1
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Sheridan RJ, Zauscher S, Brinson LC. BOTTS: broadband optimized time-temperature superposition for vastly accelerated viscoelastic data acquisition. SOFT MATTER 2024. [PMID: 39258432 DOI: 10.1039/d4sm00798k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Modern materials design strategies take advantage of the increasing amount of materials property data available and increasingly complex algorithms to take advantage of those data. However, viscoelastic materials resist this trend towards increased data rates due to their inherent time-dependent properties. Therefore, viscoelasticity measurements present a roadblock for data collection in an important aspect of material design. For thermorheologically simple (TRS) materials, time-temperature superposition (TTS) made relaxation spectrum measurements faster relative to, for example, very long creep experiments. However, TTS itself currently faces a speed limit originating in the common logarithmic discrete frequency sweep (DFS) mode of operation. In DFS, the measurement time is proportional (by a factor much greater than one) to the lowest frequency of measurement. This state of affairs has not improved for TTS for half a century or more. We utilize recent work in experimental rheometry on windowed chirps to collect three decades of complex modulus data simultaneously, resulting in a ∼500% increase in data collection. In BOTTS, we superpose several isothermal chirp responses to produce a master curve in a fraction of time required by the traditional DFS-TTS technique. The chirp responses have good, albeit nontrivial, signal-to-noise properties. We use linear error propagation and a noise-weighted least squares approach to automatically incorporate all the data into a reliable shifting method. Using model thermoset polymers, we show that DFS-TTS and BOTTS results are comparable, and therefore BOTTS data represent a first step towards a faster method for master curve generation from unmodified rheological measurement instruments.
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Affiliation(s)
- Richard J Sheridan
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, USA.
| | - Stefan Zauscher
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, USA.
| | - L Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, USA.
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2
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Shi J, Walsh D, Zou W, Rebello NJ, Deagen ME, Fransen KA, Gao X, Olsen BD, Audus DJ. Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance. ACS POLYMERS AU 2024; 4:66-76. [PMID: 38371731 PMCID: PMC10870752 DOI: 10.1021/acspolymersau.3c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/20/2024]
Abstract
Synthetic polymers, in contrast to small molecules and deterministic biomacromolecules, are typically ensembles composed of polymer chains with varying numbers, lengths, sequences, chemistry, and topologies. While numerous approaches exist for measuring pairwise similarity among small molecules and sequence-defined biomacromolecules, accurately determining the pairwise similarity between two polymer ensembles remains challenging. This work proposes the earth mover's distance (EMD) metric to calculate the pairwise similarity score between two polymer ensembles. EMD offers a greater resolution of chemical differences between polymer ensembles than the averaging method and provides a quantitative numeric value representing the pairwise similarity between polymer ensembles in alignment with chemical intuition. The EMD approach for assessing polymer similarity enhances the development of accurate chemical search algorithms within polymer databases and can improve machine learning techniques for polymer design, optimization, and property prediction.
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Affiliation(s)
- Jiale Shi
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Dylan Walsh
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Weizhong Zou
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Nathan J. Rebello
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael E. Deagen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Katharina A. Fransen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Xian Gao
- Department
of Chemical and Biomolecular Engineering, University of Notre Dame, Notre
Dame, Indiana 46556, United States
| | - Bradley D. Olsen
- Department
of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, Massachusetts 02139, United States
| | - Debra J. Audus
- Materials
Science and Engineering Division, National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
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3
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Lee D, Chen WW, Wang L, Chan YC, Chen W. Data-Driven Design for Metamaterials and Multiscale Systems: A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305254. [PMID: 38050899 DOI: 10.1002/adma.202305254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/15/2023] [Indexed: 12/07/2023]
Abstract
Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.
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Affiliation(s)
- Doksoo Lee
- Dept. of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Wei Wayne Chen
- J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77840, USA
| | - Liwei Wang
- Dept. of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Yu-Chin Chan
- Siemens Corporation, Technology, Princeton, NJ, 08540, USA
| | - Wei Chen
- Dept. of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
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4
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Liu A, Lee M, Venkatesh R, Bonsu JA, Volkovinsky R, Meredith JC, Reichmanis E, Grover MA. Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2023; 35:8816-8826. [PMID: 38027538 PMCID: PMC10653076 DOI: 10.1021/acs.chemmater.3c01842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/30/2023] [Accepted: 10/02/2023] [Indexed: 12/01/2023]
Abstract
Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based organic field-effect transistor (OFET) may vary by several orders of magnitude for a given polymer as a plethora of parameters related to solution processing, interface design/surface treatment, thin-film deposition, postprocessing, and measurement settings have a profound effect on the value of the final measurement. Incomplete contextual, experimental details hamper the availability of reusable data applicable for data-driven optimization, modeling (e.g., machine learning), and analysis of new organic devices. To curate organic device databases that contain reproducible and findable, accessible, interoperable, and reusable (FAIR) experimental data records, data ontologies that fully describe sample provenance and process history are required. However, standards for generating such process ontologies are not widely adopted for experimental materials domains. In this work, we design and implement an object-relational database for storing experimental records of OFETs. A data structure is generated by drawing on an international standard for batch process control (ISA-88) to facilitate the design. We then mobilize these representative data records, curated from the literature and laboratory experiments, to enable data-driven learning of process-structure-property relationships. The work presented herein opens the door for the broader adoption of data management practices and design standards for both the organic electronics and the wider materials community.
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Affiliation(s)
- Aaron
L. Liu
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Myeongyeon Lee
- Department
of Chemical & Biomolecular Engineering, Lehigh University, 124 East Morton Street, Bethlehem, Pennsylvania 18015, United States
| | - Rahul Venkatesh
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Jessica A. Bonsu
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Ron Volkovinsky
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - J. Carson Meredith
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
| | - Elsa Reichmanis
- Department
of Chemical & Biomolecular Engineering, Lehigh University, 124 East Morton Street, Bethlehem, Pennsylvania 18015, United States
| | - Martha A. Grover
- School
of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, United States
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5
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Huerta EA, Blaiszik B, Brinson LC, Bouchard KE, Diaz D, Doglioni C, Duarte JM, Emani M, Foster I, Fox G, Harris P, Heinrich L, Jha S, Katz DS, Kindratenko V, Kirkpatrick CR, Lassila-Perini K, Madduri RK, Neubauer MS, Psomopoulos FE, Roy A, Rübel O, Zhao Z, Zhu R. FAIR for AI: An interdisciplinary and international community building perspective. Sci Data 2023; 10:487. [PMID: 37495591 PMCID: PMC10372139 DOI: 10.1038/s41597-023-02298-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/09/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- E A Huerta
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA.
- Department of Computer Science, University of Chicago, Chicago, Illinois, 60637, USA.
| | - Ben Blaiszik
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
- Globus, University of Chicago, Chicago, Illinois, 60637, USA
| | - L Catherine Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, 27708, USA
| | - Kristofer E Bouchard
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Biological Systems & Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, 94720, USA
| | - Daniel Diaz
- Department of Physics, University of California San Diego, La Jolla, California, 92093, USA
| | - Caterina Doglioni
- Lund University, Department of Physics, Box 118, 221 00, Lund, Sweden
- School of Physics & Astronomy, The University of Manchester, Manchester, M13 9PL, UK
| | - Javier M Duarte
- Department of Physics, University of California San Diego, La Jolla, California, 92093, USA
| | - Murali Emani
- Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois, 60439, USA
| | - Ian Foster
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
- Department of Computer Science, University of Chicago, Chicago, Illinois, 60637, USA
| | - Geoffrey Fox
- Biocomplexity Institute and Department of Computer Science, University of Virginia, Charlottesville, Virginia, 22904, USA
| | - Philip Harris
- Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA
| | - Lukas Heinrich
- Technical University Munich, Arcisstraβe 21, 80333, München, Germany
| | - Shantenu Jha
- Computational Science Initiative Brookhaven National Laboratory Upton, New York, 11973, USA
- Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Daniel S Katz
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Volodymyr Kindratenko
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Christine R Kirkpatrick
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California, 92093, USA
| | - Kati Lassila-Perini
- Helsinki Institute of Physics, University of Helsinki, P.O. Box 64, Helsinki, 00014, Finland
| | - Ravi K Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, 60439, USA
| | - Mark S Neubauer
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Fotis E Psomopoulos
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, 57001, Greece
| | - Avik Roy
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Oliver Rübel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Zhizhen Zhao
- National Center for Supercomputing Applications, University of Illinois, Urbana-Champaign, Urbana, Illinois, 61801, USA
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Ruike Zhu
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
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6
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Ma B, Finan NJ, Jany D, Deagen ME, Schadler LS, Brinson LC. Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition-Property Relationship: A Case Study of NanoMine Database. Macromolecules 2023; 56:3945-3953. [PMID: 37333841 PMCID: PMC10275499 DOI: 10.1021/acs.macromol.2c02249] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/27/2023] [Indexed: 06/20/2023]
Abstract
The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature Tg (ΔTg) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC ΔTg, and built a multiple power regression metamodel to predict ΔTg. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.
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Affiliation(s)
- Boran Ma
- Department
of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Nicholas J. Finan
- Department
of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - David Jany
- Department
of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Michael E. Deagen
- Department
of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
| | - Linda S. Schadler
- Department
of Department of Mechanical Engineering, College of Engineering and
Mathematical Sciences, University of Vermont, Burlington, Vermont 05405, United States
| | - L. Catherine Brinson
- Department
of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, United States
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7
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Odegard GM, Liang Z, Siochi EJ, Warren JA. A successful strategy for MGI-inspired research. MRS BULLETIN 2023; 48:1-5. [PMID: 37361860 PMCID: PMC10153771 DOI: 10.1557/s43577-023-00525-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Affiliation(s)
| | | | | | - James A. Warren
- National Institute of Standards and Technology, Gaithersburg, USA
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8
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Brinson LC, Bartolo LM, Blaiszik B, Elbert D, Foster I, Strachan A, Voorhees PW. Community action on FAIR data will fuel a revolution in materials research. MRS BULLETIN 2023; 49:12-16. [PMID: 38283234 PMCID: PMC10808404 DOI: 10.1557/s43577-023-00498-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 01/30/2024]
Affiliation(s)
- L. Catherine Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, USA
| | - Laura M. Bartolo
- Center for Hierarchical Materials Design, Northwestern University, Evanston, USA
| | - Ben Blaiszik
- Data Science and Learning Division, Argonne National Laboratory, Lemont, USA
- Globus, The University of Chicago, Chicago, USA
| | - David Elbert
- PARADIM Materials Innovation Platform, Johns Hopkins University, Baltimore, USA
| | - Ian Foster
- Department of Computer Science, The University of Chicago, Chicago, USA
- Data Science and Learning Division, Argonne National Laboratory, Lemont, USA
| | | | - Peter W. Voorhees
- Department of Materials Science and Engineering, Northwestern University, Evanston, USA
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9
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Walsh DJ, Zou W, Schneider L, Mello R, Deagen ME, Mysona J, Lin TS, de Pablo JJ, Jensen KF, Audus DJ, Olsen BD. Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure. ACS CENTRAL SCIENCE 2023; 9:330-338. [PMID: 36968543 PMCID: PMC10037456 DOI: 10.1021/acscentsci.3c00011] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The Community Resource for Innovation in Polymer Technology (CRIPT) data model is designed to address the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties.
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Affiliation(s)
- Dylan J. Walsh
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Weizhong Zou
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Ludwig Schneider
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Reid Mello
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Michael E. Deagen
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Joshua Mysona
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Tzyy-Shyang Lin
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Juan J. de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Klavs F. Jensen
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Debra J. Audus
- Materials
Science and Engineering Division, National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Bradley D. Olsen
- Department of Chemical
Engineering, Massachusetts Institute of
Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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10
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Martin TB, Audus DJ. Emerging Trends in Machine Learning: A Polymer Perspective. ACS POLYMERS AU 2023. [DOI: 10.1021/acspolymersau.2c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Tyler B. Martin
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
| | - Debra J. Audus
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
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11
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Abstract
The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is particularly acute in the polymers domain. Here we aim to simultaneously tackle these challenges through the incorporation of scientific knowledge, thus, providing improved predictions for smaller data sets, both under interpolation and extrapolation, and a degree of explainability. We focus on imperfect theories, as they are often readily available and easier to interpret. Using a system of a polymer in different solvent qualities, we explore numerous methods for incorporating theory into machine learning using different machine-learning models, including Gaussian process regression. Ultimately, we find that encoding the functional form of the theory performs best followed by an encoding of the numeric values of the theory.
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Affiliation(s)
- Debra J Audus
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Austin McDannald
- Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Brian DeCost
- Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
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12
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Deagen ME, McCusker JP, Fateye T, Stouffer S, Brinson LC, McGuinness DL, Schadler LS. FAIR and Interactive Data Graphics from a Scientific Knowledge Graph. Sci Data 2022; 9:239. [PMID: 35624233 PMCID: PMC9142568 DOI: 10.1038/s41597-022-01352-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/26/2022] [Indexed: 11/16/2022] Open
Abstract
Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.
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Affiliation(s)
- Michael E Deagen
- Department of Mechanical Engineering, University of Vermont, Burlington, VT, USA.
| | - Jamie P McCusker
- Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Tolulomo Fateye
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Samuel Stouffer
- Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - L Cate Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | | | - Linda S Schadler
- Department of Mechanical Engineering, University of Vermont, Burlington, VT, USA
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13
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Data-driven approaches for structure-property relationships in polymer science for prediction and understanding. Polym J 2022. [DOI: 10.1038/s41428-022-00648-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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14
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Chen C, Yaari Z, Apfelbaum E, Grodzinski P, Shamay Y, Heller DA. Merging data curation and machine learning to improve nanomedicines. Adv Drug Deliv Rev 2022; 183:114172. [PMID: 35189266 PMCID: PMC9233944 DOI: 10.1016/j.addr.2022.114172] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 12/12/2022]
Abstract
Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
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Affiliation(s)
- Chen Chen
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-institutional Ph.D. Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Zvi Yaari
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Elana Apfelbaum
- Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Piotr Grodzinski
- Nanodelivery Systems and Devices Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yosi Shamay
- Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Daniel A Heller
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-institutional Ph.D. Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
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Müller M. Selection of Advances in Theory and Simulation during the First Decade of ACS Macro Letters. ACS Macro Lett 2021; 10:1629-1635. [PMID: 35549151 DOI: 10.1021/acsmacrolett.1c00750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Marcus Müller
- Institute for Theoretical Physics, Georg-August-University, 37077 Göttingen, Germany
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Lee K, Jeong S, Park J, Kim H. MoS 2-Embedded, Interpenetrating Network Composite Hydrogels that Show Controlled Release of Dyes and Tunable Strength. ACS OMEGA 2021; 6:25623-25630. [PMID: 34632218 PMCID: PMC8495838 DOI: 10.1021/acsomega.1c03690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/10/2021] [Indexed: 05/03/2023]
Abstract
This paper describes a conceptual design of hierarchical composite hydrogels. The hydrogel materials comprise MoS2 flakes and interpenetrating polymer networks, and further exhibit controlled release and tunable strength that are caused by the synergistic combination of select components. In terms of design, MoS2 flakes initiate radical polymerization of chosen monomers and simultaneously provide physical cross-linking points, both of which afford a primary composite network. Then, the sequential formation of additional networks results in functional, hierarchical, composite hydrogels. Therefore, we were able to demonstrate double-network hydrogels as a stimuli-responsive vector for programmed release of cargo molecules in response to heat or light or to form triple-network hydrogels showing tunable mechanical strength owing to intermolecular interaction between charged monomers and MoS2 flakes. The design concept would be expanded by incorporating other chalcogenides or functional monomers, which advance the properties and functionalities of materials and broadens the versatility of nanocomposite hydrogels.
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Affiliation(s)
| | | | - Jieun Park
- School of Polymer Science
and Engineering & Alan G. MacDiarmid Energy Research Institute, Chonnam National University, 77 Yongbong-ro,
Buk-gu, Gwangju 61186, Korea
| | - Hyungwoo Kim
- School of Polymer Science
and Engineering & Alan G. MacDiarmid Energy Research Institute, Chonnam National University, 77 Yongbong-ro,
Buk-gu, Gwangju 61186, Korea
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Mikhailov IV, Amoskov VM, Darinskii AA, Birshtein TM. The Structure of Dipolar Polymer Brushes and Their Interaction in the Melt. Impact of Chain Stiffness. Polymers (Basel) 2020; 12:E2887. [PMID: 33276514 PMCID: PMC7760783 DOI: 10.3390/polym12122887] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 11/30/2020] [Accepted: 11/30/2020] [Indexed: 12/24/2022] Open
Abstract
By using the numerical lattice Scheutjens-Fleer self-consistent field (SF-SCF) method we have studied the effect of the restricted flexibility of grafted chains on the structure and mutual interaction of two opposing planar conventional and A-type dipolar brushes. Brushes are immersed in the solvent consisting of chains similar to the grafted ones. The increase of the chain rigidity enhances the segregation of grafted chains in a A-type dipolar brush into two populations: backfolded chains with terminal monomers near the grafting surface and chains with the ends at the brush periphery. The fraction of backfolded chains grows by an increase of the Kuhn segment length. It is shown that two opposite A-type dipolar brushes from semi-rigid chains are attracted to each other at short distances. The attraction becomes more pronounced and begins at larger distances for more rigid chains with the same brush characteristics: polymerization degree, grafting density, and dipole moments of monomer units. This attraction is connected with the dipole-dipole interactions between chains of oncoming brushes with oppositely directed dipoles penetrating deeply into each other upon contact. This effect of the chain rigidity is opposite to that for conventional brushes without dipoles in the chains. For such brushes, an increase in the chain rigidity leads to the enhanced repulsion between them.
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Affiliation(s)
- Ivan V. Mikhailov
- Institute of Macromolecular Compounds, Russian Academy of Sciences, 199004 St. Petersburg, Russia; (V.M.A.); (A.A.D.); (T.M.B.)
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