The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a compreh...The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials.We outline architectures,representations,conditioning mechanisms,data sources,metrics,and applications,and organize existing models into a unified taxonomy.展开更多
Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with a...Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.展开更多
There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and str...There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.展开更多
One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up bot...One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.At present,we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.We provide a comprehensive overview and analysis of the most recent research in this topic.As a starting point,we introduce machine learning principles,algorithms,descriptors,and databases in materials science.We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure.Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning.We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications.Two major questions are always the interpretability of and the physical understanding gained from machine learning models.We consider therefore the different facets of interpretability and their importance in materials science.Finally,we propose solutions and future research paths for various challenges in computational materials science.展开更多
We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids.First,we use the large materials data set that we...We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids.First,we use the large materials data set that we have recently proposed to benchmark 21 different functionals,with a particular focus on approximations of the meta-generalized-gradient family.Combining these data with the results for 12 functionals in our previous work,we can analyze in detail the characteristics of each approximation and identify its strong and/or weak points.Beside confirming that mBJ,HLE16 and HSE06 are the most accurate functionals for band gap calculations,we reveal several other interesting functionals,chief among which are the local Slater potential approximation,the GGA AK13LDA,and the meta-GGAs HLE17 and TASK.We also compare the computational efficiency of these different approximations.Relying on these data,we investigate the potential for improvement of a promising subset of functionals by varying their internal parameters.The identified optimal parameters yield a family of functionals fitted for the calculation of band gaps.Finally,we demonstrate how to train machine learning models for accurate band gap prediction,using as input structural and composition data,as well as approximate band gaps obtained from density-functional theory.展开更多
We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ_(3) where X and Z are metals,and Y is H,B,C,N,O,and P.Our starting point are elect...We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ_(3) where X and Z are metals,and Y is H,B,C,N,O,and P.Our starting point are electron–phonon calculations for 397 materials performed with density-functional perturbation theory.While 43%of the materials are dynamically unstable,we discovered 16 compounds close to thermodynamic stability and with T_(c) higher than 5 K.Using these results to train interpretable machine-learning models,leads us to predict a further 57(thermodynamically unstable)materials with superconducting transition temperatures above 5 K,reaching a maximum of 17.8 K for PtHBe3.Furthermore,the models give us an understanding of the mechanism of superconductivity in antiperovskites.The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials and is easily extendable to other families of compounds or physical properties.展开更多
We propose an efficient high-throughput scheme for the discovery of stable crystalline phases.Our approach is based on the transmutation of known compounds,through the substitution of atoms in the crystal structure wi...We propose an efficient high-throughput scheme for the discovery of stable crystalline phases.Our approach is based on the transmutation of known compounds,through the substitution of atoms in the crystal structure with chemically similar ones.The concept of similarity is defined quantitatively using a measure of chemical replaceability,extracted by data-mining experimental databases.In this way we build 189,981 possible crystal phases,including 18,479 that are on the convex hull of stability.The resulting success rate of 9.72%is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials,and comparable with speed-up factors of machine learning filtering procedures.As a characterization of the set of 18,479 stable compounds,we calculate their electronic band gaps,magnetic moments,and hardness.Our approach,that can be used as a filter on top of any high-throughput scheme,enables us to efficiently extract stable compounds from tremendously large initial sets,without any initial assumption on their crystal structures or chemical compositions.展开更多
Heusler compounds attract a great deal of attention from researchers thanks to a wealth of interesting properties,among which is superconductivity.Here we perform an extensive study of the superconducting and elastic ...Heusler compounds attract a great deal of attention from researchers thanks to a wealth of interesting properties,among which is superconductivity.Here we perform an extensive study of the superconducting and elastic properties of the cubic(full-)Heusler family using a mixture of ab initio methods,as well as interpretable and predictive machine-learning models.By analyzing the statistical distributions of these properties and comparing them to anti-perovskites,we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family.In total,we discover a total of eight hypothetical materials with critical temperatures above 10 K to be compared with the current record of T_(c)=4.7 K in this family.Furthermore,we expect most of these materials to be highly ductile,making them potential candidates for the manufacture of wires and tapes for superconducting magnets.展开更多
Garnets have found important applications in modern technologies including magnetorestriction,spintronics,lithium batteries,etc.The overwhelming majority of experimentally known garnets are oxides,while explorations(e...Garnets have found important applications in modern technologies including magnetorestriction,spintronics,lithium batteries,etc.The overwhelming majority of experimentally known garnets are oxides,while explorations(experimental or theoretical)for the rest of the chemical space have been limited in scope.A key issue is that the garnet structure has a large primitive unit cell,requiring a substantial amount of computational resources.To perform a comprehensive search of the complete chemical space for new garnets,we combine recent progress in graph neural networks with high-throughput calculations.We apply the machine learning model to identify the potentially(meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions.We discover more than 600 ternary garnets with distances to the convex hull below 100 meV⋅atom−1.This includes sulfide,nitride,and halide garnets.We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance.展开更多
In this work,we present a large-scale study of gap deformation potentials based on density-functional theory calculations for over 5000 semiconductors.As expected,in most cases the band gap decreases for increasing vo...In this work,we present a large-scale study of gap deformation potentials based on density-functional theory calculations for over 5000 semiconductors.As expected,in most cases the band gap decreases for increasing volume with deformation potentials that can reach values of almost−15 eV.We find,however,also a sizeable number of materials with positive deformation potentials.Notorious members of this group are halide perovskites,known for their applications in photovoltaics.We then focus on understanding the physical reasons for so different values of the deformation potentials by investigating the correlations between this property and a large number of other material and compositional properties.We also train explainable machine learning models as well as graph convolutional networks to predict deformation potentials and establish simple rules to understand predicted values.Finally,we analyze in more detail a series of materials that have record positive and negative deformation potentials.展开更多
The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments s...The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far.Here,we propose a family of compounds,of composition Mg_(2)XH_(6)with X=Rh,Ir,Pd,or Pt,that achieves this goal.These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow.We predict that their superconducting transition temperatures are in the range of 45–80 K,or even above 100 K with appropriate electron doping of the Pt compound.These results indicate that,although very rare,high-temperature superconductivity in hydrides is achievable at room pressure.展开更多
文摘The rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and,in some cases,even predict desired properties.This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials.We outline architectures,representations,conditioning mechanisms,data sources,metrics,and applications,and organize existing models into a unified taxonomy.
基金funding from the Horizon Europe MSCA Doctoral network grant n.101073486, EUSpecLabfunded by the European Union, and from the Novo Nordisk Foundation Data Science Research Infrastructure 2022 Grant: A high-performance computing infrastructure for data-driven research on sustainable energy materials, Grant no. NNF22OC0078009+1 种基金F.N. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 899987K.S.T. is a Villum Investigator supported by VILLUM FONDEN (grant no. 37789).
文摘Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.
基金funding from the Horizon Europe MSCA Doctoral network grant n.101073486, EUSpecLab, funded by the European UnionS.B. and D.S. acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the project BO4280/11-1. H.C.W and M.A.L.M would like to thank the NHR Center PC2 for providing computing time on the Noctua 2 supercomputers.
文摘There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.
文摘One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.At present,we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.We provide a comprehensive overview and analysis of the most recent research in this topic.As a starting point,we introduce machine learning principles,algorithms,descriptors,and databases in materials science.We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure.Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning.We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications.Two major questions are always the interpretability of and the physical understanding gained from machine learning models.We consider therefore the different facets of interpretability and their importance in materials science.Finally,we propose solutions and future research paths for various challenges in computational materials science.
基金M.A.L.M.and S.B.acknowledge partial support from the DFG through the projects TRR 227,SFB 1375,FOR 2857,BO 4280/8-1,and MA 6787/9-1.
文摘We conducted a large-scale density-functional theory study on the influence of the exchange-correlation functional in the calculation of electronic band gaps of solids.First,we use the large materials data set that we have recently proposed to benchmark 21 different functionals,with a particular focus on approximations of the meta-generalized-gradient family.Combining these data with the results for 12 functionals in our previous work,we can analyze in detail the characteristics of each approximation and identify its strong and/or weak points.Beside confirming that mBJ,HLE16 and HSE06 are the most accurate functionals for band gap calculations,we reveal several other interesting functionals,chief among which are the local Slater potential approximation,the GGA AK13LDA,and the meta-GGAs HLE17 and TASK.We also compare the computational efficiency of these different approximations.Relying on these data,we investigate the potential for improvement of a promising subset of functionals by varying their internal parameters.The identified optimal parameters yield a family of functionals fitted for the calculation of band gaps.Finally,we demonstrate how to train machine learning models for accurate band gap prediction,using as input structural and composition data,as well as approximate band gaps obtained from density-functional theory.
基金T.F.T.C.acknowledges the financial support from the CFisUC through the project UIDB/04564/2020 and the Laboratory for Advanced Computing at the University of Coimbra for providing HPC resources that have contributed to the research results reported within this paper.
文摘We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ_(3) where X and Z are metals,and Y is H,B,C,N,O,and P.Our starting point are electron–phonon calculations for 397 materials performed with density-functional perturbation theory.While 43%of the materials are dynamically unstable,we discovered 16 compounds close to thermodynamic stability and with T_(c) higher than 5 K.Using these results to train interpretable machine-learning models,leads us to predict a further 57(thermodynamically unstable)materials with superconducting transition temperatures above 5 K,reaching a maximum of 17.8 K for PtHBe3.Furthermore,the models give us an understanding of the mechanism of superconductivity in antiperovskites.The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials and is easily extendable to other families of compounds or physical properties.
基金S.B.and M.A.L.M.acknowledge financial support from the DFG through Projects MA 6787/1-1,and BO 4280/8.
文摘We propose an efficient high-throughput scheme for the discovery of stable crystalline phases.Our approach is based on the transmutation of known compounds,through the substitution of atoms in the crystal structure with chemically similar ones.The concept of similarity is defined quantitatively using a measure of chemical replaceability,extracted by data-mining experimental databases.In this way we build 189,981 possible crystal phases,including 18,479 that are on the convex hull of stability.The resulting success rate of 9.72%is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials,and comparable with speed-up factors of machine learning filtering procedures.As a characterization of the set of 18,479 stable compounds,we calculate their electronic band gaps,magnetic moments,and hardness.Our approach,that can be used as a filter on top of any high-throughput scheme,enables us to efficiently extract stable compounds from tremendously large initial sets,without any initial assumption on their crystal structures or chemical compositions.
基金T.F.T.C.and P.B.acknowledge financial support from Fundação para a Ciência e Tecnologia(FCT),Portugal(projects UIDB/04564/2020 and 2022.09975.PTDC and contract 2020.04225.CEECIND)the Laboratory for Advanced Computing at University of Coimbra for providing HPC resources that have contributed to the research results reported within this paper.
文摘Heusler compounds attract a great deal of attention from researchers thanks to a wealth of interesting properties,among which is superconductivity.Here we perform an extensive study of the superconducting and elastic properties of the cubic(full-)Heusler family using a mixture of ab initio methods,as well as interpretable and predictive machine-learning models.By analyzing the statistical distributions of these properties and comparing them to anti-perovskites,we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family.In total,we discover a total of eight hypothetical materials with critical temperatures above 10 K to be compared with the current record of T_(c)=4.7 K in this family.Furthermore,we expect most of these materials to be highly ductile,making them potential candidates for the manufacture of wires and tapes for superconducting magnets.
文摘Garnets have found important applications in modern technologies including magnetorestriction,spintronics,lithium batteries,etc.The overwhelming majority of experimentally known garnets are oxides,while explorations(experimental or theoretical)for the rest of the chemical space have been limited in scope.A key issue is that the garnet structure has a large primitive unit cell,requiring a substantial amount of computational resources.To perform a comprehensive search of the complete chemical space for new garnets,we combine recent progress in graph neural networks with high-throughput calculations.We apply the machine learning model to identify the potentially(meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions.We discover more than 600 ternary garnets with distances to the convex hull below 100 meV⋅atom−1.This includes sulfide,nitride,and halide garnets.We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance.
基金S.B.acknowledges the financial support from the Volkswagen Stiftung(Momentum)through the project“dandelion”P.B.acknowledges the financial support from the CFisUC through the project UIDB/04564/2020 and FCT under the contract 2020.04225.CEECIND.
文摘In this work,we present a large-scale study of gap deformation potentials based on density-functional theory calculations for over 5000 semiconductors.As expected,in most cases the band gap decreases for increasing volume with deformation potentials that can reach values of almost−15 eV.We find,however,also a sizeable number of materials with positive deformation potentials.Notorious members of this group are halide perovskites,known for their applications in photovoltaics.We then focus on understanding the physical reasons for so different values of the deformation potentials by investigating the correlations between this property and a large number of other material and compositional properties.We also train explainable machine learning models as well as graph convolutional networks to predict deformation potentials and establish simple rules to understand predicted values.Finally,we analyze in more detail a series of materials that have record positive and negative deformation potentials.
基金T.F.T.C acknowledges financial support from FCT-Fundação para a Ciência e Tecnologia,Portugal(projects UIDB/04564/2020 and 2022.09975.PTDC)the Laboratory for Advanced Computing at University of Coimbra for providing HPC resources that have contributed to the research results reported within this paper+3 种基金funding from Horizon Europe MSCA Doctoral network grant n.101073486,EUSpecLab,funded by the European Union,and from the Keele Foundationfunding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(Grant Agreement No.802533)acknowledge PRACE for awarding access to the EuroHPC supercomputer LUMI located in CSC’s data center in Kajaani,Finland through EuroHPC Joint Undertaking(EHPC-REG-2022R03-090).funding from the Spanish Ministry of Science and Innovation(Grant No.PID2022-142861NA-I00)the Department of Education,Universities and Research of the Basque Government and the University of the Basque Country(Grant No.IT1527-22).
文摘The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far.Here,we propose a family of compounds,of composition Mg_(2)XH_(6)with X=Rh,Ir,Pd,or Pt,that achieves this goal.These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow.We predict that their superconducting transition temperatures are in the range of 45–80 K,or even above 100 K with appropriate electron doping of the Pt compound.These results indicate that,although very rare,high-temperature superconductivity in hydrides is achievable at room pressure.
