Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

Journal Article · Tue Nov 05 00:00:00 EST 2024 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.4c00303· OSTI ID:2483453

Leverant, Calen John ^[1]; Harvey, Jacob A. ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 2483453
• Report Number(s): SAND--2024-16823J
• Journal Information: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 22 Vol. 36; ISSN 0897-4756
• Publisher: American Chemical Society (ACS)
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
Density functional theory
Inorganic carbon compounds
Lattices
Metals
Transition metals