Nicholas Ho

About

Nicholas Ho is a computational researcher whose work bridges molecular dynamics, reinforcement learning, and statistical mechanics. His primary research areas include free energy landscape exploration, adaptive sampling methods, and the application of robotics-inspired planning algorithms to biomolecular systems. Ho’s major contribution lies in developing a novel formulation that integrates reinforcement learning with adaptive steered molecular dynamics to efficiently compute low free energy transition pathways between known molecular states. By leveraging Jarzynski’s equality and the stiff-spring approximation, his approach enables point estimates of free energy differences, offering a powerful alternative to traditional methods that often suffer from high computational costs. His most-cited paper, “Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics” (2022), has garnered attention for its innovative fusion of machine learning and physical chemistry, laying groundwork for more efficient exploration of complex energy landscapes. Though early in his career, Ho’s work signals a promising direction for automating and accelerating the discovery of rare events in molecular systems, with potential applications in drug design and materials science.

Research Focus

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Top Papers

1. 1
   Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

   2 citations · 2022

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