
Advanced energy storage
Materials and interfaces for safer, longer-lived batteries.
Battery performance is decided by its materials. We design electrode materials for higher capacity and faster charging, across chemistries from lithium to aqueous systems.
Battery degradation is an interface problem. We study electrode and electrolyte interfaces at the atomic scale, from dendrite suppression on metal anodes to deposition behavior in aqueous cells. These insights guide materials and operating strategies for safer and longer-lived batteries.
Selected papers
- Redox-Active Ligand-Stabilized Lithium Iron Phosphate Nanoparticles for High-Performance Lithium-Ion Battery Cathode with High Capacities and Long-Term StabilityNano-Micro Letters, 2027
- Single-Atom Active Sites in Polymeric Phthalocyanine Hybrid Electrocatalysis for High-Efficiency Lithium–Carbon Dioxide BatteriesAdvanced Functional Materials
- Electric-Field-Driven Bilayer Interphase from Oxygenated Nanodiamond-Carbon Nanoparticles for Dendrite-Free Lithium Metal BatteriesAdvanced Energy Materials, 2026

Research · 04
Semiconductor materials & processes
Atomic-scale materials and chemistry for the fab.
Semiconductor manufacturing is materials chemistry pushed to its limits. Across deposition, etching, polishing, and implantation, interactions at the atomic scale decide device performance and yield.
We use DFT and machine learning to discover and design the chemicals used in semiconductor processes. Adsorption and reaction energetics on metal, oxide, and nitride surfaces tell us how a molecule behaves at a critical interface. Property-prediction models trained on large molecular databases let us search chemical space far beyond known formulations.
We also study the materials themselves. From interconnect metals and their oxides to defects formed under processing conditions, we connect atomic-scale structure to the properties that devices depend on.
Selected papers
- Molybdenum Ion-Induced Adhesion of Colloidal Silica to Polyvinyl Acetal Brushes in Post-Chemical Mechanical Planarization Applications: Experiments and Multiscale SimulationsACS Applied Materials & Interfaces, 2026
- Ion Irradiation Induced Surface Composition Modulation in Equiatomic Binary AlloysApplied Surface Science, 2021
- Indirect-to-Direct Band Gap Transition of Si Nanosheets: Effect of Biaxial StrainJournal of Physical Chemistry C, 2018
