1
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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2
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Ziatdinov M, Ghosh A, Wong CY, Kalinin SV. AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00555-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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3
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Recent progress in perovskite solar cells: material science. Sci China Chem 2022. [DOI: 10.1007/s11426-022-1445-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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4
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Ziatdinov MA, Liu Y, Morozovska AN, Eliseev EA, Zhang X, Takeuchi I, Kalinin SV. Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201345. [PMID: 35279893 DOI: 10.1002/adma.202201345] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.
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Affiliation(s)
- Maxim A Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Anna N Morozovska
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, Kyiv, 03028, Ukraine
| | - Eugene A Eliseev
- Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, Krjijanovskogo 3, Kyiv, 03142, Ukraine
| | - Xiaohang Zhang
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Ichiro Takeuchi
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, 20742, USA
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
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5
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Valleti SMP, Zou Q, Xue R, Vlcek L, Ziatdinov M, Vasudevan R, Fu M, Yan J, Mandrus D, Gai Z, Kalinin SV. Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data. ACS NANO 2021; 15:9649-9657. [PMID: 34105943 DOI: 10.1021/acsnano.0c10851] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.
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Affiliation(s)
- Sai Mani Prudhvi Valleti
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Qiang Zou
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Rui Xue
- Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Lukas Vlcek
- Joint Institute for Computational Sciences, University of Tennessee, Knoxville, Oak Ridge, Tennessee 37831, United States
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge Tennessee 37831, United States
| | - Maxim Ziatdinov
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Rama Vasudevan
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Mingming Fu
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Jiaqiang Yan
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge Tennessee 37831, United States
| | - David Mandrus
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge Tennessee 37831, United States
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Zheng Gai
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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6
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Mukaddem KT, Beard EJ, Yildirim B, Cole JM. ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images. J Chem Inf Model 2019; 60:2492-2509. [PMID: 31714792 DOI: 10.1021/acs.jcim.9b00734] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The rise of data science is leading to new paradigms in data-driven materials discovery. This carries an essential notion that large data sources containing chemical structure and property information can be mined in a fashion that detects and exploits structure-property relationships, such that chemicals can be predicted to suit a given material application. The success of material predictions is predicated on these large data sources of chemical structure and property information being suited to a target application. Microscopy is commonly used to characterize chemical structure, especially in fields such as nanotechnology where material properties are highly dependent on the size and shape of nanoparticles. Large data sources of nanoparticle information stemming from microscopy images would thus be highly beneficial. Millions of microscopy images exist, but they lie fragmented across the literature, typically presented individually within a paper article and usually in a qualitative fashion therein, even though they harbor a wealth of numeric information. We present the ImageDataExtractor toolkit that autoidentifies and autoextracts microscopy images from scientific documents, whereupon it autonomously analyzes each image to produce quantitative particle size and shape information about its subject material. Each image is quantified by decoding its scale bar information using optical character recognition, with help from super-resolution convolutional neural networks where required. Individual particles are detected and profiled using various thresholding, segmentation, polygon fitting, and edge correction routines. The high-throughput operational capability of ImageDataExtractor means that it can be used to generate large-data sources of particle information for data-driven materials discovery. Evaluation metrics, precision and recall, are greater than 80% for the majority of the image processing steps, and precision is above 80% for all critical steps. The ImageDataExtractor tool is released under the MIT license and is available to download from http://www.imagedataextractor.org.
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Affiliation(s)
- Karim T Mukaddem
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K
| | - Edward J Beard
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.,ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K
| | - Batuhan Yildirim
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K
| | - Jacqueline M Cole
- Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.,ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.,Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K
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7
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Kalinin SV, Dyck O, Balke N, Neumayer S, Tsai WY, Vasudevan R, Lingerfelt D, Ahmadi M, Ziatdinov M, McDowell MT, Strelcov E. Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities. ACS NANO 2019; 13:9735-9780. [PMID: 31433942 DOI: 10.1021/acsnano.9b02687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Electrochemical reactions and ionic transport underpin the operation of a broad range of devices and applications, from energy storage and conversion to information technologies, as well as biochemical processes, artificial muscles, and soft actuators. Understanding the mechanisms governing function of these applications requires probing local electrochemical phenomena on the relevant time and length scales. Here, we discuss the challenges and opportunities for extending electrochemical characterization probes to the nanometer and ultimately atomic scales, including challenges in down-scaling classical methods, the emergence of novel probes enabled by nanotechnology and based on emergent physics and chemistry of nanoscale systems, and the integration of local data into macroscopic models. Scanning probe microscopy (SPM) methods based on strain detection, potential detection, and hysteretic current measurements are discussed. We further compare SPM to electron beam probes and discuss the applicability of electron beam methods to probe local electrochemical behavior on the mesoscopic and atomic levels. Similar to a SPM tip, the electron beam can be used both for observing behavior and as an active electrode to induce reactions. We briefly discuss new challenges and opportunities for conducting fundamental scientific studies, matter patterning, and atomic manipulation arising in this context.
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Affiliation(s)
- Sergei V Kalinin
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Ondrej Dyck
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Nina Balke
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Sabine Neumayer
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Wan-Yu Tsai
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Rama Vasudevan
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - David Lingerfelt
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Mahshid Ahmadi
- Joint Institute for Advanced Materials, Department of Materials Science and Engineering , University of Tennessee , Knoxville , Tennessee 37996 , United States
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Matthew T McDowell
- George W. Woodruff School of Mechanical Engineering and School of Materials Science and Engineering , Georgia Institute of Technology , Atlanta , Georgia 30332 , United States
| | - Evgheni Strelcov
- Institute for Research in Electronics and Applied Physics , University of Maryland , College Park , Maryland 20742 , United States
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8
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Vlcek L, Ziatdinov M, Maksov A, Tselev A, Baddorf AP, Kalinin SV, Vasudevan RK. Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations. ACS NANO 2019; 13:718-727. [PMID: 30609895 DOI: 10.1021/acsnano.8b07980] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In materials characterization, traditionally a single experimental sample is used to derive information about a single point in the composition space, while the imperfections, impurities, and stochastic details of material structure are deemed irrelevant or complicating factors in the analysis. Here we demonstrate that atomic-scale studies of a single nominal composition can provide information about microstructures and thermodynamic response over a finite area of chemical space. Using the principles of statistical inference, we develop a framework for incorporating structural fluctuations into statistical mechanical models and use it to solve the inverse problem of deriving effective interatomic interactions responsible for elemental segregation in a La5/8Ca3/8MnO3 thin film. The results are further analyzed by a variational autoencoder to detect anomalous behavior in the composition phase diagram. This study provides a framework for creating generative models from a combination of multiple experimental data and provides direct insight into the driving forces for cation segregation in manganites.
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Affiliation(s)
- Lukas Vlcek
- Joint Institute for Computational Sciences , University of Tennessee , Knoxville , Tennessee 37996 , United States
| | | | - Artem Maksov
- UT Bredesen Center for Interdisciplinary Research , University of Tennessee , Knoxville , Tennessee 37996 , United States
| | - Alexander Tselev
- Department of Physics , CICECO - Aveiro Institute of Materials , 3810-193 Aveiro , Portugal
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9
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Vasudevan RK, Choudhary K, Mehta A, Smith R, Kusne G, Tavazza F, Vlcek L, Ziatdinov M, Kalinin SV, Hattrick-Simpers J. Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS COMMUNICATIONS 2019; 9:10.1557/mrc.2019.95. [PMID: 32166045 PMCID: PMC7067066 DOI: 10.1557/mrc.2019.95] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 07/03/2019] [Indexed: 05/14/2023]
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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Affiliation(s)
- Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Kamal Choudhary
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
| | - Ryan Smith
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Gilad Kusne
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Francesca Tavazza
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Lukas Vlcek
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Jason Hattrick-Simpers
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
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10
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Medford AJ, Kunz MR, Ewing SM, Borders T, Fushimi R. Extracting Knowledge from Data through Catalysis Informatics. ACS Catal 2018. [DOI: 10.1021/acscatal.8b01708] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Andrew J. Medford
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318 United States
| | - M. Ross Kunz
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Sarah M. Ewing
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Tammie Borders
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Rebecca Fushimi
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
- Center for Advanced Energy Studies, 995 University Boulevard, Idaho Falls, Idaho 83401, United States
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