ChemistryNature Communications
Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms
T. Lan, H. Wang, et al.
Discover HDRL-FP, a revolutionary framework leveraging deep reinforcement learning to decode catalytic reaction mechanisms at unprecedented speed. This groundbreaking research by Tian Lan, Huan Wang, and Qi An showcases insights into hydrogen and nitrogen migration during ammonia synthesis, uncovering a transition state that simplifies processes with lower energy barriers.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Chemistry
Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
D. F. Nippa, K. Atz, et al.
Psychology
Ageing is associated with disrupted reinforcement learning whilst learning to help others is preserved
J. Cutler, M. K. Wittmann, et al.
Biology
Automated high-throughput genome editing platform with an AI learning in situ prediction model
S. Li, J. An, et al.
Engineering and Technology
Deep learning model to predict fracture mechanisms of graphene
A. J. Lew, C. Yu, et al.
