使用基于图案的指纹加速材料性能预测和设计
Accelerated materials property predictions and design using motif-based fingerprints
摘要：
数据驱动的方法对于计算材料的发现和设计特别有用，因为它们可以用于快速筛选大量材料，从而为进一步的深入研究提供领先的候选人。这种方法的一个核心挑战是开发材料的数字表示，通常称为指纹。受化学信息学的启发，我们提出了一类基于模体的拓扑指纹，用于由C、O、H、N、F等元素组成的材料，他们的协调偏好是众所周知的。我们表明，这些指纹，当代表分子或晶体时，可以有效地映射到各种属性使用基于相似性的学习模型，因此可以用来预测材料的相关属性，因为它的指纹可以被定义。介绍了两个简单的程序，以证明学习模型可以被倒置，以确定所需的指纹，然后，重建具有一组目标属性的分子。
英文摘要：
Data-driven approaches are particularly useful for computational materials discovery and design as they can be used for rapidly screening over a very large number of materials, thus suggesting lead candidates for further in-depth investigations. A central challenge of such approaches is to develop a numerical representation, often referred to as a fingerprint, of the materials. Inspired by recent developments in chem-informatics, we propose a class of hierarchical motif-based topological fingerprints for materials composed of elements such as C, O, H, N, F, etc., whose coordination preferences are well understood. We show that these fingerprints, when representing either molecules or crystals, may be effectively mapped onto a variety of properties using a similarity-based learning model and hence can be used to predict relevant properties of a material, given that its fingerprint can be defined. Two simple procedures are introduced to demonstrate that the learning model can be inverted to identify the desired fingerprints and then, to reconstruct molecules which possess a set of targeted properties.
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