| Description |
1 online resource (22 pages) : color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/PR ; 2800-79420 |
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NREL/PR ; 2800-79420.
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| Note |
"Performance Advantaged BioProducts." |
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"March 10, 2021." |
| Bibliography |
Includes bibliographical references. |
| Funding |
National Renewable Energy Laboratory DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed on Oct. 22, 2021). |
| Summary |
This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs. |
| Subject |
United States. Department of Energy. Bioenergy Technologies Office.
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Machine learning.
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Biological products -- United States.
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Peer review -- United States.
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Apprentissage automatique.
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Produits biologiques -- États-Unis.
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Évaluation par des pairs -- États-Unis.
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Biological products
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Machine learning
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Peer review
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United States https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq
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| Indexed Term |
bioproducts |
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experimental synthesis |
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property prediction |
| Genre/Form |
technical reports.
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Technical reports
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Technical reports.
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Rapports techniques.
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| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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| Added Title |
Bioenergy Technologies Office 2021 peer review : inverse bioproduct design through machine learning and molecular simulation |
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Inverse bioproduct design through machine learning and molecular simulation |
| Standard No. |
1774572 OSTI ID |
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0000-0002-9002-3585 |
| Gpo Item No. |
0430-P-09 (online) |
| Sudoc No. |
E 9.22:NREL/PR-2800-79420 |
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