Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning approach combined with generative modeling to predict optimal synthesis parameters for quantum materials, demonstrated on MoS2, enabling more efficient and accurate material synthesis planning.

Contribution

It presents a novel reinforcement learning framework coupled with deep generative modeling to predict synthesis schedules for quantum materials, extending beyond traditional trial-and-error methods.

Findings

Successfully predicted synthesis schedules for MoS2 with high accuracy.

Identified threshold temperatures and chemical potentials for reactions.

Validated predictions through computational synthesis simulations.

Abstract

Predictive materials synthesis is the primary bottleneck in realizing new functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error approaches and there are no known predictive schemes to design synthesis parameters for new materials. We use reinforcement learning to predict optimal synthesis schedules, i.e. a time-sequence of reaction conditions like temperatures and reactant concentrations, for the synthesis of a prototypical quantum material, semiconducting monolayer MoS$_{2}$, using chemical vapor deposition. The predictive reinforcement leaning agent is coupled to a deep generative model to capture the crystallinity and phase-composition of synthesized MoS$_{2}$ during CVD synthesis as a function of time-dependent synthesis conditions. This model, trained on 10000 computational synthesis simulations, successfully learned threshold temperatures and chemical potentials for the onset of chemical reactions and predicted new synthesis schedules for producing well-sulfidized crystalline and phase-pure MoS$_{2}$, which were validated by computational synthesis simulations. The model can be extended to predict profiles for synthesis of complex structures including multi-phase heterostructures and can also predict long-time behavior of reacting systems, far beyond the domain of the MD simulations used to train the model, making these predictions directly relevant to experimental synthesis.