Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML...Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML is a potentially valuable tool for predictive modeling and process optimization.Herein,we first review the recent progress in data-driven ML for molecular crystal design,including property and structure predictions.ML can accelerate the development of the solvates,co-crystals,and colloidal nanocrystals,and improve the efficiency of crystal design.Next,this review summarizes ML algorithms for crystallization behavior prediction and process regulation.ML models support drug solubility prediction,particle agglomeration prediction,and spherical crystal design.ML-based in situ image processing can extract particle information and recognize crystal products.The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented.Finally,emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.展开更多
Clay minerals can experience strong tensile and compressive forces in extreme environments such as the deep sea and subsurface.Moreover,the presence of water films greatly affects the mechanical properties of clay.To ...Clay minerals can experience strong tensile and compressive forces in extreme environments such as the deep sea and subsurface.Moreover,the presence of water films greatly affects the mechanical properties of clay.To explore these properties,we use a molecular dynamics(MD)simulation method to study axial mechanical behavior and failure mechanisms of hydrated kaolinite.Two types of deformation are applied to kaolinite examples with varying water film thicknesses:stretching along the transverse(x)direction,and compression along the longitudinal(z)direction.The ultimate strengths of hydrated kaolinite with different water film thicknesses range from 8.12%to 27.53%(for stretching along the x-direction)and from 15.71%to 26.02%(for compression along the z-direction)less than those of dehydrated kaolinite.Additionally,we find that hydrated kaolinite is more prone to tensile than compressive failure under high stress.When stretched along the x-direction,the diffusion of water molecules results in unstable tensile properties.When compressed along the z-direction,water films weaken the compressive strength of the system and lead to greater compressive deformation,but also delay the time at which the system fails.Furthermore,we investigated the failure mechanisms of hydrated kaolinite through analysis of interaction energies.The tensile failure along the x-direction is caused by the breaking of the covalent bonds in the clay mineral sheet.On the other hand,the compressive failure along the z-direction is due to the crushing of the internal structure of the clay mineral sheet.展开更多
基金financially supported by the National Natural Science Foundation of China(22008173,21938009,and 21676179)the Major Key Technology Project of ShandongProvincial Key Research and Development Program(2021CXGC010514)the support of the China Scholarship Council。
文摘Machine learning(ML)can optimize the research paradigm and shorten the time from discovery to application of novel functional materials,pharmaceuticals,and fine chemicals.Besides supporting material and drug design,ML is a potentially valuable tool for predictive modeling and process optimization.Herein,we first review the recent progress in data-driven ML for molecular crystal design,including property and structure predictions.ML can accelerate the development of the solvates,co-crystals,and colloidal nanocrystals,and improve the efficiency of crystal design.Next,this review summarizes ML algorithms for crystallization behavior prediction and process regulation.ML models support drug solubility prediction,particle agglomeration prediction,and spherical crystal design.ML-based in situ image processing can extract particle information and recognize crystal products.The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented.Finally,emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.
基金supported by the National Natural Science Foundation of China(No.52009149)the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515012612),China。
文摘Clay minerals can experience strong tensile and compressive forces in extreme environments such as the deep sea and subsurface.Moreover,the presence of water films greatly affects the mechanical properties of clay.To explore these properties,we use a molecular dynamics(MD)simulation method to study axial mechanical behavior and failure mechanisms of hydrated kaolinite.Two types of deformation are applied to kaolinite examples with varying water film thicknesses:stretching along the transverse(x)direction,and compression along the longitudinal(z)direction.The ultimate strengths of hydrated kaolinite with different water film thicknesses range from 8.12%to 27.53%(for stretching along the x-direction)and from 15.71%to 26.02%(for compression along the z-direction)less than those of dehydrated kaolinite.Additionally,we find that hydrated kaolinite is more prone to tensile than compressive failure under high stress.When stretched along the x-direction,the diffusion of water molecules results in unstable tensile properties.When compressed along the z-direction,water films weaken the compressive strength of the system and lead to greater compressive deformation,but also delay the time at which the system fails.Furthermore,we investigated the failure mechanisms of hydrated kaolinite through analysis of interaction energies.The tensile failure along the x-direction is caused by the breaking of the covalent bonds in the clay mineral sheet.On the other hand,the compressive failure along the z-direction is due to the crushing of the internal structure of the clay mineral sheet.
