Kebotix, Inc. , 501 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.
Department of Chemistry and Chemical Biology , Harvard University , Cambridge , Massachusetts 02138 , United States.
ACS Appl Mater Interfaces. 2019 Jul 17;11(28):24825-24836. doi: 10.1021/acsami.9b01226. Epub 2019 Mar 25.
The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Such systems learn through every single experiment in an analogy to a human scientific expert. While the general idea of an autonomous system for molecular synthesis and characterization has been around for a while, its implementations for the materials sciences are sparse. Here we provide an overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in.
深度学习在处理大量数据方面的成功，例如在图像或语音识别和生成方面，提高了这些工具也可以应用于解决材料科学中复杂问题的可能性。在这篇专题文章中，我们专注于分子设计，旨在回答如何预测和合成具有定制物理、化学或生物特性的分子的问题。这个问题的一个潜在答案可能是通过使用智能系统来找到，该系统将物理模型和计算机器学习技术与自动化合成和表征工具集成在一起。这些系统通过类似于人类科学专家的每一次实验进行学习。虽然分子合成和表征自主系统的一般概念已经存在了一段时间，但它在材料科学中的实现却很少。在这里，我们提供了化学自动化的发展概述，并重点介绍了机器学习技术在化学和制药行业中的应用，以及深度学习带来的新功能。
