We present the electronic moment tensor potentials(eMTPs),a class of machine-learning interatomic models and a generalization of the classical MTPs,reproducing both the electronic and vibrational degrees of freedom,up...We present the electronic moment tensor potentials(eMTPs),a class of machine-learning interatomic models and a generalization of the classical MTPs,reproducing both the electronic and vibrational degrees of freedom,up to the accuracy of ab initio calculations.Following the original polynomial interpolation idea of the MTPs,the eMTPs are defined as polynomials of vibrational and electronic degrees of freedom,corrected to have a finite interatomic cutoff.Practically,an eMTP is constructed from the classical MTPs fitted to a training set,whose energies and forces are calculated with electronic temperatures corresponding to the Chebyshev nodes on a given temperature interval.The eMTP energy is hence a Chebyshev interpolation of the classical MTPs.Using the eMTP,one can obtain the temperature-dependent vibrational free energy including anharmonicity coming from phonon interactions,the electronic free energy coming from electron interactions,and the coupling of atomic vibrations and electronic excitations.Each of the contributions can be accessed individually using the proposed formalism.The performance of eMTPs is demonstrated for two refractory systems which have a significant electronic,vibrational and coupling contribution up to the melting point—unary Nb,and a disordered TaVCrW high-entropy alloy.Highly accurate thermodynamic and kinetic quantities can now be obtained just by using eMTPs,without any further ab initio calculations.The proposed construction to include the electronic degree of freedom can also be applied to other machine-learning models.展开更多
Wepresent an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components.It is particularly important for high entropy alloys(HEAs),where multiple ...Wepresent an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components.It is particularly important for high entropy alloys(HEAs),where multiple principal elements can form numerous potential intermetallic compounds during the condensation process,making it challenging to predict the dominant phase.Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice(FCC or BCC)accelerated by machine-learning interatomic potentials.The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures.展开更多
Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge com...Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space.Ab initio calculations have emerged as a powerful approach that complements experiment.However,for multicomponent alloys existing approaches suffer from the chemical complexity involved.In this work we propose a method for studying HEAs computationally.Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations.The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA.The approach is employed to study phase stability,phase transitions,and chemical short-range order.The importance of including local relaxation effects is revealed:they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature.Finally,a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.展开更多
We present the magnetic Moment Tensor Potentials(mMTPs),a class of machine-learning interatomic potentials,accurately reproducing both vibrational and magnetic degrees of freedom as provided,e.g.,from first-principles...We present the magnetic Moment Tensor Potentials(mMTPs),a class of machine-learning interatomic potentials,accurately reproducing both vibrational and magnetic degrees of freedom as provided,e.g.,from first-principles calculations.The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space.The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron,with applications to phonon calculations for different magnetic states,and molecular-dynamics simulations with fluctuating magnetic moments.展开更多
The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensit...The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions,however,has remained a daunting challenge.Recently,employing well-designed site environment descriptors and machine learning(ML),notable progress has been made in predicting the propensity for stress-activated β processes(i.e.,shear transformations)from the static structure.展开更多
The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens a...The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。展开更多
The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines.The vast compositional parameter spac...The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines.The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design.However,as of now a method to compute efficiently,yet with high accuracy the thermodynamic properties of such alloys has been missing.One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys.In this work,a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential.We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.展开更多
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(grant agreement No 865855)The authors acknowledge support by the state of Baden-Württemberg through bwHPC and the German Research Foundation(DFG)through grant No.INST 40/575-1 FUGG(JUSTUS 2 cluster)and the support by the Stuttgart Center for Simulation Science(SimTech)B.G.and A.S.acknowledge support from the collaborative DFG-RFBR Grant(Grants No.DFG KO 5080/3-1,DFG GR 3716/6-1,and RFBR 20-53-12012).
文摘We present the electronic moment tensor potentials(eMTPs),a class of machine-learning interatomic models and a generalization of the classical MTPs,reproducing both the electronic and vibrational degrees of freedom,up to the accuracy of ab initio calculations.Following the original polynomial interpolation idea of the MTPs,the eMTPs are defined as polynomials of vibrational and electronic degrees of freedom,corrected to have a finite interatomic cutoff.Practically,an eMTP is constructed from the classical MTPs fitted to a training set,whose energies and forces are calculated with electronic temperatures corresponding to the Chebyshev nodes on a given temperature interval.The eMTP energy is hence a Chebyshev interpolation of the classical MTPs.Using the eMTP,one can obtain the temperature-dependent vibrational free energy including anharmonicity coming from phonon interactions,the electronic free energy coming from electron interactions,and the coupling of atomic vibrations and electronic excitations.Each of the contributions can be accessed individually using the proposed formalism.The performance of eMTPs is demonstrated for two refractory systems which have a significant electronic,vibrational and coupling contribution up to the melting point—unary Nb,and a disordered TaVCrW high-entropy alloy.Highly accurate thermodynamic and kinetic quantities can now be obtained just by using eMTPs,without any further ab initio calculations.The proposed construction to include the electronic degree of freedom can also be applied to other machine-learning models.
基金supported by the Russian Science Foundation(grant number 23-13-00332,https://rscf.ru/project/23-13-00332/).
文摘Wepresent an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components.It is particularly important for high entropy alloys(HEAs),where multiple principal elements can form numerous potential intermetallic compounds during the condensation process,making it challenging to predict the dominant phase.Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice(FCC or BCC)accelerated by machine-learning interatomic potentials.The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures.
基金This collaboration might not have been possible had the authors not met at a number of research programs at the Institute of Pure and Applied Mathematics,UCLA.T.K.and A.S.were supported by the Russian Science Foundation(Grant number 18-13-00479)F.K.acknowledges funding from the Deutsche Forschungsgemeinschaft(SPP 2006)+1 种基金the Netherlands Organization for Scientific Research NWO/STW(VIDI grant 15707)J.N.acknowledges financial support by the DFG under project number NE 428/19-1.
文摘Recently,high-entropy alloys(HEAs)have attracted wide attention due to their extraordinary materials properties.A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space.Ab initio calculations have emerged as a powerful approach that complements experiment.However,for multicomponent alloys existing approaches suffer from the chemical complexity involved.In this work we propose a method for studying HEAs computationally.Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations.The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA.The approach is employed to study phase stability,phase transitions,and chemical short-range order.The importance of including local relaxation effects is revealed:they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature.Finally,a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.
基金We acknowledge support from the collaborative DFG-RFBR Grant(Grants no.DFG KO 5080/3-1,DFG GR 3716/6-1,and RFBR 20-53-12012)B.G.acknowledges the support by the Stuttgart Center for Simulation Science(SimTech)and funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No.865855).
文摘We present the magnetic Moment Tensor Potentials(mMTPs),a class of machine-learning interatomic potentials,accurately reproducing both vibrational and magnetic degrees of freedom as provided,e.g.,from first-principles calculations.The accuracy is achieved by a two-step minimization scheme that coarse-grains the atomic and the spin space.The performance of the mMTPs is demonstrated for the prototype magnetic system bcc iron,with applications to phonon calculations for different magnetic states,and molecular-dynamics simulations with fluctuating magnetic moments.
基金Q.W.and E.M.are supported at JHU by U.S.Department of Energy(DOE),DOE-BESDMSE,under grant DE-FG02-19ER46056Q.W.also acknowledges the support of National Natural Science Foundation of China(51701190)+1 种基金J.D.acknowledges the Chinese Thousand-Youth-Talent Program,and the Young Talent Startup Program of Xi’an Jiaotong UniversityA.S.and E.P.are supported by the Russian Science Foundation(grant number 18-13-00479).
文摘The elementary excitations in metallic glasses(MGs),i.e.,β processes that involve hopping between nearby sub-basins,underlie many unusual properties of the amorphous alloys.A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions,however,has remained a daunting challenge.Recently,employing well-designed site environment descriptors and machine learning(ML),notable progress has been made in predicting the propensity for stress-activated β processes(i.e.,shear transformations)from the static structure.
基金The computations involved in this work were conducted on the computer cluster at Skolkovo Institute of Science and Technology(Skoltech)CEST Multiscale Molecular Modelling group and Massachusetts Institute of Technology(MIT)Nuclear Science Engineering department.E.T.,Z.S.,A.S.,and J.L.acknowledge support by the Skoltech-MIT Next Generation Program 2016-7/NGPE.T.and A.S.acknowledge support by the Center for Integrated Nanotechnologies,an Office of Science User Facility operated for the U.S.Department of Energy Office of Science by Los Alamos National Laboratory(Contract 89233218CNA000001)+1 种基金Sandia National Laboratories(Contract DE-NA-0003525)M.D.acknowledges support from MIT J-Clinic for Machine Learning and Health.S.S.acknowledges support from Nanyang Technological University through the Distinguished University Professorship.
文摘The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。
基金We thank Jan Janssen and Konstantin Gubaev for fruitful discussions.Funding by the Deutsche Forschungsgemeinschaft(SPP 2006)the European Research Council(ERC)under the EU’s Horizon 2020 Research and Innovation Programme(Grant no.639211)is gratefully acknowledged+1 种基金F.K.acknowledges NWO/STW(VIDI grant 15707)A.S.was supported by the Russian Science Foundation(Grant no.18-13-00479)。
文摘The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines.The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design.However,as of now a method to compute efficiently,yet with high accuracy the thermodynamic properties of such alloys has been missing.One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys.In this work,a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential.We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.
