Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) with domain knowledge in physics, chemistry, and materials science. AI-driven materials design can be divided to mainly two stages; the first one is the modeling stage, where the goal is to build an accurate regression or classification model to predict material properties (e.g. glass transition temperature) or attributes (e.g. toxic/non-toxic). The next stage is design, where the goal is to assemble or tune material structures so that they can achieve user-demanded target property values based on a prediction model that is trained in the modeling stage. For maximum benefit, these two stages should be architected to form a coherent workflow. Today there are several emerging services and tools for AI-driven material design, however, most of them provide only partial technical components (e.g. data analyzer, regression model, structure generator, etc.), that are useful for specific purposes, but for comprehensive material design, those components need to be orchestrated appropriately. Our material design system provides an end-to-end solution to this problem, with a workflow that consists of data input, feature encoding, prediction modeling, solution search, and structure generation. The system builds a regression model to predict properties, solves an inverse problem on the trained model, and generates novel chemical structure candidates that satisfy the target properties. In this paper we will introduce the methodology of our system, and demonstrate a simple example of inverse design generating new chemical structures that satisfy targeted physical property values.

中文翻译：

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人工智能驱动的有机分子逆向设计系统

设计具有所需特性的新型材料是许多制造业的核心需求。在这种工业需求的推动下，已经开发了各种算法和工具，将人工智能（机器学习和分析）与物理、化学和材料科学领域的知识相结合。AI驱动的材料设计主要可以分为两个阶段；第一个是建模阶段，目标是建立一个准确的回归或分类模型来预测材料特性（例如玻璃化转变温度）或属性（例如有毒/无毒）。下一阶段是设计，其目标是组装或调整材料结构，以便它们能够基于在建模阶段训练的预测模型实现用户要求的目标属性值。为了最大的利益，这两个阶段应该被构建成一个连贯的工作流程。如今，人工智能驱动的材料设计有几种新兴的服务和工具，但大多数只提供部分技术组件（例如数据分析器、回归模型、结构生成器等），用于特定目的，但用于综合材料设计，这些组件需要适当地编排。我们的材料设计系统为这个问题提供了端到端的解决方案，其工作流程包括数据输入、特征编码、预测建模、解决方案搜索和结构生成。该系统建立回归模型来预测特性，解决训练模型的逆问题，并生成满足目标特性的新化学结构候选。