We consider a steady-state(but transient)situation in which a warm dense aggregate is a two-temperature system with equilibrium electrons at temperature T_(e),ions at T_(i),and T_(e)≠T_(i).Such states are achievable ...We consider a steady-state(but transient)situation in which a warm dense aggregate is a two-temperature system with equilibrium electrons at temperature T_(e),ions at T_(i),and T_(e)≠T_(i).Such states are achievable by pump–probe experiments.For warm dense hydrogen in such a twotemperature situation,we investigate nuclear quantum effects(NQEs)on structure and thermodynamic properties,thereby delineating the limitations of ordinary ab initio molecular dynamics.We use path integral molecular dynamics(PIMD)simulations driven by orbital-free density functional theory(OFDFT)calculations with state-of-the-art noninteracting free-energy and exchange-correlation functionals for the explicit temperature dependence.We calibrate the OFDFT calculations against conventional(explicit orbitals)Kohn–Sham DFT.We find that when the ratio of the ionic thermal de Broglie wavelength to the mean interionic distance is larger than about 0.30,the ionic radial distribution function is meaningfully affected by the inclusion of NQEs.Moreover,NQEs induce a substantial increase in both the ionic and electronic pressures.This confirms the importance of NQEs for highly accurate equation-of-state data on highly driven hydrogen.For Te>20 kK,increasing Te in the warm dense hydrogen has slight effects on the ionic radial distribution function and equation of state in the range of densities considered.In addition,we confirm that compared with thermostatted ring-polymer molecular dynamics,the primitive PIMD algorithm overestimates electronic pressures,a consequence of the overly localized ionic description from the primitive scheme.展开更多
The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors,including the retardation of the interaction and the Coulomb pseudopotential,to predict the critical temp...The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors,including the retardation of the interaction and the Coulomb pseudopotential,to predict the critical temperature T_(c).McMillan,Allen,and Dynes derived approximate closed-form expressions for the critical temperature within this theory,which depends on the electron–phonon spectral functionα^(2)F(ω).Here we show that modern machine-learning techniques can substantially improve these formulae,accounting for more general shapes of theα^(2)F function.Using symbolic regression and the SISSO framework,together with a database of artificially generatedα^(2)F functions and numerical solutions of the Eliashberg equations,we derive a formula for T_(c)that performs as well as Allen–Dynes for low-T_(c)superconductors and substantially better for higher-T_(c)ones.This corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes.This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.展开更多
Fullerenes,as characteristic carbon nanomaterials,offer significant potential for diverse applications due to their structural diversity and tunable properties.Numerous isomers can exist for a specific fullerene size,...Fullerenes,as characteristic carbon nanomaterials,offer significant potential for diverse applications due to their structural diversity and tunable properties.Numerous isomers can exist for a specific fullerene size,yet a comprehensive understanding of their fundamental properties remains elusive.展开更多
Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms(CSPA).However,the computational cost of the ab initio calculat...Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms(CSPA).However,the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells,reducing the compositional and structural space the algorithms can explore.Past studies have bypassed unneeded ab initio calculations by utilizing machine learning to predict the stability of a material.Specifically,graph neural networks trained on large datasets of relaxed structures display high fidelity in predicting formation energy.Unfortunately,the geometries of structures produced by CSPA deviate from the relaxed state,which leads to poor predictions,hindering the model’s ability to filter unstable material.To remedy this behavior,we propose a simple,physically motivated,computationally efficient perturbation technique that augments training data,improving predictions on unrelaxed structures by 66%.Finally,we show how this error reduction can accelerate CSPA.展开更多
All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational...All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy.To address this challenge,we combine effective two-and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable,sufficiently accurate for applications,as fast as the fastest traditional empirical potentials,and two to four orders of magnitude faster than state-of-the-art machine-learning potentials.For data from empirical potentials,we demonstrate the exact retrieval of the potential.For data from density functional theory,the predicted energies,forces,and derived properties,including phonon spectra,elastic constants,and melting points,closely match those of the reference method.The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.展开更多
Building on the pioneering work of Jean-Marie Andre and working in the laboratory he founded, the authors have developed a code called FT-1D to make Hartree-Fock electronic structure computations for stereoregular pol...Building on the pioneering work of Jean-Marie Andre and working in the laboratory he founded, the authors have developed a code called FT-1D to make Hartree-Fock electronic structure computations for stereoregular polymers using Ewald-type con- vergence acceleration methods. That code also takes full advantage of all line-group symmetries to calculate only the minimal set of two-electron integrals and to optimize the computation of the Fock matrix. The present communication reports a bench- mark study of the FT-1D code using polytetrafluoroethylene （PTFE） as a test case. Our results not only confirm the algorith- mic correctness of the code through agreement with other studies where they are applicable, but also show that the use of con- vergence acceleration enables accurate results to be obtained in situations where other widely-used codes （e.g., PLH and Crys- tal） fail. It is also found that full attention to the line-group symmetry of the PTFE polymer leads to an increase of between one and two orders of magnitude in the speed of computation. The new code can therefore be viewed as extending the range of electronic-structure computations for stereoregular polymers beyond the present scope of the successful and valuable code Crystal.展开更多
The discovery of substrate materials has been dominated by trial and error,opening the opportunity for a systematic search.We generate bonding networks for materials from the Materials Project and systematically break...The discovery of substrate materials has been dominated by trial and error,opening the opportunity for a systematic search.We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals.Successful cleavage reduces the bonding network to two periodic dimensions.We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals,4626 of which have a maximum Miller index of one.We characterize the likelihood of cleavage by creating monolayers of these surfaces and calculating their thermodynamic stability using density functional theory to discover 3991 potential substrates.Following,we identify distinct trends in the work of cleavage and relate them to bonding in the three-dimensional precursor.We illustrate the potential impact of the substrate database by identifying several improved epitaxial substrates for the transparent conductor BaSnO3.The open-source databases of predicted and commercial substrates are available at MaterialsWeb.org.展开更多
基金The majority of this work was done while D.K.was a visitor at the University of Florida.He was supported by the Science Challenge Project of China under Grant No.TZ2016001the NSFC under Grant No.11874424+3 种基金the National Key R&D Program of China under Grant No.2017YFA0403200He also acknowledges support by the China Scholarship Council.K.L.(for the majority of the work done while at the University of Florida)S.B.T.were supported by U.S.Department of Energy Grant No.DE-SC0002139Most of the computations were performed on the HiPerGator-II system at the University of Florida.
文摘We consider a steady-state(but transient)situation in which a warm dense aggregate is a two-temperature system with equilibrium electrons at temperature T_(e),ions at T_(i),and T_(e)≠T_(i).Such states are achievable by pump–probe experiments.For warm dense hydrogen in such a twotemperature situation,we investigate nuclear quantum effects(NQEs)on structure and thermodynamic properties,thereby delineating the limitations of ordinary ab initio molecular dynamics.We use path integral molecular dynamics(PIMD)simulations driven by orbital-free density functional theory(OFDFT)calculations with state-of-the-art noninteracting free-energy and exchange-correlation functionals for the explicit temperature dependence.We calibrate the OFDFT calculations against conventional(explicit orbitals)Kohn–Sham DFT.We find that when the ratio of the ionic thermal de Broglie wavelength to the mean interionic distance is larger than about 0.30,the ionic radial distribution function is meaningfully affected by the inclusion of NQEs.Moreover,NQEs induce a substantial increase in both the ionic and electronic pressures.This confirms the importance of NQEs for highly accurate equation-of-state data on highly driven hydrogen.For Te>20 kK,increasing Te in the warm dense hydrogen has slight effects on the ionic radial distribution function and equation of state in the range of densities considered.In addition,we confirm that compared with thermostatted ring-polymer molecular dynamics,the primitive PIMD algorithm overestimates electronic pressures,a consequence of the overly localized ionic description from the primitive scheme.
基金The work presented here was performed under the auspice of Basic Energy Sciences,United States Department of Energy,contract number DE-SC0020385.
文摘The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors,including the retardation of the interaction and the Coulomb pseudopotential,to predict the critical temperature T_(c).McMillan,Allen,and Dynes derived approximate closed-form expressions for the critical temperature within this theory,which depends on the electron–phonon spectral functionα^(2)F(ω).Here we show that modern machine-learning techniques can substantially improve these formulae,accounting for more general shapes of theα^(2)F function.Using symbolic regression and the SISSO framework,together with a database of artificially generatedα^(2)F functions and numerical solutions of the Eliashberg equations,we derive a formula for T_(c)that performs as well as Allen–Dynes for low-T_(c)superconductors and substantially better for higher-T_(c)ones.This corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes.This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.
基金supported by the University of Florida’s new faculty start-up funding.
文摘Fullerenes,as characteristic carbon nanomaterials,offer significant potential for diverse applications due to their structural diversity and tunable properties.Numerous isomers can exist for a specific fullerene size,yet a comprehensive understanding of their fundamental properties remains elusive.
基金This work was supported by the National Science Foundation under grants Nos.PHY-1549132the Center for Bright Beams,and the software fellowship awarded to J.B.G.by the Molecular Sciences Software Institute funded by the National Science Foundation(Grant No.ACI-1547580).
文摘Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms(CSPA).However,the computational cost of the ab initio calculations required by CSPA limits its utility to small unit cells,reducing the compositional and structural space the algorithms can explore.Past studies have bypassed unneeded ab initio calculations by utilizing machine learning to predict the stability of a material.Specifically,graph neural networks trained on large datasets of relaxed structures display high fidelity in predicting formation energy.Unfortunately,the geometries of structures produced by CSPA deviate from the relaxed state,which leads to poor predictions,hindering the model’s ability to filter unstable material.To remedy this behavior,we propose a simple,physically motivated,computationally efficient perturbation technique that augments training data,improving predictions on unrelaxed structures by 66%.Finally,we show how this error reduction can accelerate CSPA.
基金S.R.X.and R.G.H.were supported by the United States Department of Energy under contract number DE-SC0020385R.G.H.was supported by the U.S.National Science Foundation under contract number DMR 2118718+3 种基金M.R.acknowledges partial support by the European Center of Excellence in Exascale Computing TREX-Targeting Real Chemical Accuracy at the Exascalethis project has received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Agreement No.952165Part of the research was performed while the authors visited the Institute for Pure and Applied Mathematics,which is supported by the National Science Foundation(Grant No.DMS 1440415)Computational resources were provided by the University of Florida Research Computing Center.We thank Ajinkya Hire for the implementation of UF potentials in LAMMPS and Alexander Shapeev for fitting the MTP potentials.We thank Thomas Bischoff,Jason Gibson,Bastian Jäckl,Hendrik Kraß,Ming Li,Johannes Margraf,Paul-Rene Mayer,Pawan Prakash,Robert Schmid,and Benjamin Walls for testing of and contributing to the UF implementation.
文摘All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy.To address this challenge,we combine effective two-and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable,sufficiently accurate for applications,as fast as the fastest traditional empirical potentials,and two to four orders of magnitude faster than state-of-the-art machine-learning potentials.For data from empirical potentials,we demonstrate the exact retrieval of the potential.For data from density functional theory,the predicted energies,forces,and derived properties,including phonon spectra,elastic constants,and melting points,closely match those of the reference method.The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.
基金FEH was supported by U.S.National Science Foundation Grant PHY-0601758Part of this research has been funded by BELSPO(IAP P7/05 network"Functional Supramolecular Systems")+1 种基金The calculations were performed on the computing facilities of the Consortium deséquipements de Calcul Intensif(CéCI),in particular those of the Plateforme Technologique de Calcul Intensif(PTCI)installed in the University of Namur,for which we gratefully acknowledge financial support of the FNRS-FRFC(Conventions No.2.4.617.07.F and 2.5020.11)the University of Namur
文摘Building on the pioneering work of Jean-Marie Andre and working in the laboratory he founded, the authors have developed a code called FT-1D to make Hartree-Fock electronic structure computations for stereoregular polymers using Ewald-type con- vergence acceleration methods. That code also takes full advantage of all line-group symmetries to calculate only the minimal set of two-electron integrals and to optimize the computation of the Fock matrix. The present communication reports a bench- mark study of the FT-1D code using polytetrafluoroethylene （PTFE） as a test case. Our results not only confirm the algorith- mic correctness of the code through agreement with other studies where they are applicable, but also show that the use of con- vergence acceleration enables accurate results to be obtained in situations where other widely-used codes （e.g., PLH and Crys- tal） fail. It is also found that full attention to the line-group symmetry of the PTFE polymer leads to an increase of between one and two orders of magnitude in the speed of computation. The new code can therefore be viewed as extending the range of electronic-structure computations for stereoregular polymers beyond the present scope of the successful and valuable code Crystal.
基金We thank S.Xie,A.M.Z.Tan,W.DeBenedetti,I.Bazarov,and S.Karkare for helpful discussions.This work was supported by the National Science Foundation under grants Nos.PHY-1549132the Center for Bright Beams,DMR-1542776 and OAC-1740251the UF Informatics Institute.This research used computational resources of the University of Florida Research Computing Center.Part of this research was performed while the author was visiting the Institute for Pure and Applied Mathematics(IPAM),which is supported by the National Science Foundation under grant No.DMS-1440415.
文摘The discovery of substrate materials has been dominated by trial and error,opening the opportunity for a systematic search.We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals.Successful cleavage reduces the bonding network to two periodic dimensions.We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals,4626 of which have a maximum Miller index of one.We characterize the likelihood of cleavage by creating monolayers of these surfaces and calculating their thermodynamic stability using density functional theory to discover 3991 potential substrates.Following,we identify distinct trends in the work of cleavage and relate them to bonding in the three-dimensional precursor.We illustrate the potential impact of the substrate database by identifying several improved epitaxial substrates for the transparent conductor BaSnO3.The open-source databases of predicted and commercial substrates are available at MaterialsWeb.org.
