Argonne National Laboratory

Research Highlight | Center for Nanoscale Materials

Decision trees plus AI equal discovery of boron metastable phases
In an APL Machine Learning paper, researchers employed an algorithm with reinforcement learning and decision trees to search the configurational-compositional space of boron structures.
Computed (a) equilibrium phase diagram and metastable phase diagrams of boron at free energies of (b) 50 meV/atom, (c) 100 meV/atom, and (d) 200 meV/atom.

Scientific Achievement

A workflow that uses reinforcement learning coupled with decision trees was developed to search for stable and metastable boron phases.

Significance and Impact

This new framework for representing metastable boron phases offers valuable insights for experimental design, streamlining the discovery process of these phases, renowned for their attractive properties absent in stable phases.

Research Details

  • Phase diagrams that locate the phase space at different levels of metastability were generated.
  • Eight boron metastable phases were found within an enthalpy range of 0.2 eV/atom, including two not reported before.

DOI: 10.1063/5.0175994

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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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