论文标题

通过概率增强学习对量子材料的预测合成

Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning

论文作者

Rajak, Pankaj, Krishnamoorthy, Aravind, Mishra, Ankit, Kalia, Rajiv K., Nakano, Aiichiro, Vashishta, Priya

论文摘要

预测材料合成是实现新功能和量子材料的主要瓶颈。目前,通过耗时的试验和错误方法来确定合成有前途材料的策略,并且没有已知的预测方案来设计新材料的合成参数。我们使用强化学习来预测最佳的合成时间表,即,使用化学蒸气沉积,将反应条件(如温度和反应物浓度)的时间序列,用于综合原型量子材料,半导体单层MOS $ _ {2} $。预测增强倾斜剂与深层生成模型耦合,以捕获CVD合成过程中合成MOS $ _ {2} $的结晶度和相结合,这是时间依赖性合成条件的函数。该模型对10000个计算合成模拟进行了训练,成功地学习了化学反应发作的阈值温度和化学潜力,并预测了产生硫化良好的晶体和相位纯度MOS $ _ {2} $的新合成时间表,通过计算综合模拟验证了这些溶剂。可以扩展该模型以预测概况,以合成包括多相异质结构的复杂结构,并且还可以预测反应系统的长期行为,远远超出了用于训练模型的MD模拟的域,使这些预测与实验合成直接相关。

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.

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