标题： 材料信息学中的外推预测生成模型 显示英文标题

标题： A Generative Model for Extrapolation Prediction in Materials Informatics

摘要： 我们报告了一种用于材料信息学中回归任务的深度生成模型。 该模型被介绍为数据插补器的一个组成部分，并能预测有机分子的超过20种不同的实验性质。 该插补器旨在通过“想象”数据库中的缺失数据来预测材料性质，从而可以使用不完整的材料数据。 即使移除60%的数据，也不会降低模型任务中的预测准确性。 此外，该模型在外推预测方面表现出色，其中测试数据的目标值超出了训练数据的范围。 这种外推已被视为探索新材料的关键技术，但由于其难度，至今很少被研究。 我们证明，与传统的线性回归和提升模型相比，使用该插补器可以使预测性能提高超过30%。 当某种实验性质的记录较少（少于100个案例）时，这种优势尤其明显，因为传统方法在这种情况下预测会很困难。 所提出的方法可以更高效地探索功能材料并突破之前的性能限制。 显示英文摘要

摘要： We report a deep generative model for regression tasks in materials informatics. The model is introduced as a component of a data imputer, and predicts more than 20 diverse experimental properties of organic molecules. The imputer is designed to predict material properties by "imagining" the missing data in the database, enabling the use of incomplete material data. Even removing 60% of the data does not diminish the prediction accuracy in a model task. Moreover, the model excels at extrapolation prediction, where target values of the test data are out of the range of the training data. Such extrapolation has been regarded as an essential technique for exploring novel materials, but has hardly been studied to date due to its difficulty. We demonstrate that the prediction performance can be improved by >30% by using the imputer compared with traditional linear regression and boosting models. The benefit becomes especially pronounced with few records for an experimental property (< 100 cases) when prediction would be difficult by conventional methods. The presented approach can be used to more efficiently explore functional materials and break through previous performance limits.