Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles
This work addresses the combinatorial optimization challenge for material scientists designing nanoparticles, offering a transferable strategy that could reduce search costs, but it is incremental as it builds on existing RL and graph encoding methods.
The researchers tackled the problem of finding optimal element ordering in bimetallic alloy nanoparticles by framing it as a reinforcement learning task, resulting in an agent that discovered known ground state structures and extrapolated to unseen sizes, though with limitations in multi-element cases.
We approach the search for optimal element ordering in bimetallic alloy nanoparticles (NPs) as a reinforcement learning (RL) problem, and have built an RL agent that learns to perform such global optimisation using the geometric graph representation of the NPs. To demonstrate the effectiveness, we train an RL agent to perform composition-conserving atomic swap actions on the icosahedral nanoparticle structure. Trained once on randomised $Ag_{X}Au_{309-X}$ compositions and orderings, the agent discovers previously established ground state structure. We show that this optimization is robust to differently ordered initialisations of the same NP compositions. We also demonstrate that a trained policy can extrapolate effectively to NPs of unseen size. However, the efficacy is limited when multiple alloying elements are involved. Our results demonstrate that RL with pre-trained equivariant graph encodings can navigate combinatorial ordering spaces at the nanoparticle scale, and offer a transferable optimisation strategy with the potential to generalise across composition and reduce repeated individual search cost.