{"id":347,"date":"2024-09-25T13:36:45","date_gmt":"2024-09-25T18:36:45","guid":{"rendered":"https:\/\/wpdev.acsu.buffalo.edu\/jiayu-peng-lab\/?page_id=347"},"modified":"2026-09-29T05:51:44","modified_gmt":"2026-09-29T05:51:44","slug":"research","status":"publish","type":"page","link":"https:\/\/ubwp.buffalo.edu\/jiayu-peng-lab\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\n

Discovering and understanding materials for energy and chemical technologies increasingly requires navigating enormous spaces of compositions, structures, interfaces, and operating conditions. Our group develops physics-grounded AI and modeling approaches to make this search more efficient while retaining the physical and chemical understanding needed for reliable materials design.<\/strong><\/p>\n\n\n\n

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Human- and data-driven materials discovery.<\/strong> Combining physical insight, simulation, experiments, and machine learning to navigate vast chemical and materials spaces.<\/figcaption><\/figure>\n\n\n\n

We work at the intersection of materials physics, surface chemistry, atomistic simulation, and AI<\/strong>. Our research combines first-principles calculations and statistical-mechanical modeling with machine learning, such as graph neural networks, machine-learning interatomic potentials, generative models, active learning, and emerging agentic AI<\/strong>. A central goal is to move beyond black-box property prediction toward AI approaches that can reveal physical principles, quantify uncertainty, guide simulations and experiments, and ultimately support scientific decision-making.<\/p>\n\n\n\n

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Physics-driven machine learning for materials design.<\/strong> Integrating high-throughput atomistic modeling, AI, and characterization to understand and optimize materials and interfaces.<\/figcaption><\/figure>\n\n\n\n

Our research spans several interconnected directions. We develop AI-enabled atomistic models of electronic structure and chemical reactivity<\/strong> to understand how local composition, bonding, and atomic environments govern catalytic and materials properties. We study dynamic surfaces and interfaces, such as stability, reconstruction, and the evolution of active-site ensembles under realistic operating conditions<\/strong>, where the relevant material may differ substantially from an idealized static structure. These themes build on the group\u2019s work connecting atomistic simulations and data-driven approaches to the thermodynamics and kinetics of catalyst degradation and surface restructuring.<\/p>\n\n\n\n

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Modeling dynamic catalytic interfaces.<\/strong> Atomistic and data-driven approaches for understanding electrocatalyst stability, surface reconstruction, and reaction-driven structural evolution.<\/figcaption><\/figure>\n\n\n\n
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A growing focus of our group is chemical disorder and compositionally complex materials<\/strong>. Rather than treating disorder as an imperfection to be averaged away, we seek to understand how chemical ordering, local correlations, defects, and configurational ensembles influence materials properties. We develop disorder-aware and disorder-native AI approaches that connect atomistic representations with statistical mechanics and experimental observables<\/strong>, with applications spanning alloys, oxides, high-entropy materials, and reactive interfaces.<\/p>\n\n\n\n

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Chemical and structural disorder in materials.<\/strong> Understanding and modeling the continuum from ordered crystals to locally correlated, compositionally complex, and disordered materials.<\/figcaption><\/figure>\n\n\n\n

We are also exploring AI-driven scientific discovery workflows<\/strong> that connect computation more directly with experiments. Working closely with collaborators, we develop materials-centric agentic AI<\/strong> that integrates physics-based modeling, multimodal characterization data, and uncertainty-aware active learning and decision-making. Our goal is to enable closed-loop workflows in which simulations and experiments iteratively inform one another, extending materials discovery from brute-force, black-box screening toward increasingly<\/strong><\/strong> reasoning-enabled and autonomous approaches<\/strong>.<\/p>\n\n\n\n

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Toward closed-loop AI-driven discovery.<\/strong> Working with experimental collaborators to connect physics-grounded modeling, reasoning-enabled agentic AI, and autonomous experimentation.<\/figcaption><\/figure>\n\n\n\n

Across these directions, our broader goal is to develop materials-centric AI that is physically interpretable, experimentally grounded, and useful for discovering actionable scientific principles<\/strong>\u2014not simply faster prediction models.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Discovering and understanding materials for energy and chemical technologies increasingly requires navigating enormous spaces of compositions, structures, interfaces, and operating conditions. Our group develops physics-grounded AI and modeling approaches to … <\/p>\n

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