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cond-mat.mtrl-sci2025
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
Andrij Vasylenko, Federico Ottomano, Christopher M. Collins +3
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidat…
cs.LG2025
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
Elena Zamaraeva, Christopher M. Collins, George R. Darling +8
Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a n…