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Research Highlight | Center for Nanoscale Materials

Machine learning facilitates construction of metastable phase diagrams

In a paper appearing in Nature Communications, researchers report a machine learning-based approach for producing phase diagrams, which are applicable to metastable materials that are far from equilibrium.

Scientific Achievement

A machine learning-based approach has been developed for producing metastable phase diagrams, which are applicable to materials that are far from equilibrium. This automated framework combines machine learning and high-performance computing with first-principles physics and atomistic simulations.

Significance and Impact

This approach is able to predict, identify, and map the free energy of metastable materials. The results can be used for designing experiments and accelerating the discovery of metastable materials, which often display exotic properties with practical applications.

Research Details

A metastable phase diagram was constructed for carbon to map hundreds of metastable states ranging from near equilibrium to far from equilibrium (400 meV/atom).

DOI10.1038/s41467-022-30820-8

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About Argonne’s Center for Nanoscale Materials
The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE’s Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://​sci​ence​.osti​.gov/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​-​a​t​-​a​-​G​lance.

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