While machine learning excels in simulating material thermal properties,its application to orderdisorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing p...While machine learning excels in simulating material thermal properties,its application to orderdisorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces,forces,and vibrational properties in the presence of a photoexcited electron-hole plasma.Here,we present a novel approach that combines constrained density functional theory with machine learning,yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties.Applied to photoexcited silicon,our potential accurately reproduces the phonon dispersion of the crystal phase and allows for molecular dynamics simulations of tens of thousands of atoms.We show that,at low enough temperatures,the non-thermal melting transition is driven by a soft phonon and the formation of a double-well potential,at odds with thermal melting being strictly first order.Our method paves the way to large-scale,longtime simulations of light-induced order-disorder phase transitions with ab initio accuracy.展开更多
基金EuroHPC access on LUMI(EHPC-REG-2022R03-090)for high performance computing resourcesWe acknowledge the CINECA awards HP10BMFDN9 and HP10BBEQIL under the ISCRA initiative,for the availability of high performance computing resources and supportThis work was funded by the European Union(ERC,DELIGHT,101052708),Views and opinions expressed are however those of the author(s)only and do not necessarily reflect those of the European Union or the European Research Council.Neither the European Union nor the granting authority can be held responsible for them.
文摘While machine learning excels in simulating material thermal properties,its application to orderdisorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces,forces,and vibrational properties in the presence of a photoexcited electron-hole plasma.Here,we present a novel approach that combines constrained density functional theory with machine learning,yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties.Applied to photoexcited silicon,our potential accurately reproduces the phonon dispersion of the crystal phase and allows for molecular dynamics simulations of tens of thousands of atoms.We show that,at low enough temperatures,the non-thermal melting transition is driven by a soft phonon and the formation of a double-well potential,at odds with thermal melting being strictly first order.Our method paves the way to large-scale,longtime simulations of light-induced order-disorder phase transitions with ab initio accuracy.
