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Tracking materials science data lineage to manage millions of materials experiments and analyses

npj Computational Materials 2019年第1期5卷 460-468页

作者： Edwin Soedarmadji Helge S.Stein Santosh K.Suram dan guevarra John M.Gregoire Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCAUSA Toyota Research Institute Los AltosCA 94022USA

In an era of rapid advancement of algorithms that extract knowledge from data,data and metadata management are increasingly critical to research *** materials science,there are few examples of experimental databases that contain many different types of information,and compared with other disciplines,the database sizes are relatively *** these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments,which we address with the development of a lightweight data management framework that is generally applicable for experimental science and *** years of managing experiments with this system has yielded the Materials Experiment and Analysis Database(MEAD)that contains raw data and metadata from millions of materials synthesis and characterization experiments,as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source *** unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing *** search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their *** enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences,and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

关键词： linking distillation autonomous

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Analyzing machine learning models to accelerate generation of fundamental materials insights

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npj Computational Materials 2019年第1期5卷 858-866页

作者： Mitsutaro Umehara Helge S.Stein dan guevarra Paul F.Newhouse David A.Boyd John M.Gregoire Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction *** neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science *** a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in *** building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical *** interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human *** also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data.

关键词： neural prediction desired

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Multi-component background learning automates signal detection for spectroscopic data

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npj Computational Materials 2019年第1期5卷 484-490页

作者： Sebastian E.Ament Helge S.Stein dan guevarra Lan Zhou Joel A.Haber David A.Boyd Mitsutaro Umehara John M.Gregoire Carla P.Gomes Department of Computer Science Cornell UniversityIthacaNY 14850USA Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Automated experimentation has yielded data acquisition rates that supersede human processing *** Intelligence offers new possibilities for automating data interpretation to generate large,high-quality *** subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data ***,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of *** approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background *** the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization *** identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.

关键词： signal component spectroscopic

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aterials structure-property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

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npj Computational Materials 2022年第1期 574-580页

作者： dan guevarra Lan Zhou Matthias H.Richter Aniketa Shinde Di Chen Carla P.Gomes John M.Gregoire Division of Engineering and Applied Science and Liquid Sunlight Alliance California Institute of TechnologyPasadenaCAUSA Department of Computer Science Cornell UniversityIthacaNYUSA

Properties can be tailored by tuning composition in high-order composition *** spaces with complex phase behavior,modeling the properties as a function of composition and phase distribution remains a formidable *** present materials structure–property factorization(MSPF)as an approach to automate modeling of such data and identify synergistic phase *** is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks(DRNets)to matrix factorization-based modeling of the representative properties of each phase in a *** is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation,which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity *** the measured photoactivity to a learned model for non-interacting phases,synergistic phase interactions are identified to guide further photoactivity optimization and *** identifies synergistic interactions of a BiVO_(4)-like phase with both Cu_(2)V_(2)O_(7)-like and CuV_(2)O_(6)-like phases,creating avenues for understanding complex photoelectrocatalysts.

关键词： synergistic phase property

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