Epps Robert W, Volk Amanda A, Reyes Kristofer G, Abolhasani Milad
Department of Chemical and Biomolecular Engineering, North Carolina State University Raleigh North Carolina 27606 USA
Department of Materials Design and Innovation, University at Buffalo Buffalo New York 14260 USA.
Chem Sci. 2021 Mar 9;12(17):6025-6036. doi: 10.1039/d0sc06463g. eCollection 2021 May 5.
Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments - the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents - to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorable technique for intelligently and efficiently navigating a complex material synthesis space with multiple targets. The developed ensemble neural network-based decision-making algorithm enables more efficient material formulation optimization in a no prior information environment than well-established algorithms.
自主机器人实验策略的使用正在迅速增加，因为无需用户干预，它们就能高效、精确地收敛到多种新兴材料的最佳本征和非本征合成条件。然而，随着材料合成变得更加复杂，自主平台中使用的人工智能（AI）引导决策算法的元决策变得更加重要。在这项工作中，利用来自一个自驱动模块化微流控材料合成器中1000多次内部进行的金属卤化物钙钛矿量子点合成的数据，开发了一个替代模型。该模型旨在表示具有多个输出参数（峰值发射、发射线宽和量子产率）的真实机器人材料合成系统的全局故障率、合成空间的不可行区域、合成真值和采样噪声。利用该模型，在单周期水平强化学习框架内，对超过600000次模拟实验自动探索了150多种AI引导决策策略——相当于7.5年的连续机器人操作和400升试剂——以确定加速多目标材料开发的最有效方法。具体而言，研究了基于集成神经网络的材料开发策略的结构和元决策，该策略为智能、高效地在具有多个目标的复杂材料合成空间中导航提供了一种有利的技术。与成熟算法相比，所开发的基于集成神经网络的决策算法能够在无先验信息环境中更高效地进行材料配方优化。
