Active-learning and materials design: the example of high glass transition temperature polymers
Active-learning and materials design: the example of high glass transition temperature polymers
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DOI：
10.1557/mrc.2019.78
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发表时间：
2019-09-01
影响因子：
1.9
通讯作者：
Ramprasad, Rampi
中科院分区：
文献类型：
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作者：
Kim, Chiho;Chandrasekaran, Anand;Ramprasad, Rampi
机器学习(ML)方法已被证明在现代材料创新管道中非常有用。通常，ML模型是根据预定的过去数据进行训练的，然后用于对新的测试用例进行预测。然而，主动学习是一种范式，在这种范式中，ML模型可以通过为“次优实验”提供动态建议/询问来指导学习过程本身。在这项工作中，作者展示了主动学习框架如何帮助发现具有高玻璃化转变温度(T-g)的聚合物。从聚合物T-g测量的初始小数据集开始，作者使用高斯过程回归和主动学习框架来迭代地将候选聚合物的T-g测量添加到训练数据集中。主动学习框架采用三种决策策略(开发、探索或平衡开发/探索)中的一种来选择“次优实验”。一旦选择了具有大于某一阈值温度的T-g的10种聚合物，则主动学习工作流程终止。在发现高T-g聚合物方面，作者对上述三种策略(相对于随机选择方法)的性能进行了统计基准测试，以应对这一特定的示范性材料设计挑战。
Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the "next-best experiment." In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures (T-g). Starting from an initial small dataset of polymer T-g measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add T-g measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the "next-best experiment." The active-learning workflow terminates once 10 polymers possessing a T-g greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high-T-g polymers for this particular demonstrative materials design challenge.