AI Article Synopsis
- Computational exploration of materials' compositional spaces aids in synthetic research and speeds up the discovery of new materials.
- Many current methods use high-throughput sampling to quickly evaluate energy, which can sacrifice accuracy.
- The PhaseBO learning algorithm enhances the process by optimizing material stoichiometry while maintaining accurate energy evaluations, thus improving discovery chances.
Article Abstract
Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation.
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| http://dx.doi.org/10.1063/5.0180818 | DOI Listing |
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