Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Materials Science Benchmark and A Sparsity-Oriented Optimization Framework

Xuxi Chen, Tianlong Chen, Everardo Yeriel Olivares, Kate Elder, Scott McCall, Aurelien Perron, Joseph McKeown, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher

Keywords: AI4Science, sparsity, bi-level optimization

Abstract: Discovering high-entropy alloys (HEAs) with high yield strength (YS) is crucial in materials science. However, the YS can only be accurately measured by expensive and time-consuming experiments, hence cannot be acquired at scale. Learning-based methods could facilitate the discovery, but the lack of a comprehensive dataset on HEA YS has created barriers. We present X-Yield, a materials science benchmark with 240 experimentally measured (high-quality) and over 100,000 simulated (low-quality) HEA YS data. Due to the scarcity of experimental results and the quality gap with simulated data, existing transfer learning methods cannot generalize well on our dataset. We address this cross-quality few-shot transfer problem by leveraging model sparsification "twice" --- as a noise-robust feature regularizer at the pre-training stage, and as a data-efficient regularizer at the transfer stage. While the workflow already performs decently with sparsity patterns tuned independently for either stage, we propose a bi-level optimization framework termed Bi-RPT, that jointly learns optimal masks and allocates sparsity for both stages. The effectiveness of Bi-RPT is validated through experiments on X-Yield, alongside other testbeds. Specifically, we achieve a reduction of 8.9-19.8% in test MSE and a gain of 0.98-1.53% in test accuracy, using only 5-10% of the hard-to-generate real experimental data. The codes are available in https://github.com/VITA-Group/Bi-RPT.

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Submission Number: 20