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

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

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

DOI:https://doi.org/10.1063/5.0068207· OSTI ID:1837843

Sarker, Suchismita ^[1]; Tang-Kong, Robert ^[1]; Schoeppner, Rachel ^[2];

^[3];

^[4]; Van Campen, Douglas G. ^[1]; Takeuchi, Ichiro ^[3];

^[5];

^[6];

^[7];

^[1]

SLAC National Accelerator Lab., Menlo Park, CA (United States). Stanford Synchrotron Radiation Lightsource (SSRL)
Colorado School of Mines, Golden, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Maryland, College Park, MD (United States)
National Inst. of Standards and Technology (NIST), Gaithersburg, MD (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States); Colorado School of Mines, Golden, CO (United States)

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:: SLAC National Accelerator Lab., Menlo Park, CA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)

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

Grant/Contract Number:: AC02-76SF00515; FWP-100250; AC36-08GO28308

OSTI ID:: 1837843

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

Report Number(s):: NREL/JA-5K00-80853; TRN: US2300509

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

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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