Discovering Exceptionally Hard and Wear-Resistant Metallic Glasses by Combining Machine-Learning with High Throughput Experimentation

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

Research output: Contribution to journalArticlepeer-review

14 Scopus Citations


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.

Original languageAmerican English
Article number011403
Number of pages6
JournalApplied Physics Reviews
Issue number1
StatePublished - 1 Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 Author(s).

NREL Publication Number

  • NREL/JA-5K00-80853


  • combinatorial sputtering
  • machine learning
  • mechanical properties
  • metallic glasses
  • structural materials


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