Pressure-induced phase transformations in materials are of interest in a range of fields,including geophysics,planetary sciences,and shock physics.In addition,the high-pressure phases can exhibit desirable properties,...Pressure-induced phase transformations in materials are of interest in a range of fields,including geophysics,planetary sciences,and shock physics.In addition,the high-pressure phases can exhibit desirable properties,eliciting interest in materials science.Despite its importance,the process of finding new high-pressure phases,either experimentally or computationally,is time-consuming and often driven by intuition.In this study,we use graph neural networks trained on density functional theory(DFT)equation of state data of 2258 materials and 7255 phases to identify potential phase transitions.The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations.Importantly,the new data is added to the training set,the model is refined,and a new cycle of discovery is started.Within 13 iterations,we discovered 28 new high-pressure stable phases(never synthesized through high-pressure routes nor reported in high-pressure computational works)and rediscovered 18 pressure-induced phase transitions.The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.展开更多
Reactive force fields have enabled an atomic level description of a wide range of phenomena,from chemistry at extreme conditions to the operation of electrochemical devices and catalysis.While significant insight and ...Reactive force fields have enabled an atomic level description of a wide range of phenomena,from chemistry at extreme conditions to the operation of electrochemical devices and catalysis.While significant insight and semi-quantitative understanding have been drawn from such work,the accuracy of reactive force fields limits quantitative predictions.We developed a neural network reactive force field(NNRF)for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine(RDX).NNRF was trained using energies and forces of a total of 3100 molecules(11,941 geometries)and 15 condensed matter systems(32,973 geometries)obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions.The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained.The root mean square(RMS)error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.展开更多
The response of materials to shock loading is important to planetary science,aerospace engineering,and energetic materials.Thermally activated processes,including chemical reactions and phase transitions,are significa...The response of materials to shock loading is important to planetary science,aerospace engineering,and energetic materials.Thermally activated processes,including chemical reactions and phase transitions,are significantly accelerated by energy localization into hotspots.These result from the interaction of the shockwave with the materials’microstructure and are governed by complex,coupled processes,including the collapse of porosity,interfacial friction,and localized plastic deformation.These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone.We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input.The accuracy of the Microstructure-Informed Shock-induced Temperature net(MISTnet)model is higher than the current state of the art and its evaluation requires a fraction of the computation cost.展开更多
Condense phase molecular systems organize in wide range of distinct molecular configurations,including amorphous melt and glass as well as crystals often exhibiting polymorphism,that originate from their intricate int...Condense phase molecular systems organize in wide range of distinct molecular configurations,including amorphous melt and glass as well as crystals often exhibiting polymorphism,that originate from their intricate intra-and intermolecular forces.While accurate coarse-grain(CG)models for these materials are critical to understand phenomena beyond the reach of all-atom simulations,current models cannot capture the diversity of molecular structures.We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks(GNN)and data from an all-atom simulations and apply it to the high-energy density material RDX.We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder.The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces.The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.展开更多
基金support from the U.S.National Science Foundation FAIROS program(award 2226418)and the computational resources from nanoHUB.
文摘Pressure-induced phase transformations in materials are of interest in a range of fields,including geophysics,planetary sciences,and shock physics.In addition,the high-pressure phases can exhibit desirable properties,eliciting interest in materials science.Despite its importance,the process of finding new high-pressure phases,either experimentally or computationally,is time-consuming and often driven by intuition.In this study,we use graph neural networks trained on density functional theory(DFT)equation of state data of 2258 materials and 7255 phases to identify potential phase transitions.The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations.Importantly,the new data is added to the training set,the model is refined,and a new cycle of discovery is started.Within 13 iterations,we discovered 28 new high-pressure stable phases(never synthesized through high-pressure routes nor reported in high-pressure computational works)and rediscovered 18 pressure-induced phase transitions.The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.
基金This work was support by the US Office of Naval Research,Multidisciplinary University Research Initiatives(MURI)Program,Contract:N00014-16-1-2557.
文摘Reactive force fields have enabled an atomic level description of a wide range of phenomena,from chemistry at extreme conditions to the operation of electrochemical devices and catalysis.While significant insight and semi-quantitative understanding have been drawn from such work,the accuracy of reactive force fields limits quantitative predictions.We developed a neural network reactive force field(NNRF)for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine(RDX).NNRF was trained using energies and forces of a total of 3100 molecules(11,941 geometries)and 15 condensed matter systems(32,973 geometries)obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions.The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained.The root mean square(RMS)error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.
基金This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-20-2-0189.This work was supported in part by high-performance computer time and resources from the DoD High-Performance Computing Modernization Program.The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies,either expressed or implied,of the Army Research Laboratory,or the US Government.The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
文摘The response of materials to shock loading is important to planetary science,aerospace engineering,and energetic materials.Thermally activated processes,including chemical reactions and phase transitions,are significantly accelerated by energy localization into hotspots.These result from the interaction of the shockwave with the materials’microstructure and are governed by complex,coupled processes,including the collapse of porosity,interfacial friction,and localized plastic deformation.These mechanisms are not fully understood and the lack of models limits our ability to predict shock to detonation transition from chemistry and microstructure alone.We demonstrate that deep learning can be used to predict the resulting shock-induced temperature fields in composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input.The accuracy of the Microstructure-Informed Shock-induced Temperature net(MISTnet)model is higher than the current state of the art and its evaluation requires a fraction of the computation cost.
基金sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-20-2-0189supported in part by high-performance computer time and resources from the DoD High Performance Computing Modernization Program.
文摘Condense phase molecular systems organize in wide range of distinct molecular configurations,including amorphous melt and glass as well as crystals often exhibiting polymorphism,that originate from their intricate intra-and intermolecular forces.While accurate coarse-grain(CG)models for these materials are critical to understand phenomena beyond the reach of all-atom simulations,current models cannot capture the diversity of molecular structures.We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks(GNN)and data from an all-atom simulations and apply it to the high-energy density material RDX.We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder.The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces.The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.
