Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys

Volume: 209, Pages: 114366 - 114366
Published: Mar 1, 2022
Abstract
Machine Learning has thrived on the emergence of data-driven materials science. However, the materials datasets acquired at existing research efforts have significant imbalance issues. This paper investigated the data imbalance for the glass-forming ability of ternary alloy systems, which consists of abundant, low-fidelity high-throughput data, and sparse, high-fidelity traditional experimental data. We demonstrated a new method to handle the...
Paper Details
Title
Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys
Published Date
Mar 1, 2022
Volume
209
Pages
114366 - 114366
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