Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report.According to news originating from Manhattan,Kansas,by NewsRx correspondents,research stated,"Thermodynamic phase stability of three elemental boron allotropes,i.e.,a-B,b-B,and g-B,was investigated using a Ba yesian interatomic potential trained via a sparse Gaussian process (SGP).SGP po tentials trained with data sets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics (MD) simulations t o predict wide-ranging thermodynamic,structural,and vibrational properties." Our news journalists obtained a quote from the research from Kansas State Univer sity,"The simulated phase diagram (500-1400 K and 0-16 GPa) agrees with experim ental measurements.The SGP-based MD simulations also successfully predicted tha t the B13 defect is critical in stabilizing b-B below 700 K.At higher temperatu res,the entropy becomes the dominant factor,making b-B the more stable phase o ver a-B." According to the news editors,the research concluded:"This letter demonstrates that SGP potentials based on a training set consisting of defect-free-only syst ems could make correct predictions of defect-related phenomena in solid-state cr ystals,paving the path to investigate crystal phase stability and transitions."
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