ON EXTRAPOLATION IN MATERIAL PROPERTY REGRESSION
Keywords: material property prediction, regression, extrapolation
TL;DR: We explore extrapolation in material properties regression (MPR), revealing limitations in current methods. Our new Matching-based EXtrapolation (MEX) framework achieves state-of-the-art performance, enhancing material discovery.
Abstract: Deep learning methods have yielded exceptional performances in material property regression (MPR). However, most existing methods operate under the assumption that the training and test are independent and identically distributed (i.i.d.). This overlooks the importance of extrapolation - predicting material properties beyond the range of training data - which is essential for advanced material discovery, as researchers strive to identify materials with exceptional properties that exceed current capabilities. In this paper, we address this gap by introducing a comprehensive benchmark comprising seven tasks specifically designed to evaluate extrapolation in MPR. We critically evaluate existing methods including deep imbalanced regression (DIR) and regression data augmentation (DA) methods, and reveal their limitations in extrapolation tasks. To address these issues, we propose the Matching-based EXtrapolation (MEX) framework, which reframes MPR as a material-property matching problem to alleviate the inherent complexity of the direct material-to-label mapping paradigm for better extrapolation. Our experimental results show that MEX outperforms all existing methods on our benchmark and demonstrates exceptional capability in identifying promising materials, underscoring its potential for advancing material discovery.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 12945
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