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Patra TK, Meenakshisundaram V, Hung JH, Simmons DS. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn. ACS COMBINATORIAL SCIENCE 2017; 19:96-107. [PMID: 27997791 DOI: 10.1021/acscombsci.6b00136] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.
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Affiliation(s)
- Tarak K. Patra
- Department of Polymer Engineering, The University of Akron, 250 South Forge Street, Akron, Ohio 44325, United States
| | - Venkatesh Meenakshisundaram
- Department of Polymer Engineering, The University of Akron, 250 South Forge Street, Akron, Ohio 44325, United States
| | - Jui-Hsiang Hung
- Department of Polymer Engineering, The University of Akron, 250 South Forge Street, Akron, Ohio 44325, United States
| | - David S. Simmons
- Department of Polymer Engineering, The University of Akron, 250 South Forge Street, Akron, Ohio 44325, United States
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Yosipof A, Shimanovich K, Senderowitz H. Materials Informatics: Statistical Modeling in Material Science. Mol Inform 2016; 35:568-579. [DOI: 10.1002/minf.201600047] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 07/11/2016] [Indexed: 01/01/2023]
Affiliation(s)
- Abraham Yosipof
- Department of Business Administration; Peres Academic Center; Rehovot 76102 Israel
- College of Law & Business; Ramat-Gan 26 Ben Gurion Street Israel
| | - Klimentiy Shimanovich
- Department of Chemistry; Bar Ilan University; Ramat-Gan 5290002 Israel
- Department of Physical Electronics, School of Electrical Engineering, Faculty of Engineering; Tel Aviv University; Ramat Aviv 69978 Israel
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Lin T, Kellici S, Gong K, Thompson K, Evans JRG, Wang X, Darr JA. Rapid Automated Materials Synthesis Instrument: Exploring the Composition and Heat-Treatment of Nanoprecursors Toward Low Temperature Red Phosphors. ACTA ACUST UNITED AC 2010; 12:383-92. [DOI: 10.1021/cc9001108] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tian Lin
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Suela Kellici
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Kenan Gong
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Kathryn Thompson
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Julian R. G. Evans
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Xue Wang
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
| | - Jawwad A. Darr
- Christopher Ingold Laboratories, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom, and Institute of Particle Science & Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Clarendon Road, Leeds LS2 9JT, United Kingdom
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