Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

Sarker, Suchismita; Tang-Kong, Robert; Schoeppner, Rachel; Ward, Logan; Hasan, Naila Al; Van Campen, Douglas G.; Takeuchi, Ichiro; Hattrick-Simpers, Jason; Zakutayev, Andriy; Packard, Corinne E.; Mehta, Apurva

doi:10.1063/5.0068207

Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

Journal Article · Mon Jan 10 00:00:00 UTC 2022 · Applied Physics Reviews

DOI:https://doi.org/10.1063/5.0068207· OSTI ID:2564495

Sarker, Suchismita; Tang-Kong, Robert; Schoeppner, Rachel; ; ; Van Campen, Douglas G.; Takeuchi, Ichiro; ; ; ;

Lack of crystalline order in amorphous alloys, commonly called metallic glasses (MGs), tends to make them harder and more wear-resistant than their crystalline counterparts. However, finding inexpensive MGs is daunting; finding one with enhanced wear resistance is a further challenge. Relying on machine learning (ML) predictions of MGs alone requires a highly precise model; however, incorporating high-throughput (HiTp) experiments into the search rapidly leads to higher performing materials even from moderately accurate models. Here, we exploit this synergy between ML predictions and HiTp experimentation to discover new hard and wear-resistant MGs in the Fe–Nb–B ternary material system. Several of the new alloys exhibit hardness greater than 25 GPa, which is over three times harder than hardened stainless steel and only surpassed by diamond and diamond-like carbon. This ability to use less than perfect ML predictions to successfully guide HiTp experiments, demonstrated here, is especially important for searching the vast Multi-Principal-Element-Alloy combinatorial space, which is still poorly understood theoretically and sparsely explored experimentally.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE; USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office

Grant/Contract Number:: AC02-76SF00515; AC36-08GO28308

Other Award/Contract Number:: FWP-100250

OSTI ID:: 2564495

Alternate ID(s):: OSTI ID: 1837843
OSTI ID: 1839138
OSTI ID: 1840917

Report Number(s):: NREL/JA-5K00-80853; 011403

Journal Information:: Applied Physics Reviews, Journal Name: Applied Physics Reviews Journal Issue: 1 Vol. 9; ISSN 1931-9401

Publisher:: American Institute of PhysicsCopyright Statement

Country of Publication:: United States

Language:: English

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Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

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