In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for s...In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.展开更多
Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately descr...Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.12374109)the National Key Research and Development Program of China (Grant No.2023YFA1406600)。
文摘In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.
基金supported by the National Natural Science Foundation of China(Grant Nos.52225502,52305200)New Cornerstone Science Foundation through the XPLORER PRIZE,National Key Research and Development Program of China(2024YFB3410201,2018YFB0704300)+1 种基金Scientific and Technological Project of Yunnan Precious Metals Laboratory(YPML-20240502088)Key Research and Development Program of Yunnan Province(202203ZA080001).
文摘Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects.A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications.Multiphases of hydrogenated carbon materials,from crystal to amorphous,with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions.Here,we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning,and construct a general-purpose pre-trained machine learning potential(MLP)for hydrogen-carbon systems.The pre-trained MLP is further efficiently transferred to three target spaces of deposition,friction and fracture with scale reliability.This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.
