The exploration of molecular configuration space is often hindered by thermally activated barriers on complex, high-dimensional potential energy surfaces, making conventional molecular dynamics (MD) simulations inefficient for accessing rare yet important transitions. Enhanced sampling techniques based on collective variables (CVs) are commonly...
Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomistic systems. Current machine learning approaches use expensive, task-specific, and data-free training procedures, limiting their ability to benefit from high-quality...
Electrochemical CO 2 reduction holds promise for an efficient and decentralized route towards energy and climate resilience by enabling the conversion of CO 2 into value-added chemicals and fuels at mild temperatures and pressures. Early systematic studies by Hori et al. in the mid-1980s identified Cu metal as unique in...
Artificial intelligence (AI) and big data are becoming deeply integrated into modern scientific research, giving rise to an emerging research field of ab initio AI, which applies state-of-the-art AI techniques to help solve long-standing bottlenecks of ab initio calculations—most notably the tradeoff between accuracy and efficiency. In this talk, Dr. Xiaoxun Gong will discuss the recently...
Rhys Bunting traces the evolution of atomistic modeling from its molecular dynamics origins to today’s AI-enhanced methods and explores how computation can better capture the complexity of real experimental systems. His talk highlights approaches that integrate diverse modeling techniques to describe catalytic and physical phenomena...
Andrea Giunto discusses how AI and automation are accelerating experimental materials science by bridging the gap between computational predictions and real-world synthesis. His talk focuses on automated characterization using SEM-EDS to enable large-scale...
Dr. Lixin Lu presents advances in accurate and efficient electronic structure methods for complex chemical systems, with a focus on heavy elements. Her talk highlights developments in relativistic multi reference methods, GPU-accelerated approaches, and DFT corrected strategies that recover electron correlation efficiently. Through case studies, she demonstrates how these...
Computer simulation of many-body chemical systems has advanced significantly in recent years through data-driven modeling. Generally, we classify the role of data-driven and machine-learning techniques into (i) accelerating simulation, amplifying the accessible length(time) scales, and (ii) improving method accuracy...
The connection between measure-theoretic optimal transport and dissipative nonequilibrium dynamics provides a framework for quantifying nonequilibrium control costs, leading to a geometric interpretation of control and a collection of thermodynamic speed limits. However, realizing such optimal control strategies is challenging both in laboratory...
