Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
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DOI：
10.1002/advs.201902607
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发表时间：
2020-01-09
期刊：
影响因子：
15.1
通讯作者：
Gu, Grace X.
中科院分区：
文献类型：
--
作者：
Chen, Chun-Teh;Gu, Grace X.
近年来，机器学习（ML）技术被认为是发现和设计新材料的有前途的工具。然而，缺乏鲁棒的逆向设计方法来识别有前途的候选材料，而不探索整个设计空间的原因是一个根本的瓶颈。提出了一种基于生成式反设计网络的通用反设计方法。这种基于ML的逆设计方法使用反向传播来计算目标函数相对于设计变量的分析梯度。这种逆向设计方法能够通过使用反向传播提供梯度信息的快速计算并使用不同的初始值运行数百万次优化来克服局部最小值陷阱。此外，在逆向设计方法中采用主动学习策略，以提高候选材料的性能，并减少所需的训练数据量。与被动学习相比，主动学习策略能够生成更好的设计，并在复合材料的案例研究中将训练数据量减少至少一个数量级。反设计方法进行了比较，与传统的基于梯度的拓扑优化和无梯度遗传算法和每种方法的优点和缺点进行了讨论时，应用到材料的发现和设计问题。
In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general-purpose inverse design approach is presented using generative inverse design networks. This ML-based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order-of-magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient-based topology optimization and gradient-free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems.