Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept
Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept
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通过端到端方法解决化学问题：概念验证
DOI：
10.1021/acs.jpca.0c06319
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Xiaodong Wen
中科院分区：
文献类型：
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作者：
Xiaotong Liu;Tianfu Zhang;Tao Yang;Xiulei Liu;Xin Song;Yong Yang;Ning Li;Gian-Marco Rignanese;Yong-Wang Li;Xiaodong Wen
传统上，化学问题是通过演绎法解决的。要解决的问题通常与实验测量的属性值有关，使用量子化学软件计算，或者（最近）使用机器学习模型预测。在本文中，我们证明了可以使用端到端（E2E）机器学习采用归纳方法。该方法用于解决以下化学问题：（i）确定具有一个缺失原子的分子中的完全配位（FC）和欠配位（UC）原子，（ii）识别在这样的不完整分子中缺失的原子的类型，以及（iii）根据现有数据集预测两个分子之间的反应方向。E2E方法导致这三个问题的准确率分别高于99%、98%和93%。最后，为了达到这样的精度，分子的描述符，称为袋的集群，介绍和一系列先前提出的描述符相比，突出了一系列的优点。
Traditionally, chemistry problems are solved by means of a deductive approach. The question to be addressed is typically related to the value of a property that is either measured experimentally, computed using quantum-chemistry software, or (more recently) predicted using a machine-learned model. In this paper, we demonstrate that an inductive approach can be adopted using End-to-End (E2E) machine learning. This approach is illustrated for tackling the following chemistry problems: (i) determine the fully coordinated (FC) and undercoordinated (UC) atoms in a molecule with one missing atom, (ii) identify the type of atom that is missing in such an incomplete molecule, and (iii) predict the direction of a reaction between two molecules according to an existing dataset. The E2E approach leads to accuracies higher than 99%, 98%, and 93% for these three problems, respectively. Finally, in order to achieve such accuracies, a descriptor for the molecules, called bag of clusters, is introduced and compared with a series previously proposed descriptors, highlighting a series of advantages.