Formamidine lead triiodide(FAPbI_(3))perovskites have become the most promising photovoltaic materials for perovskite solar cells with record power conversion efficiency(PCE).However,random nucleation,phase transition...Formamidine lead triiodide(FAPbI_(3))perovskites have become the most promising photovoltaic materials for perovskite solar cells with record power conversion efficiency(PCE).However,random nucleation,phase transition,and lattice defects are still the key challenges limiting the quality of FAPbI_(3) films.Previous studies show that the introduction or adding of seeds in the precursor is effective to promote the nucleation and crystallization of perovskite films.Nevertheless,the seed-assisted approach focuses on heterogeneous seeds or hetero-composites,which inevitably induce a lattice-mismatch,the genera-tion of strain or defects,and the phase segregation in the perovskite films.Herein,we first demonstrate that high-quality perovskite films are controllably prepared using α-and δ-phases mixed FAPbI_(3) micro-crystal as the homogeneous seeds with the one-step antisolvent method.The partially dissolved seeds with suitable sizes improve the crystallinity of the perovskite flm with preferable orientation,improved carrier lifetime,and increased carrier mobility.More importantly,the α-phase-containing seeds promote the formation of α-phase FAPbI_(3) films,leading to the reduction of residual lattice strain and the suppres-sion of I-ion migration.Besides,the adding of dimethyl 2,6-pyridine dicarboxylate(DPD)into the pre-cursor further suppresses the generation of defects,contributing to the PCE of devices prepared in air ambient being significantly improved to 23.75%,among the highest PCEs for fully air-processed FAPbI_(3) solar cells.The unpackaged target devices possess a high stability,maintaining 80%of the initial PCE under simulated solar illumination exceeding 800 h.展开更多
Immersive services are the typical emerging services in current IMT-2020 network.With the development of network evolution,real-time interactive applications emerge one after another.This article provides an overview ...Immersive services are the typical emerging services in current IMT-2020 network.With the development of network evolution,real-time interactive applications emerge one after another.This article provides an overview on immersive services which focus on real-time interaction.The scenarios,framework,requirements,key technologies,and issues of interactive immersive service are presented.展开更多
Solid-state batteries have received increasing attention in scientific and industrial communities,which benefits from the intrinsically safe solid electrolytes(SEs).Although much effort has been devoted to designing S...Solid-state batteries have received increasing attention in scientific and industrial communities,which benefits from the intrinsically safe solid electrolytes(SEs).Although much effort has been devoted to designing SEs with high ionic conductivities,it is extremely difficult to fully understand the ionic diffusion mechanisms in SEs through conventional experimental and theoretical methods.Herein,the temperature-dependent concerted diffusion mechanism of ions in SEs is explored through machinelearning molecular dynamics,taking Li_(10)GeP_(2)S_(12) as a prototype.Weaker diffusion anisotropy,more disordered Li distributions,and shorter residence time are observed at a higher temperature.Arrhenius-type temperature dependence is maintained within a wide temperature range,which is attributed to the linear temperature dependence of jump frequencies of various concerted diffusion modes.These results provide a theoretical framework to understand the ionic diffusion mechanisms in SEs and deepen the understanding of the chemical origin of temperature-dependent concerted diffusions in SEs.展开更多
Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface(PES)is the philosopher's stone for unraveling the nature of matter via atomistic simulatio...Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface(PES)is the philosopher's stone for unraveling the nature of matter via atomistic simulation.This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES.In this work,we develop an accurate PES model for the Al-Cu-Mg system by employing deep potential(DP),a neural network based representation of the PES,and DP generator(DP-GEN),a concurrent-learning scheme that generates a compact set of ab initio data for training.The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties,including formation energy,equilibrium volume,equation of state,interstitial energy,vacancy and surface formation energy,as well as elastic moduli.Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.展开更多
Objective To evaluate the prevalence,awareness,treatment and control rate of hypertension among elder population in China.Methods Data form a cross-sectional stratified multistage random sampling survey conducted from...Objective To evaluate the prevalence,awareness,treatment and control rate of hypertension among elder population in China.Methods Data form a cross-sectional stratified multistage random sampling survey conducted from 2012 to 2015 were used to analyzed.Finally,a total of 134,397 participants aged≥60 years were enrolled in our study.Hypertension was defined as systolic BP≥140 mmHg,and/or diastolic BP≥90 mmHg,and/or use of antihypertensive medicine within 2 weeks.Among participants with hypertension,control rate of hypertension was defined as the participant presenting as hypertensive,but with a systolic BP measure less than 140 mm Hg and diastolic BP measure less than 90 mm Hg.展开更多
This paper proposes a method for the rapid detection of subsurface damage(SSD)of Si C using atmospheric inductivity coupled plasma.As a plasma etching method operated at ambient pressure with no bias voltage,this meth...This paper proposes a method for the rapid detection of subsurface damage(SSD)of Si C using atmospheric inductivity coupled plasma.As a plasma etching method operated at ambient pressure with no bias voltage,this method does not introduce any new SSD to the substrate.Plasma diagnosis and simulation are used to optimize the detection operation.Assisted by an Si C cover,a taper can be etched on the substrate with a high material removal rate.Confocal laser scanning microscopy and scanning electron microscope are used to analyze the etching results,and scanning transmission electron microscope(STEM)is adopted to confirm the accuracy of this method.The STEM result also indicates that etching does not introduce any SSD,and the thoroughly etched surface is a perfectly single crystal.A rapid SSD screening ability is also demonstrated,showing that this method is a promising approach for the rapid detection of SSD.展开更多
Objective To investigate the prevalence of hypertension overweight/obesity and the combined effect on the incidence of cardiovascular disease(CVD).Methods The study population(aged from 35 to 64)were selected from 9 r...Objective To investigate the prevalence of hypertension overweight/obesity and the combined effect on the incidence of cardiovascular disease(CVD).Methods The study population(aged from 35 to 64)were selected from 9 regions of China by cluster sampling method.The baseline was conducted in 2010,and the follow-up survey was done in 2017.Participants with 24≤BMI28 kg/m^2 was defined as overweight,BMI≥28 kg/m^2 was defined as obesity.展开更多
Objective To evaluate the effect of a workplace-based comprehensive intervention strategy on the improvement of blood pressure (BP) control.Methods A cluster controlled trail, with workplaces (clusters)assigned to eit...Objective To evaluate the effect of a workplace-based comprehensive intervention strategy on the improvement of blood pressure (BP) control.Methods A cluster controlled trail, with workplaces (clusters)assigned to either the intervention or control group. Totally, 30 statedowned enterprises across China were included, among which 20were allocated to the intervention group and 10 to the control group.展开更多
Objective To determine whether a workplace-based multicomponent intervention strategy could improve BP control among Chinese working population.Methods A cluster-controlled trail,with workplaces assigned to either the...Objective To determine whether a workplace-based multicomponent intervention strategy could improve BP control among Chinese working population.Methods A cluster-controlled trail,with workplaces assigned to either the intervention or control group.60 workplaces across 20 urban regions of China were selected.4,548 hypertensive employees aged 18-60 years were assigned intervention(n=3,470)or control(n=1,078),of whom 4,205(92.5%;intervention,n=3,209;control,n=996)were included in this analysis.展开更多
Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying...Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.展开更多
A new force is introduced in the social force model （SFM） for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pede...A new force is introduced in the social force model （SFM） for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.展开更多
Trophoblast stem cells (TSCs), which can be derived from the trophoectoderm of a blastocyst, have the ability to sustain self-renewal and differentiate into various placental trophoblast cell types. Meanwhile, essenti...Trophoblast stem cells (TSCs), which can be derived from the trophoectoderm of a blastocyst, have the ability to sustain self-renewal and differentiate into various placental trophoblast cell types. Meanwhile, essential insights into the molecular mechanisms controlling the placental development can be gained by using TSCs as the cell model. Esrrb is a transcription factor that has been shown to play pivotal roles in both embryonic stem cell (ESC) and TSC, but the precise mechanism whereby Esrrb regulates TSC-specific transcriptome during differentiation and reprogramming is still largely unknown. In the present study, we elucidate the function of Esrrb in self-renewal and differentiation of TSCs, as well as during the induced TSC (iTSC) reprogramming. We demonstrate that the precise level of Esrrb is critical for stem state maintenance and further trophoblast differentiation of TSCs, as ectopically expressed Esrrb can partially block the rapid differentiation of TSCs in the absence of fibroblast growth factor 4. However, Esrrb depletion results in downregulation of certain key TSC-specific transcription factors, consequently causing a rapid differentiation of TSCs and these Esrrb-deficient TSCs lose the ability of hemorrhagic lesion formation in vivo. This function of Esrrb is exerted by directly binding and activating a core set of TSC-specific target genes including Cdx2, Eomes, Sox2, Fgfr4, and Bmp4. Furthermore, we show that Esrrb overexpression can facilitate the MEF-to-iTSC conversion. Moreover, Esrrb can substitute for Eomes to generate GEsTM-iTSCs. Thus, our findings provide a better understanding of the molecular mechanism of Esrrb in maintaining TSC self-renewal and during iTSC reprogramming.展开更多
Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potent...Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.展开更多
We report on an extensive study of the viscosity of liquid water at near-ambient conditions,performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics(AIMD),based on density...We report on an extensive study of the viscosity of liquid water at near-ambient conditions,performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics(AIMD),based on density-functional theory(DFT).In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy,our ab initio approach is enhanced with deep-neural-network potentials(NNP).This approach is first validated against AIMD results,obtained by using the Perdew–Burke–Ernzerhof(PBE)exchange-correlation functional and paying careful attention to crucial,yet often overlooked,aspects of the statistical data analysis.Then,we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed(SCAN)functional.Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one,our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.展开更多
Despite their rich information content,electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized.We introduce a transferable high-fidelity neural networ...Despite their rich information content,electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized.We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials.This predictive representation of ab initio electronic structure,combined with machinelearning boosted molecular dynamics,enables efficient and accurate electronic evolution and sampling.When it is applied to a one-dimension charge-density wave material,carbyne,we are able to compute the spectral function and optical conductivity in the canonical ensemble.The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit.The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties,paving the way to previously inaccessible or challenging avenues in materials modeling.展开更多
To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and be...To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.展开更多
基金supported by the National Natural Science Foundation of China (61604131,62025403)the Natural Science Foundation of Zhejiang Province (LY19F040009)+1 种基金the Fundamental Research Funds of Zhejiang SciTech University (23062120-Y)the Open Project of Key Laboratory of Solar Energy Utilization and Energy Saving Technology of Zhejiang Province (ZJS-OP-2020-07)
文摘Formamidine lead triiodide(FAPbI_(3))perovskites have become the most promising photovoltaic materials for perovskite solar cells with record power conversion efficiency(PCE).However,random nucleation,phase transition,and lattice defects are still the key challenges limiting the quality of FAPbI_(3) films.Previous studies show that the introduction or adding of seeds in the precursor is effective to promote the nucleation and crystallization of perovskite films.Nevertheless,the seed-assisted approach focuses on heterogeneous seeds or hetero-composites,which inevitably induce a lattice-mismatch,the genera-tion of strain or defects,and the phase segregation in the perovskite films.Herein,we first demonstrate that high-quality perovskite films are controllably prepared using α-and δ-phases mixed FAPbI_(3) micro-crystal as the homogeneous seeds with the one-step antisolvent method.The partially dissolved seeds with suitable sizes improve the crystallinity of the perovskite flm with preferable orientation,improved carrier lifetime,and increased carrier mobility.More importantly,the α-phase-containing seeds promote the formation of α-phase FAPbI_(3) films,leading to the reduction of residual lattice strain and the suppres-sion of I-ion migration.Besides,the adding of dimethyl 2,6-pyridine dicarboxylate(DPD)into the pre-cursor further suppresses the generation of defects,contributing to the PCE of devices prepared in air ambient being significantly improved to 23.75%,among the highest PCEs for fully air-processed FAPbI_(3) solar cells.The unpackaged target devices possess a high stability,maintaining 80%of the initial PCE under simulated solar illumination exceeding 800 h.
文摘Immersive services are the typical emerging services in current IMT-2020 network.With the development of network evolution,real-time interactive applications emerge one after another.This article provides an overview on immersive services which focus on real-time interaction.The scenarios,framework,requirements,key technologies,and issues of interactive immersive service are presented.
基金supported by the National Key Research and Development Program(2021YFB2500210)the Beijing Municipal Natural Science Foundation(Z20J00043)+4 种基金the National Natural Science Foundation of China(22109086 and 21825501)the China Postdoctoral Science Foundation(2021TQ0161 and 2021 M691709)the Guoqiang Institute at Tsinghua University(2020GQG1006)the support from the Shuimu Tsinghua Scholar Program of Tsinghua Universitythe support from the Tsinghua National Laboratory for Information Science and Technology for theoretical simulations。
文摘Solid-state batteries have received increasing attention in scientific and industrial communities,which benefits from the intrinsically safe solid electrolytes(SEs).Although much effort has been devoted to designing SEs with high ionic conductivities,it is extremely difficult to fully understand the ionic diffusion mechanisms in SEs through conventional experimental and theoretical methods.Herein,the temperature-dependent concerted diffusion mechanism of ions in SEs is explored through machinelearning molecular dynamics,taking Li_(10)GeP_(2)S_(12) as a prototype.Weaker diffusion anisotropy,more disordered Li distributions,and shorter residence time are observed at a higher temperature.Arrhenius-type temperature dependence is maintained within a wide temperature range,which is attributed to the linear temperature dependence of jump frequencies of various concerted diffusion modes.These results provide a theoretical framework to understand the ionic diffusion mechanisms in SEs and deepen the understanding of the chemical origin of temperature-dependent concerted diffusions in SEs.
基金the National Natural Science Foundation of China(Grant No.11871110)the National Key Research and Development Program of China(Grant Nos.2016YFB0201200 and 2016YFB0201203)Beijing Academy of Artificial Intelligence(BAAI).
文摘Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface(PES)is the philosopher's stone for unraveling the nature of matter via atomistic simulation.This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES.In this work,we develop an accurate PES model for the Al-Cu-Mg system by employing deep potential(DP),a neural network based representation of the PES,and DP generator(DP-GEN),a concurrent-learning scheme that generates a compact set of ab initio data for training.The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties,including formation energy,equilibrium volume,equation of state,interstitial energy,vacancy and surface formation energy,as well as elastic moduli.Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.
文摘Objective To evaluate the prevalence,awareness,treatment and control rate of hypertension among elder population in China.Methods Data form a cross-sectional stratified multistage random sampling survey conducted from 2012 to 2015 were used to analyzed.Finally,a total of 134,397 participants aged≥60 years were enrolled in our study.Hypertension was defined as systolic BP≥140 mmHg,and/or diastolic BP≥90 mmHg,and/or use of antihypertensive medicine within 2 weeks.Among participants with hypertension,control rate of hypertension was defined as the participant presenting as hypertensive,but with a systolic BP measure less than 140 mm Hg and diastolic BP measure less than 90 mm Hg.
基金supported by the National Natural Science Foundation of China(52035009,52005243)the Science and Technology Innovation Committee of Shenzhen Municipality(JCYJ20200109141003910,GJHZ20180928155412525)。
文摘This paper proposes a method for the rapid detection of subsurface damage(SSD)of Si C using atmospheric inductivity coupled plasma.As a plasma etching method operated at ambient pressure with no bias voltage,this method does not introduce any new SSD to the substrate.Plasma diagnosis and simulation are used to optimize the detection operation.Assisted by an Si C cover,a taper can be etched on the substrate with a high material removal rate.Confocal laser scanning microscopy and scanning electron microscope are used to analyze the etching results,and scanning transmission electron microscope(STEM)is adopted to confirm the accuracy of this method.The STEM result also indicates that etching does not introduce any SSD,and the thoroughly etched surface is a perfectly single crystal.A rapid SSD screening ability is also demonstrated,showing that this method is a promising approach for the rapid detection of SSD.
文摘Objective To investigate the prevalence of hypertension overweight/obesity and the combined effect on the incidence of cardiovascular disease(CVD).Methods The study population(aged from 35 to 64)were selected from 9 regions of China by cluster sampling method.The baseline was conducted in 2010,and the follow-up survey was done in 2017.Participants with 24≤BMI28 kg/m^2 was defined as overweight,BMI≥28 kg/m^2 was defined as obesity.
文摘Objective To evaluate the effect of a workplace-based comprehensive intervention strategy on the improvement of blood pressure (BP) control.Methods A cluster controlled trail, with workplaces (clusters)assigned to either the intervention or control group. Totally, 30 statedowned enterprises across China were included, among which 20were allocated to the intervention group and 10 to the control group.
文摘Objective To determine whether a workplace-based multicomponent intervention strategy could improve BP control among Chinese working population.Methods A cluster-controlled trail,with workplaces assigned to either the intervention or control group.60 workplaces across 20 urban regions of China were selected.4,548 hypertensive employees aged 18-60 years were assigned intervention(n=3,470)or control(n=1,078),of whom 4,205(92.5%;intervention,n=3,209;control,n=996)were included in this analysis.
基金supported by the National Key Research and Development Program of China(2022YFA1004302)
文摘Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.
基金Project supported by the National Natural Science Foundation of China （Nos. 51278221 and 51378076）, the Chinese Postdoc- toral Science Foundation （Nos. 2015M571369 and 2012M511343）, and Jilin Science and Technology Development Program, China （Nos. 20140204027SF and 20170101155JC）
文摘A new force is introduced in the social force model （SFM） for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.
基金the National Key R&D Program of China (2016YFA0100400)the National Natural Science Foundation of China (31721003)+6 种基金the Ministry of Science and Technology of China (2015CB964800, 2015CB964503, and 2018YFA0108900)the National Natural Science Foundation of China (81630035, 31871446, and 31771646)the Shanghai Rising-Star Program (17QA1404200)the Shanghai Chenguang Program (16CG17)the Shanghai Municipal Medical and Health Discipline Construction Projects (2017ZZ02015)National Postdoctoral Program for Innovative Talents (BX201700307)China Postdoctoral Science Foundation (2017M621527).
文摘Trophoblast stem cells (TSCs), which can be derived from the trophoectoderm of a blastocyst, have the ability to sustain self-renewal and differentiate into various placental trophoblast cell types. Meanwhile, essential insights into the molecular mechanisms controlling the placental development can be gained by using TSCs as the cell model. Esrrb is a transcription factor that has been shown to play pivotal roles in both embryonic stem cell (ESC) and TSC, but the precise mechanism whereby Esrrb regulates TSC-specific transcriptome during differentiation and reprogramming is still largely unknown. In the present study, we elucidate the function of Esrrb in self-renewal and differentiation of TSCs, as well as during the induced TSC (iTSC) reprogramming. We demonstrate that the precise level of Esrrb is critical for stem state maintenance and further trophoblast differentiation of TSCs, as ectopically expressed Esrrb can partially block the rapid differentiation of TSCs in the absence of fibroblast growth factor 4. However, Esrrb depletion results in downregulation of certain key TSC-specific transcription factors, consequently causing a rapid differentiation of TSCs and these Esrrb-deficient TSCs lose the ability of hemorrhagic lesion formation in vivo. This function of Esrrb is exerted by directly binding and activating a core set of TSC-specific target genes including Cdx2, Eomes, Sox2, Fgfr4, and Bmp4. Furthermore, we show that Esrrb overexpression can facilitate the MEF-to-iTSC conversion. Moreover, Esrrb can substitute for Eomes to generate GEsTM-iTSCs. Thus, our findings provide a better understanding of the molecular mechanism of Esrrb in maintaining TSC self-renewal and during iTSC reprogramming.
基金This work is supported by the Research Grants Council,Hong Kong SAR through the Collaborative Research Fund under project number 8730054Early Career Scheme Fund under project number 21205019+1 种基金T.Q.W.acknowledges the support of the Hong Kong institute for Advanced Study,City University of Hong Kong through a postdoctoral fellowship.The work of H.W.is supported by the National Science Foundation of China under Grant No.11871110the Beijing Academy of Artificial Intelligence(BAAI).L.F.Z.acknowledges the support of the BAAI.We are also grateful for Dr.Wanrun Jiang,Fengbo Yuan,and Denghui Lu for helpful discussions on the training,free energy calculations,and model compression.
文摘Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.
基金This work was partially funded by the EU through the MAX Centre of Excellence for supercomputing applications(Project No.824143)the Italian MUR,through the PRIN grant FERMATThe work at Princeton University was supported by the Computational Chemical Sciences Center“Chemistry in Solution and at Interfaces”funded by the US Department of Energy under Award No.DE-SC0019394.
文摘We report on an extensive study of the viscosity of liquid water at near-ambient conditions,performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics(AIMD),based on density-functional theory(DFT).In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy,our ab initio approach is enhanced with deep-neural-network potentials(NNP).This approach is first validated against AIMD results,obtained by using the Perdew–Burke–Ernzerhof(PBE)exchange-correlation functional and paying careful attention to crucial,yet often overlooked,aspects of the statistical data analysis.Then,we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed(SCAN)functional.Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one,our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.
基金supported by the National Natural Science Foundation of China(11725415 and 11934001)the Ministry of Science and Technology of China(2018YFA0305601 and2016YFA0301004)+1 种基金by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB28000000)supported in part by the Center for Chemistry in Solution and at Interfaces(CSI)at Princeton University,funded by the DOE Award DE-SC0019394。
文摘Despite their rich information content,electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized.We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials.This predictive representation of ab initio electronic structure,combined with machinelearning boosted molecular dynamics,enables efficient and accurate electronic evolution and sampling.When it is applied to a one-dimension charge-density wave material,carbyne,we are able to compute the spectral function and optical conductivity in the canonical ensemble.The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit.The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties,paving the way to previously inaccessible or challenging avenues in materials modeling.
基金T W and D J S gratefully acknowledge the support of the Research Grants Council,Hong Kong SAR,through the Collaborative Research Fund Project No.8730054The work of H W is supported by the National Science Foundation of China under Grant Nos.11871110 and 12122103The work of W E is supported in part by a gift from iFlytek to Princeton University。
文摘To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
