Intelligent Surface Science: Revolutionizing Nanostructure Discovery with AI-guided First-Principles Thermodynamics

主講人 : Prof. Aloysius Soon

Recent advances in experimental and theoretical methods have greatly enhanced our ability to characterize the physical, chemical, and crystallographic properties of well-defined surfaces and interfaces. In particular, the study of low-dimensional nanomaterials on  l substrates has attracted considerable interest, driven by progress in surface spectroscopy and microscopy. Computational simulations have played a complementary role, providing critical insights through data-driven and theory-guided atomistic modeling. However, reconciling experimental and theoretical de ions of supported nanostructures remains a major challenge. This keynote discusses how modern computational approaches and artificial intelligence (AI) are helping to bridge this gap in theoretical surface science. We revisit the application of ab initio atomistic thermodynamics–commonly used in computational catalysis–to predict stable catalyst surfaces under realistic technical conditions. By incorporating AI-driven global optimization, this method addresses the longstanding pressure and temperature divide between ultra-high vacuum experiments and industrial reactor environments. Using a  stable O/Cu surface oxide as a case study, we illustrate how AI-enhanced first-principles simulations reveal novel nanostructures, thereby broadening thematerials search space and reducing dependence on experimental intuition. Looking forward, we highlight the potential of integrating these approaches and extending thermodynamic analysis to aqueous electrochemical systems. This integrated strategy paves the way for intelligent, data-driven surface structure determination and the discovery of industrially relevant interfacial configurations.

主講人簡介：

Aloysius Soon教授是英國物理學(xué)會會士（FInstP）、英國皇家化學(xué)會會士（FRSC）及英國特許科學(xué)家（CSci）。他先后于新加坡國立大學(xué)、奧克蘭大學(xué)及悉尼大學(xué)獲化學(xué)學(xué)士、碩士及物理學(xué)博士學(xué)位，曾任德國馬克斯·普朗克學(xué)會弗里茨·哈伯研究所洪堡研究員。Soon教授在計算材料科學(xué)領(lǐng)域具有廣泛影響力，長期致力于發(fā)展第一性原理計算與機器學(xué)習(xí)方法，深刻揭示材料表/界面的物理化學(xué)機制。他在二維材料、功能氧化物、催化與能源材料等前沿方向做出了系統(tǒng)性貢獻，在Nature Materials、Nature Catalysis、Advanced Materials等頂級期刊發(fā)表論文140余篇，引用超6500次，H指數(shù)39。他的研究在表面科學(xué)、缺陷熱力學(xué)及機器學(xué)習(xí)輔助材料發(fā)現(xiàn)等方面具有突出影響，并與韓國三星、LG、現(xiàn)代汽車等工業(yè)界及國際頂尖科研機構(gòu)保持廣泛合作。