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While high-entropy alloys (HEAs) present exponentially large compositional space for alloy design, they also create enormous computational challenges to trace the compositional space, especially for the inherently expensive density functional theory calculations (DFT). Recent works have integrated machine learning into DFT to overcome these challenges. However, often these models require an intensive search of appropriate physics-based descriptors. In this paper, we employ a 3D convolutional neural network over just one descriptor, i.e., the charge density derived from DFT, to simplify and bypass the hunt for the descriptors. We show that the elastic constants of face-centered cubic multi-elemental alloys in the Ni–Cu–Au–Pd–Pt system can be predicted from charge density. In addition, using our recent PREDICT approach, we show that the model can be trained only on the charge densities of simpler binary and ternary alloys to effectively predict elastic constants in complex multi-elemental alloys, thereby further enabling easier property-tracing in the large compositional space of HEAs. 

This paper proposes a question-answering (QA) benchmark for spatial reasoning on nat- ural language text which contains more real- istic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reason- ing rules to automatically generate a spatial de- scription of visual scenes and corresponding QA pairs. Experiments show that further pre- training LMs on these automatically generated data significantly improves LMs’ capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster inves- tigations into more sophisticated models for spatial reasoning over text.