Graph neural networks(GNNs)have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications.To date,a number of successful GNNs have been propose...Graph neural networks(GNNs)have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications.To date,a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis.However,a consistent benchmark of these models remains lacking,hindering the development and consistent evaluation of new models in the materials field.Here,we present a workflow and testing platform,MatDeepLearn,for quickly and reproducibly assessing and comparing GNNs and other machine learning models.We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry.From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized.We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs,due to learned rather than defined representations.Meanwhile several weaknesses of GNNs are also observed including high data requirements,and suggestions for further improvement for applications in materials chemistry are discussed.展开更多
With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties ...With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations.Compared to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character.However,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations.With regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference.Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes.We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening studies.To encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.展开更多
Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to t...Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials,more complex edge reconstructions can be realized through careful control over the synthesis conditions.However,the whole family of synthesizable,reconstructed edges remains poorly studied.Here,we develop a computational approach integrating ensemble-generation,force-relaxation,and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties.展开更多
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a comp...The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.展开更多
基金This work was supported by the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy(UNCAGE-ME),an Energy Frontier Research Center funded by the U.S.Department of Energy,Office of Science,Basic Energy Sciences.Work was performed at the Center for Nanophase Materials Sciences,which is a US Department of Energy Office of Science User Facility.V.F.was also supported by a Eugene P.Wigner Fellowship at Oak Ridge National Laboratory.J.Z.was supported by the U.S.Department of Energy,Office of Science,Office of Advanced Scientific Computing Research,Applied Mathematics programand by the Artificial Intelligence Initiative at the Oak Ridge National Laboratory(ORNL).ORNL is operated by UT-Battelle,LLC.,for the U.S.Department of Energy under Contract DEAC05-00OR22725This research used resources of the National Energy Research Scientific Computing Center,supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘Graph neural networks(GNNs)have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications.To date,a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis.However,a consistent benchmark of these models remains lacking,hindering the development and consistent evaluation of new models in the materials field.Here,we present a workflow and testing platform,MatDeepLearn,for quickly and reproducibly assessing and comparing GNNs and other machine learning models.We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry.From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized.We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs,due to learned rather than defined representations.Meanwhile several weaknesses of GNNs are also observed including high data requirements,and suggestions for further improvement for applications in materials chemistry are discussed.
基金A.S.R.acknowledges support via a Miller Research Fellowship from the Miller Institute for Basic Research in Science,University of California,BerkeleyP.H.,C.T.O.,M.K.H.,and K.A.P.acknowledge support by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under Contract No.DE-AC02-05-CH11231(Materials Project program KC23MP)D.G.T.and R.Q.S.acknowledge support from the U.S.Department of Energy,Office of Basic Energy Sciences,Division of Chemical Sciences,Geosciences and Biosciences through the Nanoporous Materials Genome Center under Award Number DE-FG02-17ER16362。
文摘With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations.Compared to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character.However,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations.With regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference.Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes.We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening studies.To encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.
基金This manuscript has been authored by UT-Battelle,LLC under Contract No.DE-AC05-00OR22725 with the U.S.Department of Energy.
文摘Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials,more complex edge reconstructions can be realized through careful control over the synthesis conditions.However,the whole family of synthesizable,reconstructed edges remains poorly studied.Here,we develop a computational approach integrating ensemble-generation,force-relaxation,and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties.
基金This work was performed at the Center for Nanophase Materials Sciences,which is a US Department of Energy Office of Science User Facility.Support was provided by the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy(UNCAGE-ME),an Energy Frontier Research Center funded by U.S.Department of Energy,Office of Science,Basic Energy Sciences.VF was also supported by a Eugene P.Wigner Fellowship at Oak Ridge National Laboratory.JZ was supported by the U.S.Department of Energy,Office of Science,Office of Advanced Scientific Computing Research,Applied Mathematics Programby the Artificial Intelligence Initiative at the Oak Ridge National Laboratory(ORNL).ORNL is operated by UTBattelle,LLC.,for the U.S.Department of Energy under Contract DEAC05-00OR22725This research used resources of the National Energy Research Scientific Computing Center,supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231.
文摘The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.