MACE Research
Modelling–AI–Computation–Energy
Research Interests
At MACE Lab, we aim to accelerate the discovery and understanding of energy-related materials by integrating data-driven methods with AI-/ML-enhanced multiscale simulations. Our core research focuses on elucidating the dynamic behaviors of heterogeneous interfaces, surface reactions, and nanomaterial transformations that underpin key processes in catalysis and energy storage.
A central challenge in this field lies in capturing energy and mass transport, as well as material evolution at complex solid–liquid or solid–gas interfaces under operando conditions. These interfacial dynamics are difficult to monitor experimentally and often span wide temporal and spatial scales. To overcome this, we are developing advanced theoretical frameworks that couple atomic-level simulations with mesoscale models, enabling us to probe interfacial processes across multiple length and time scales.
Our efforts include modeling environment-induced reaction networks, nanostructure reconstruction, and interface-specific reactivity. By combining AI-powered generative models with physics-informed simulations, we seek not only to interpret known phenomena but also to guide the rational design of novel materials for applications in electrocatalysis, heterogeneous catalysis, and energy conversion and storage systems.
Ultimately, our mission is to bridge the gap between atomic-scale understanding and real-world performance, enabling predictive and scalable solutions for a more sustainable energy future.
Join us in pushing the boundaries of theory-driven materials innovation—where atoms, algorithms, and applications converge!
Research Highlight
1. Developed multiscale kinetic Monte Carlo models to simulate the dynamic structural reconstruction of metal-based nanoparticles, enabling insights into long-timescale and large-scale catalytic behavior (References: (1) JACS Au, 2024, 4, 1892–1900; (2) J Am Chem Soc, 2025, 147, 15796–15805; (3) J Phys Chem C, 2021, 125, 19756; (4) Adv Theor Simul, 2019, 2, 1800127)
2. Decoded the complex effects of electrocatalytic environments (such as applied potential, pH, electrolyte ions, and hydrogen-bond networks) on activity and selectivity in CO₂/CO reduction and other clean-energy conversion reactions (References: (1) Nat Nanotechnol, 2024, 19, 311–318; (2) Nat Catal, 2023, 6, 310–318; (3) Nat Energy, 2023, 8, 179–190; (4) ACS Energy Lett, 2023, 8, 4096–4103)
3. Data-driven and AI/ML-powered discovery and design of efficient catalytic nanomaterials accelerate breakthroughs in sustainable energy conversion (References: (1) Nat Catal, 2025, 8, 239–247; (2) J Am Chem Soc, 2024, 146, 14267–14277; (3) Nano Research, 2024, 17, 2360–2367; (4) Matter, 2024, 7, 4099–41131; (5) https://arxiv.org/abs/2404.12445)
