MACS, a multi-agent RL optimizer for periodic crystals, achieves faster local geometry optimization with fewer energy calls and a lower failure rate than BFGS, FIRE, and other baselines, and transfers zero-shot to chemically related unseen compositions.
Learning to optimize in swarms
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MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
MACS, a multi-agent RL optimizer for periodic crystals, achieves faster local geometry optimization with fewer energy calls and a lower failure rate than BFGS, FIRE, and other baselines, and transfers zero-shot to chemically related unseen compositions.