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Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds

机译：基于电子配置的神经网络模型，以预测无机化合物的物理化学性质

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摘要

Registration, evaluation, and authorization of chemicals (REACH), the regulation of chemicals in use, imposes the characterization and report of the physicochemical properties of compounds. To cope with the financial burden of the experiments, the use of computational models is permitted for prediction of properties. Although a number of physicochemical property prediction models have been developed, their applicability domain is limited to organic molecules since most available data are concerned with organic molecules, and most of the molecular descriptors are restricted to organic molecule calculations. Prediction models developed for inorganic compounds were intended to predict endpoints relevant to novel material design. Therefore, no models were available for predicting endpoints of inorganic compounds that are significant to regulatory perspectives. In this study, boiling point, water solubility, melting point, and pyrolysis point prediction models were developed for inorganic compounds based on their composition. The electron configuration of each element in the molecule was used as a descriptor in this study. The dataset covered a wide range of endpoints and diverse elements in their structure. The performance of the models was measured usingR(2), mean absolute error, and Spearman's correlation coefficient, and indicated good prediction accuracy of continuous endpoints and prioritization of inorganic compounds.
机译：化学品（达到）的注册，评估和授权，使用中的化学品调节，施加了化合物的物理化学性质的表征和报告。为了应对实验的财务负担，允许使用计算模型进行性能预测。尽管已经开发了许多物理化学性质预测模型，但是它们的适用性域仅限于有机分子，因为大多数可用数据涉及有机分子，并且大多数分子描述符仅限于有机分子计算。用于无机化合物开发的预测模型旨在预测与新型材料设计相关的终点。因此，没有用于预测对调节观点的无机化合物的终点的模型。在该研究中，为基于其组成的无机化合物开发了沸点，水溶性，熔点和热解点预测模型。分子中每个元素的电子构型被用作本研究中的描述符。 DataSet涵盖了各种端点和各种元素的结构。使用（2），平均绝对误差和Spearman的相关系数测量模型的性能，并表明了连续终点的良好预测精度和无机化合物的优先级。

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  • 来源
    《RSC Advances》 |2020年第55期|共11页
  • 作者

    Shin Hyun Kil;

  • 作者单位

    Korea Inst Toxicol Dept Predict Toxicol Toxicoinformat Grp Daejeon 34114 South Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;
  • 关键词

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