This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput ch...This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with highthroughput calculations and materials informatics.Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries.Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional,structural and(multi)functional properties of the materials contained in the libraries.The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials,using combinatorial processing.Furthermore,these datasets are the basis for multifunctional existence diagrams,comprising correlations between composition,processing,structure and properties,which can be used for the design of future materials.展开更多
We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,...We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,such as the structural similarity of textured diffraction patterns.While other artificial intelligence(AI)agents are effective at classifying XRD data into known phases,a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis.展开更多
Multiple-principal element alloys hold great promise for multifunctional material discovery(e.g.,for novel electrocatalysts based on complex solid solutions)in a virtually unlimited compositional space.Here,the phase ...Multiple-principal element alloys hold great promise for multifunctional material discovery(e.g.,for novel electrocatalysts based on complex solid solutions)in a virtually unlimited compositional space.Here,the phase constitution of the noble metal system Ag-Ir-Pd-Pt-Ru was investigated over a large compositional range in the quinary composition space and for different annealing temperatures from 600 to 900°C using thin-film materials libraries.Composition-dependent X-ray diffraction mapping of the asdeposited thin-film materials library indicates different phases being present across the composition space(face-centered cubic(fcc),hexagonal close packed(hcp)and mixed fcc+hcp),which are strongly dependent on the Ru content.In general,low Ru contents promote the fcc phase,whereas high Ru contents favor the formation of an hcp solid-solution phase.Furthermore,a temperature-induced phase transformation study was carried out for a selected measurement area of fcc-Ag_(5)Ir_(8)Pd_(56)Pt_(8)Ru_(23).With increasing temperature,the initial fcc phase transforms to an intermediate C14-type Laves phase at 360°C,and then to hcp when the temperature reaches 510°C.The formation and disappearance of the hexagonal Laves phase,which covers a wide temperature range,plays a crucial role of bridging the fcc to hcp phase transition.The obtained composition,phase and temperature data are transformed into phase maps which could be used to guide theoretical studies and lay a basis for tuning the functional properties of these materials.展开更多
Despite outstanding accomplishments in catalyst discovery,finding new,more efficient,environmentally neutral,and noble metalfree catalysts remains challenging and unsolved.Recently,complex solid solutions consisting o...Despite outstanding accomplishments in catalyst discovery,finding new,more efficient,environmentally neutral,and noble metalfree catalysts remains challenging and unsolved.Recently,complex solid solutions consisting of at least five different elements and often named as high-entropy alloys have emerged as a new class of electrocatalysts for a variety of reactions.The multicomponent combinations of elements facilitate tuning of active sites and catalytic properties.Predicting optimal catalyst composition remains difficult,making testing of a very high number of them indispensable.We present the high-throughput screening of the electrochemical activity of thin film material libraries prepared by combinatorial co-sputtering of metals which are commonly used in catalysis(Pd,Cu,Ni)combined with metals which are not commonly used in catalysis(Ti,Hf,Zr).Introducing unusual elements in the search space allows discovery of catalytic activity for hitherto unknown compositions.Material libraries with very similar composition spreads can show different activities vs.composition trends for different reactions.In order to address the inherent challenge of the huge combinatorial material space and the inability to predict active electrocatalyst compositions,we developed a high-throughput process based on co-sputtered material libraries,and performed high-throughput characterization using energy dispersive X-ray spectroscopy(EDS),scanning transmission electron microscopy(SEM),X-ray diffraction(XRD)and conductivity measurements followed by electrochemical screening by means of a scanning droplet cell.The results show surprising material compositions with increased activity for the oxygen reduction reaction and the hydrogen evolution reaction.Such data are important input data for future data-driven materials prediction.展开更多
文摘This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with highthroughput calculations and materials informatics.Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries.Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional,structural and(multi)functional properties of the materials contained in the libraries.The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials,using combinatorial processing.Furthermore,these datasets are the basis for multifunctional existence diagrams,comprising correlations between composition,processing,structure and properties,which can be used for the design of future materials.
基金This study was funded by the German Research Foundation(DFG)as part of Collaborative Research Centers SFB-TR 87 and SFB-TR 103This research used resources of the National Synchrotron Light Source II,a U.S.Department of Energy(DOE)Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No.DE-SC0012704the BNL Laboratory Directed Research and Development(LDRD)project 20-032‘Accelerating materials discovery with total scattering via machine learning’.The center for interface dominated high-performance materials(ZGH,Ruhr-Universität Bochum,Bochum,Germany)is acknowledged for X-ray diffraction experiments.
文摘We apply variational autoencoders(VAE)to X-ray diffraction(XRD)data analysis on both simulated and experimental thin-film data.We show that crystal structure representations learned by a VAE reveal latent information,such as the structural similarity of textured diffraction patterns.While other artificial intelligence(AI)agents are effective at classifying XRD data into known phases,a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know:it can rapidly identify data outside the distribution it was trained on,such as novel phases and mixtures.These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both‘on-the-fly’and during post hoc analysis.
基金Deutsche Forschungsgemeinschaft(DFG),projects LU1175/26-1 and LU1175/22-1.
文摘Multiple-principal element alloys hold great promise for multifunctional material discovery(e.g.,for novel electrocatalysts based on complex solid solutions)in a virtually unlimited compositional space.Here,the phase constitution of the noble metal system Ag-Ir-Pd-Pt-Ru was investigated over a large compositional range in the quinary composition space and for different annealing temperatures from 600 to 900°C using thin-film materials libraries.Composition-dependent X-ray diffraction mapping of the asdeposited thin-film materials library indicates different phases being present across the composition space(face-centered cubic(fcc),hexagonal close packed(hcp)and mixed fcc+hcp),which are strongly dependent on the Ru content.In general,low Ru contents promote the fcc phase,whereas high Ru contents favor the formation of an hcp solid-solution phase.Furthermore,a temperature-induced phase transformation study was carried out for a selected measurement area of fcc-Ag_(5)Ir_(8)Pd_(56)Pt_(8)Ru_(23).With increasing temperature,the initial fcc phase transforms to an intermediate C14-type Laves phase at 360°C,and then to hcp when the temperature reaches 510°C.The formation and disappearance of the hexagonal Laves phase,which covers a wide temperature range,plays a crucial role of bridging the fcc to hcp phase transition.The obtained composition,phase and temperature data are transformed into phase maps which could be used to guide theoretical studies and lay a basis for tuning the functional properties of these materials.
基金support by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)in the framework of the projects AN 1570/2-1(C.A.,S.S.)and LU 1175/31-1)(A.L)the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement CasCat[833408],W.S.).
文摘Despite outstanding accomplishments in catalyst discovery,finding new,more efficient,environmentally neutral,and noble metalfree catalysts remains challenging and unsolved.Recently,complex solid solutions consisting of at least five different elements and often named as high-entropy alloys have emerged as a new class of electrocatalysts for a variety of reactions.The multicomponent combinations of elements facilitate tuning of active sites and catalytic properties.Predicting optimal catalyst composition remains difficult,making testing of a very high number of them indispensable.We present the high-throughput screening of the electrochemical activity of thin film material libraries prepared by combinatorial co-sputtering of metals which are commonly used in catalysis(Pd,Cu,Ni)combined with metals which are not commonly used in catalysis(Ti,Hf,Zr).Introducing unusual elements in the search space allows discovery of catalytic activity for hitherto unknown compositions.Material libraries with very similar composition spreads can show different activities vs.composition trends for different reactions.In order to address the inherent challenge of the huge combinatorial material space and the inability to predict active electrocatalyst compositions,we developed a high-throughput process based on co-sputtered material libraries,and performed high-throughput characterization using energy dispersive X-ray spectroscopy(EDS),scanning transmission electron microscopy(SEM),X-ray diffraction(XRD)and conductivity measurements followed by electrochemical screening by means of a scanning droplet cell.The results show surprising material compositions with increased activity for the oxygen reduction reaction and the hydrogen evolution reaction.Such data are important input data for future data-driven materials prediction.
