The rapid advancements in ultrafast laser technology have paved the way forpumping and probing the out-of-equilibrium dynamics of nuclei in crystals.However,interpreting these experiments is extremely challenging due ...The rapid advancements in ultrafast laser technology have paved the way forpumping and probing the out-of-equilibrium dynamics of nuclei in crystals.However,interpreting these experiments is extremely challenging due to the complex nonlinear responses in systems where lattice excitations interact,particularly in crystals composed of light atoms or at low temperatures where the quantum nature of ions becomes significant.In this work,we address the nonequilibrium quantum ionic dynamics from first principles.Our approach is general and can be applied to simulate any crystal,in combination with a first-principles treatment of electrons or external machine-learning potentials.It is implemented by leveraging the nonequilibrium time-dependent self-consistent harmonic approximation(TD-SCHA),with a stable,energy-conserving,correlated stochastic integration scheme that achieves an accuracy of O(dt^(3)).We benchmark the method with both a simple onedimensional model to test its accuracy and a realistic 40-atom cell of SrTiO_(3)under THz laser pump,paving the way for simulations of ultrafast THz-Xraypump-probe spectroscopy like those performed in synchrotron facilities.展开更多
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when ap...Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model.This severely limits the practical application of these models in systems with dynamics governed by important rare events,such as chemical reactions and diffusion.We present an adaptive Bayesian inference method for automating the training of interpretable,low-dimensional,and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework,the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model.The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency,while requiring a minimal amount of ab initio training data.We provide a fully opensource implementation of our method,as well as a procedure to map trained models to computationally efficient tabulated force fields.展开更多
Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic ...Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant,but rotationally-covariant to the coordinate of the atoms.We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems,but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.Finally,we use our framework to perform an MD simulation of Li7P3S11,a superionic conductor,and show that resulting Li diffusion coefficient is within 14%of that obtained directly from AIMD.The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.展开更多
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.This allo...We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.This allows for automated active learning of models combining near-quantum accuracy,built-in uncertainty,and constant cost of evaluation that is comparable to classical analytical models,capable of simulating millions of atoms.Using this approach,we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer.We discover an unusual phase transformation mechanism of 2D stanene,where ripples lead to nucleation of bilayer defects,densification into a disordered multilayer structure,followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature.The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.展开更多
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been rec...Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been recently developed to train force fields efficiently and automatically.Among them,Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions.In this work,we present a general Bayesian active learning workflow,where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors.To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation,we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude.We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide(SiC)polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured.The resulting model exhibits close agreement with both ab initio calculations and experimental measurements,and outperforms existing empirical models on vibrational and thermal properties.The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.展开更多
This work examines challenges associated with the accuracy of machine-learned force fields(MLFFs)for bulk solid and liquid phases of d-block elements.In exhaustive detail,we contrast the performance of force,energy,an...This work examines challenges associated with the accuracy of machine-learned force fields(MLFFs)for bulk solid and liquid phases of d-block elements.In exhaustive detail,we contrast the performance of force,energy,and stress predictions across the transition metals for two leading MLFF models:a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes(FLARE),and an equivariant message-passing neural network(NequIP).Early transition metals present higher relative errors and aremore difficult to learn relative to late platinum-and coinage-group elements,and this trend persists across model architectures.Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution.Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level,we determine that the large,sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex,harder-to-learn potential energy surface for these metals.Increasing the fictitious electronic temperature(smearing)modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn.This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals,aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.展开更多
Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales.Theoretically grounded bottom-up models are appealing due to their thermodynamic con...Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales.Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models.In this direction,machine learning approaches hold great promise to fitting complex many-body data.However,training models may require collection of large amounts of expensive data.Moreover,quantifying trained model accuracy is challenging,especially in cases of non-trivial free energy configurations,where training data may be sparse.We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces.Specifically,we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems.Uncertainties also characterize models’accuracy of free energy predictions,even when training is performed only on forces.This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models.展开更多
基金funded by the Swiss National Science Foundation(SNSF,mobility fellowship P500PT\_217861)the Department of Navy award N00014-20-1-2418 issued by the Office of Naval Research and Robert Bosch LLC.L.M.thanks the European Union under the program Horizon 2020 for the award and funding of the MSCA individual fellowship(grant number 101018714)Computational resources were provided by the FAS Division of Science Research Computing Group at Harvard University.
文摘The rapid advancements in ultrafast laser technology have paved the way forpumping and probing the out-of-equilibrium dynamics of nuclei in crystals.However,interpreting these experiments is extremely challenging due to the complex nonlinear responses in systems where lattice excitations interact,particularly in crystals composed of light atoms or at low temperatures where the quantum nature of ions becomes significant.In this work,we address the nonequilibrium quantum ionic dynamics from first principles.Our approach is general and can be applied to simulate any crystal,in combination with a first-principles treatment of electrons or external machine-learning potentials.It is implemented by leveraging the nonequilibrium time-dependent self-consistent harmonic approximation(TD-SCHA),with a stable,energy-conserving,correlated stochastic integration scheme that achieves an accuracy of O(dt^(3)).We benchmark the method with both a simple onedimensional model to test its accuracy and a realistic 40-atom cell of SrTiO_(3)under THz laser pump,paving the way for simulations of ultrafast THz-Xraypump-probe spectroscopy like those performed in synchrotron facilities.
基金B.K.acknowledges generous gift funding support from Bosch Research and partial support from the National Science Foundation under Grant No.1808162L.S.was supported by the Integrated Mesoscale Architectures for Sustainable Catalysis(IMASC),an Energy Frontier Research Center funded by the U.S.Department of Energy,Office of Science,Basic Energy Sciences under Award#DE-SC0012573A.M.K.and S.B.acknowledge funding from the MIT-Skoltech Center for Electrochemical Energy Storage.S.B.T.is supported by the Department of Energy Computational Science Graduate Fellowship under grant DE-FG02-97ER25308.
文摘Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model.This severely limits the practical application of these models in systems with dynamics governed by important rare events,such as chemical reactions and diffusion.We present an adaptive Bayesian inference method for automating the training of interpretable,low-dimensional,and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework,the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model.The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency,while requiring a minimal amount of ab initio training data.We provide a fully opensource implementation of our method,as well as a procedure to map trained models to computationally efficient tabulated force fields.
基金This work was performed in and funded by Bosch Research and Technology Center.This work was partially supported by ARPA-E Award No.DE-AR0000775This research used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory,which is supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725C.W.P.and C.W.also acknowledge financial assistance from Award No.70NANB14H012 from US Department of Commerce,National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design(CHiMaD)and the Toyota Research Institute(TRI).The authors also thank Eric Isaacs and Yizhou Zhu for helpful discussion。
文摘Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant,but rotationally-covariant to the coordinate of the atoms.We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems,but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.Finally,we use our framework to perform an MD simulation of Li7P3S11,a superionic conductor,and show that resulting Li diffusion coefficient is within 14%of that obtained directly from AIMD.The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
基金Y.X.is supported by the US Department of Energy(DOE)Office of Basic Energy Sciences under Award No.DE-SC0020128L.S.is supported by the Integrated Mesoscale Architectures for Sustainable Catalysis(IMASC),an Energy Frontier Research Center funded by the US Department of Energy(DOE)Office of Basic Energy Sciences under Award No.DE-SC0012573+1 种基金A.C.is supported by the Harvard Quantum InitiativeJ.V.is supported by Robert Bosch LLC and the National Science Foundation(NSF),Office of Advanced Cyberinfrastructure,Award No.2003725.
文摘We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.This allows for automated active learning of models combining near-quantum accuracy,built-in uncertainty,and constant cost of evaluation that is comparable to classical analytical models,capable of simulating millions of atoms.Using this approach,we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer.We discover an unusual phase transformation mechanism of 2D stanene,where ripples lead to nucleation of bilayer defects,densification into a disordered multilayer structure,followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature.The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.
基金YX was supported from the US Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences(BES)under Award No.DE-SC0020128JV was supported by the National Science Foundation award number 2003725AJ was supported by the Aker scholarship.
文摘Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been recently developed to train force fields efficiently and automatically.Among them,Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions.In this work,we present a general Bayesian active learning workflow,where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors.To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation,we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude.We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide(SiC)polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured.The resulting model exhibits close agreement with both ab initio calculations and experimental measurements,and outperforms existing empirical models on vibrational and thermal properties.The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.
基金supported by the US Department of Energy,Office of Basic Energy Sciences Award No.DE-SC0022199 and No.DE-SC0020128,as well as by Robert Bosch LLCC.J.O.is supported by the National Science Foundation Graduate Research Fellowship Program under Grant No.DGE1745303S.B.T.was supported by the Department of Energy Computational Science Graduate Fellowship under grant DE-FG02-97ER25308.
文摘This work examines challenges associated with the accuracy of machine-learned force fields(MLFFs)for bulk solid and liquid phases of d-block elements.In exhaustive detail,we contrast the performance of force,energy,and stress predictions across the transition metals for two leading MLFF models:a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes(FLARE),and an equivariant message-passing neural network(NequIP).Early transition metals present higher relative errors and aremore difficult to learn relative to late platinum-and coinage-group elements,and this trend persists across model architectures.Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution.Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level,we determine that the large,sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex,harder-to-learn potential energy surface for these metals.Increasing the fictitious electronic temperature(smearing)modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn.This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals,aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.
基金supported by a NASA Space Technology Graduate Research Opportunityby the NSF through the Harvard University Materials Research Science and Engineering Center Grant No.DMR-2011754by a Multidisciplinary University Research Initiative sponsored by the Office of Naval Research,under Grant N00014-20-1-2418.
文摘Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales.Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models.In this direction,machine learning approaches hold great promise to fitting complex many-body data.However,training models may require collection of large amounts of expensive data.Moreover,quantifying trained model accuracy is challenging,especially in cases of non-trivial free energy configurations,where training data may be sparse.We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces.Specifically,we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems.Uncertainties also characterize models’accuracy of free energy predictions,even when training is performed only on forces.This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models.
