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Adetunji AI, Erasmus M. Green Synthesis of Bioplastics from Microalgae: A State-of-the-Art Review. Polymers (Basel) 2024; 16:1322. [PMID: 38794516 PMCID: PMC11124873 DOI: 10.3390/polym16101322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/30/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
The synthesis of conventional plastics has increased tremendously in the last decades due to rapid industrialization, population growth, and advancement in the use of modern technologies. However, overuse of these fossil fuel-based plastics has resulted in serious environmental and health hazards by causing pollution, global warming, etc. Therefore, the use of microalgae as a feedstock is a promising, green, and sustainable approach for the production of biobased plastics. Various biopolymers, such as polyhydroxybutyrate, polyurethane, polylactic acid, cellulose-based polymers, starch-based polymers, and protein-based polymers, can be produced from different strains of microalgae under varying culture conditions. Different techniques, including genetic engineering, metabolic engineering, the use of photobioreactors, response surface methodology, and artificial intelligence, are used to alter and improve microalgae stocks for the commercial synthesis of bioplastics at lower costs. In comparison to conventional plastics, these biobased plastics are biodegradable, biocompatible, recyclable, non-toxic, eco-friendly, and sustainable, with robust mechanical and thermoplastic properties. In addition, the bioplastics are suitable for a plethora of applications in the agriculture, construction, healthcare, electrical and electronics, and packaging industries. Thus, this review focuses on techniques for the production of biopolymers and bioplastics from microalgae. In addition, it discusses innovative and efficient strategies for large-scale bioplastic production while also providing insights into the life cycle assessment, end-of-life, and applications of bioplastics. Furthermore, some challenges affecting industrial scale bioplastics production and recommendations for future research are provided.
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Affiliation(s)
- Adegoke Isiaka Adetunji
- Centre for Mineral Biogeochemistry, University of the Free State, Bloemfontein 9301, South Africa
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2
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Deng S, Chen C, Li K, Chen X, Xia K, Li S. Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers. J Phys Chem B 2023; 127:10077-10087. [PMID: 37942925 DOI: 10.1021/acs.jpcb.3c06025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.
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Affiliation(s)
- Siyan Deng
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Chao Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ke Li
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
| | - Xi Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Kelin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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3
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Purcell TAR, Scheffler M, Ghiringhelli LM. Recent advances in the SISSO method and their implementation in the SISSO++ code. J Chem Phys 2023; 159:114110. [PMID: 37721326 DOI: 10.1063/5.0156620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
Accurate and explainable artificial-intelligence (AI) models are promising tools for accelerating the discovery of new materials. Recently, symbolic regression has become an increasingly popular tool for explainable AI because it yields models that are relatively simple analytical descriptions of target properties. Due to its deterministic nature, the sure-independence screening and sparsifying operator (SISSO) method is a particularly promising approach for this application. Here, we describe the new advancements of the SISSO algorithm, as implemented into SISSO++, a C++ code with Python bindings. We introduce a new representation of the mathematical expressions found by SISSO. This is a first step toward introducing "grammar" rules into the feature creation step. Importantly, by introducing a controlled nonlinear optimization to the feature creation step, we expand the range of possible descriptors found by the methodology. Finally, we introduce refinements to the solver algorithms for both regression and classification, which drastically increase the reliability and efficiency of SISSO. For all these improvements to the basic SISSO algorithm, we not only illustrate their potential impact but also fully detail how they operate both mathematically and computationally.
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Affiliation(s)
- Thomas A R Purcell
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Matthias Scheffler
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany
| | - Luca M Ghiringhelli
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195 Berlin, Germany
- Physics Department and IRIS-Adlershof, Humboldt Universität zu Berlin, Zum Großen Windkanal 2, D-12489 Berlin, Germany
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4
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Ohno M, Hayashi Y, Zhang Q, Kaneko Y, Yoshida R. SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions. J Chem Inf Model 2023; 63:5539-5548. [PMID: 37604495 PMCID: PMC10498440 DOI: 10.1021/acs.jcim.3c00329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Indexed: 08/23/2023]
Abstract
Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.
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Affiliation(s)
- Mitsuru Ohno
- Daicel
Corporation, Kita-ku, 530-0011 Osaka, Japan
| | - Yoshihiro Hayashi
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
- The
Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan
| | - Qi Zhang
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
| | - Yu Kaneko
- Daicel
Corporation, Kita-ku, 530-0011 Osaka, Japan
| | - Ryo Yoshida
- The
Institute of Statistical Mathematics, Research Organization of Information
and Systems, Tachikawa, Tokyo 190-8562, Japan
- The
Graduate University for Advanced Studies, SOKENDAI, Tachikawa, Tokyo 190-8562, Japan
- National
Institute for Materials Science, 305-0047 Ibaraki, Japan
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5
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Goswami L, Kushwaha A, Napathorn SC, Kim BS. Valorization of organic wastes using bioreactors for polyhydroxyalkanoate production: Recent advancement, sustainable approaches, challenges, and future perspectives. Int J Biol Macromol 2023; 247:125743. [PMID: 37423435 DOI: 10.1016/j.ijbiomac.2023.125743] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 06/23/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
Microbial polyhydroxyalkanoates (PHA) are encouraging biodegradable polymers, which may ease the environmental problems caused by petroleum-derived plastics. However, there is a growing waste removal problem and the high price of pure feedstocks for PHA biosynthesis. This has directed to the forthcoming requirement to upgrade waste streams from various industries as feedstocks for PHA production. This review covers the state-of-the-art progress in utilizing low-cost carbon substrates, effective upstream and downstream processes, and waste stream recycling to sustain entire process circularity. This review also enlightens the use of various batch, fed-batch, continuous, and semi-continuous bioreactor systems with flexible results to enhance the productivity and simultaneously cost reduction. The life-cycle and techno-economic analyses, advanced tools and strategies for microbial PHA biosynthesis, and numerous factors affecting PHA commercialization were also covered. The review includes the ongoing and upcoming strategies viz. metabolic engineering, synthetic biology, morphology engineering, and automation to expand PHA diversity, diminish production costs, and improve PHA production with an objective of "zero-waste" and "circular bioeconomy" for a sustainable future.
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Affiliation(s)
- Lalit Goswami
- Department of Chemical Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Republic of Korea
| | - Anamika Kushwaha
- Department of Chemical Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Republic of Korea
| | | | - Beom Soo Kim
- Department of Chemical Engineering, Chungbuk National University, Cheongju, Chungbuk 28644, Republic of Korea.
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6
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Sun Y, Ma J, Zhao W, Qu Y, Gou Z, Chen H, Tian Y, Wu F. Digital mapping of soil organic carbon density in China using an ensemble model. ENVIRONMENTAL RESEARCH 2023; 231:116131. [PMID: 37209984 DOI: 10.1016/j.envres.2023.116131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 05/22/2023]
Abstract
The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0-20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (±1.90) kg C/m2). The SOC storage amount in surface soil (0-20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.
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Affiliation(s)
- Yi Sun
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Jin Ma
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Wenhao Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yajing Qu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Zilun Gou
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Haiyan Chen
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yuxin Tian
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
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7
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McDonald SM, Augustine EK, Lanners Q, Rudin C, Catherine Brinson L, Becker ML. Applied machine learning as a driver for polymeric biomaterials design. Nat Commun 2023; 14:4838. [PMID: 37563117 PMCID: PMC10415291 DOI: 10.1038/s41467-023-40459-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 07/24/2023] [Indexed: 08/12/2023] Open
Abstract
Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.
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Affiliation(s)
| | - Emily K Augustine
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Quinn Lanners
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Cynthia Rudin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - L Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA
| | - Matthew L Becker
- Department of Chemistry, Duke University, Durham, NC, USA.
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.
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8
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Yue T, He J, Tao L, Li Y. High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning. J Chem Theory Comput 2023; 19:4641-4653. [PMID: 37338332 DOI: 10.1021/acs.jctc.3c00131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
The ability to store and release elastic strain energy, as well as mechanical strength, are crucial factors in both natural and man-made mechanical systems. The modulus of resilience (R) indicates a material's capacity to absorb and release elastic strain energy, with the yield strength (σy) and Young's modulus (E) as R = σy2/(2E) for linear elastic solids. To improve the R in linear elastic solids, a high σy and low E combination in materials is sought after. However, achieving this combination is a significant challenge as both properties typically increase together. To address this challenge, we propose a computational method to quickly identify polymers with a high modulus of resilience using machine learning (ML) and validate the predictions through high-fidelity molecular dynamics (MD) simulations. Our approach commences by training single-task ML models, multitask ML models, and Evidential Deep Learning models to forecast the mechanical properties of polymers based on experimentally reported values. Utilizing explainable ML models, we were able to determine the critical substructures that significantly impact the mechanical properties of polymers, such as E and σy. This information can be utilized to create and develop new polymers with improved mechanical characteristics. Our single-task and multitask ML models can predict the properties of 12 854 real polymers and 8 million hypothetical polyimides and uncover 10 new real polymers and 10 hypothetical polyimides with exceptional modulus of resilience. The improved modulus of resilience of these novel polymers was validated through MD simulations. Our method efficiently speeds up the discovery of high-performing polymers using ML predictions and MD validation and can be applied to other polymer material discovery challenges, such as polymer membranes, dielectric polymers, and more.
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Affiliation(s)
- Tianle Yue
- Department of Mechanical Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Jinlong He
- Department of Mechanical Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ying Li
- Department of Mechanical Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
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9
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Nigmatullin R, Taylor CS, Basnett P, Lukasiewicz B, Paxinou A, Lizarraga-Valderrama LR, Haycock JW, Roy I. Medium chain length polyhydroxyalkanoates as potential matrix materials for peripheral nerve regeneration. Regen Biomater 2023; 10:rbad063. [PMID: 37501678 PMCID: PMC10369215 DOI: 10.1093/rb/rbad063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/25/2023] [Accepted: 06/04/2023] [Indexed: 07/29/2023] Open
Abstract
Polyhydroxyalkanoates are natural, biodegradable, thermoplastic and sustainable polymers with a huge potential in fabrication of bioresorbable implantable devices for tissue engineering. We describe a comparative evaluation of three medium chain length polyhydroxyalkanoates (mcl-PHAs), namely poly(3-hydroxyoctanoate), poly(3-hydroxyoctanoate-co-3-hydoxydecanoate) and poly(3-hydroxyoctanoate-co-3-hydroxydecanoate-co-3-hydroxydodecanoate), one short chain length polyhydroxyalkanoate, poly(3-hydroxybutyrate), P(3HB) and synthetic aliphatic polyesters (polycaprolactone and polylactide) with a specific focus on nerve regeneration, due to mechanical properties of mcl-PHAs closely matching nerve tissues. In vitro biological studies with NG108-15 neuronal cell and primary Schwann cells did not show a cytotoxic effect of the materials on both cell types. All mcl-PHAs supported cell adhesion and viability. Among the three mcl-PHAs, P(3HO-co-3HD) exhibited superior properties with regards to numbers of cells adhered and viable cells for both cell types, number of neurite extensions from NG108-15 cells, average length of neurite extensions and Schwann cells. Although, similar characteristics were observed for flat P(3HB) surfaces, high rigidity of this biomaterial, and FDA-approved polymers such as PLLA, limits their applications in peripheral nerve regeneration. Therefore, we have designed, synthesized and evaluated these materials for nerve tissue engineering and regenerative medicine, the interaction of mcl-PHAs with neuronal and Schwann cells, identifying mcl-PHAs as excellent materials to enhance nerve regeneration and potentially their clinical application in peripheral nerve repair.
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Affiliation(s)
- Rinat Nigmatullin
- Higher Steaks Ltd., 25 Cambridge Science Park Rd, Milton, Cambridge CB4 0FW, UK
- School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London W1B 2HW, UK
| | - Caroline S Taylor
- Department of Materials Science & and Engineering, The University of Sheffield, Sheffield S3 7HQ, UK
| | - Pooja Basnett
- School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London W1B 2HW, UK
| | - Barbara Lukasiewicz
- School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London W1B 2HW, UK
| | - Alexandra Paxinou
- School of Life Sciences, College of Liberal Arts and Sciences, University of Westminster, London W1B 2HW, UK
- Foundation of Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes (FORTH/ICE-HT), P.O. Box 1414, GR 26504, Rion, Patras, Greece
| | | | - John W Haycock
- Department of Materials Science & and Engineering, The University of Sheffield, Sheffield S3 7HQ, UK
| | - Ipsita Roy
- Correspondence address. Tel: +44-114-222-5962, E-mail: (I.R.)
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10
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Kuenneth C, Ramprasad R. polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics. Nat Commun 2023; 14:4099. [PMID: 37433807 DOI: 10.1038/s41467-023-39868-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.
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Affiliation(s)
- Christopher Kuenneth
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- Faculty of Engineering Science, University of Bayreuth, 95447, Bayreuth, Germany
| | - Rampi Ramprasad
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
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11
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Martin TB, Audus DJ. Emerging Trends in Machine Learning: A Polymer Perspective. ACS POLYMERS AU 2023; 3:239-258. [PMID: 37334191 PMCID: PMC10273415 DOI: 10.1021/acspolymersau.2c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.
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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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12
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Liu S, Yu JM, Gan YC, Qiu XZ, Gao ZC, Wang H, Chen SX, Xiong Y, Liu GH, Lin SE, McCarthy A, John JV, Wei DX, Hou HH. Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications. Mil Med Res 2023; 10:16. [PMID: 36978167 PMCID: PMC10047482 DOI: 10.1186/s40779-023-00448-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 02/23/2023] [Indexed: 03/30/2023] Open
Abstract
Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or synthetic materials, biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix (ECM). Additionally, such materials have mechanical adaptability, microstructure interconnectivity, and inherent bioactivity, making them ideal for the design of living implants for specific applications in TE and regenerative medicine. This paper provides an overview for recent progress of biomimetic natural biomaterials (BNBMs), including advances in their preparation, functionality, potential applications and future challenges. We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM. Moreover, we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications. Finally, we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.
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Affiliation(s)
- Shuai Liu
- Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, The Fifth Affiliated Hospital, School of Basic Medical Science, Southern Medical University, Guangzhou, 510900, China
| | - Jiang-Ming Yu
- Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, 200336, China
| | - Yan-Chang Gan
- Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, The Fifth Affiliated Hospital, School of Basic Medical Science, Southern Medical University, Guangzhou, 510900, China
| | - Xiao-Zhong Qiu
- Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, The Fifth Affiliated Hospital, School of Basic Medical Science, Southern Medical University, Guangzhou, 510900, China
| | - Zhe-Chen Gao
- Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, 200336, China
| | - Huan Wang
- The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, Guangdong, China.
| | - Shi-Xuan Chen
- Engineering Research Center of Clinical Functional Materials and Diagnosis & Treatment Devices of Zhejiang Province, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325011, Zhejiang, China.
| | - Yuan Xiong
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Guo-Hui Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Si-En Lin
- Department of Orthopaedics and Traumatology, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, 999077, China
| | - Alec McCarthy
- Department of Functional Materials, Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, USA
| | - Johnson V John
- Mary & Dick Holland Regenerative Medicine Program, College of Medicine, University of Nebraska Medical Center, Omaha, NE, 68130, USA
| | - Dai-Xu Wei
- Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University, Shanghai, 200336, China.
- Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong Institute of Brain Science, Zigong, 643002, Sichuan, China.
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Department of Life Sciences and Medicine, Northwest University, Xi'an, 710127, China.
| | - Hong-Hao Hou
- Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, The Fifth Affiliated Hospital, School of Basic Medical Science, Southern Medical University, Guangzhou, 510900, China.
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13
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Shen SC, Khare E, Lee NA, Saad MK, Kaplan DL, Buehler MJ. Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics. Chem Rev 2023; 123:2242-2275. [PMID: 36603542 DOI: 10.1021/acs.chemrev.2c00479] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.
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Affiliation(s)
- Sabrina C Shen
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Eesha Khare
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nicolas A Lee
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,School of Architecture and Planning, Media Lab, Massachusetts Institute of Technology, 75 Amherst Street, Cambridge, Massachusetts 02139, United States
| | - Michael K Saad
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - David L Kaplan
- Department of Biomedical Engineering, Tufts University, 4 Colby Street, Medford, Massachusetts 02155, United States
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Avenue 1-165, Cambridge, Massachusetts 02139, United States.,Center for Computational Science and Engineering, Schwarzman College of Computing, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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14
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Nguyen T, Bavarian M. Machine learning approach to polymer reaction engineering: Determining monomers reactivity ratios. POLYMER 2023. [DOI: 10.1016/j.polymer.2023.125866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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15
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Volgin IV, Batyr PA, Matseevich AV, Dobrovskiy AY, Andreeva MV, Nazarychev VM, Larin SV, Goikhman MY, Vizilter YV, Askadskii AA, Lyulin SV. Machine Learning with Enormous "Synthetic" Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks. ACS OMEGA 2022; 7:43678-43691. [PMID: 36506114 PMCID: PMC9730753 DOI: 10.1021/acsomega.2c04649] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/28/2022] [Indexed: 06/17/2023]
Abstract
In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure-property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature T g as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a "transfer learning" approach with an enormous "synthetic" data set for pretraining and a small experimental data set for its fine-tuning. The "synthetic" data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their T g values calculated using the well-established Askadskii's quantitative structure-property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both "synthetic" and experimental data sets are included into a PolyAskInG database (Polymer Askadskii's Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of T g of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed "transfer learning" to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantum-chemical QM9 database for small compounds and fine-tuned on an experimental T g values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the "synthetic" polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and "synthetic" data sets (so-called "reality gap" problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large "synthetic" data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.
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Affiliation(s)
- Igor V. Volgin
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Pavel A. Batyr
- Federal
State Unitary Enterprise “State Research Institute of Aviation
Systems” (GosNIIAS), Moscow 125167, Russian Federation
| | - Andrey V. Matseevich
- A.N.
Nesmeyanov Institute of Organoelement Compounds of Russian Academy
of Sciences (INEOS RAS), Moscow 119991, Russian Federation
| | - Alexey Yu. Dobrovskiy
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Maria V. Andreeva
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Victor M. Nazarychev
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Sergey V. Larin
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Mikhail Ya. Goikhman
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
| | - Yury V. Vizilter
- Federal
State Unitary Enterprise “State Research Institute of Aviation
Systems” (GosNIIAS), Moscow 125167, Russian Federation
| | - Andrey A. Askadskii
- A.N.
Nesmeyanov Institute of Organoelement Compounds of Russian Academy
of Sciences (INEOS RAS), Moscow 119991, Russian Federation
- Moscow
State University of Civil Engineering (MGSU), Moscow 129337, Russian Federation
| | - Sergey V. Lyulin
- Institute
of Macromolecular Compounds of the Russian Academy of Sciences (IMC
RAS), St. Petersburg 199004, Russian Federation
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16
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Novel Production Methods of Polyhydroxyalkanoates and Their Innovative Uses in Biomedicine and Industry. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238351. [PMID: 36500442 PMCID: PMC9740486 DOI: 10.3390/molecules27238351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
Polyhydroxyalkanoate (PHA), a biodegradable polymer obtained from microorganisms and plants, have been widely used in biomedical applications and devices, such as sutures, cardiac valves, bone scaffold, and drug delivery of compounds with pharmaceutical interests, as well as in food packaging. This review focuses on the use of polyhydroxyalkanoates beyond the most common uses, aiming to inform about the potential uses of the biopolymer as a biosensor, cosmetics, drug delivery, flame retardancy, and electrospinning, among other interesting uses. The novel applications are based on the production and composition of the polymer, which can be modified by genetic engineering, a semi-synthetic approach, by changing feeding carbon sources and/or supplement addition, among others. The future of PHA is promising, and despite its production costs being higher than petroleum-based plastics, tools given by synthetic biology, bioinformatics, and machine learning, among others, have allowed for great production yields, monomer and polymer functionalization, stability, and versatility, a key feature to increase the uses of this interesting family of polymers.
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17
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Tao L, Arbaugh T, Byrnes J, Varshney V, Li Y. Unified machine learning protocol for copolymer structure-property predictions. STAR Protoc 2022; 3:101875. [PMID: 36595914 PMCID: PMC9700038 DOI: 10.1016/j.xpro.2022.101875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/06/2022] [Accepted: 11/01/2022] [Indexed: 11/23/2022] Open
Abstract
Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer types such as alternating, random, block, and gradient copolymers. We detail steps for necessary software installation and construction of datasets. We further describe training and optimization steps for four neural network models and subsequent model visualization and comparison using training and test values. For complete details on the use and execution of this protocol, please refer to Tao et al. (2022).1.
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Affiliation(s)
- Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Tom Arbaugh
- Department of Physics, Wesleyan University, Middletown, CT 06459, USA
| | | | - Vikas Varshney
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH 45433, USA
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA,Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA,Corresponding author
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18
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Schmid F. Understanding and Modeling Polymers: The Challenge of Multiple Scales. ACS POLYMERS AU 2022. [DOI: 10.1021/acspolymersau.2c00049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Friederike Schmid
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128Mainz, Germany
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19
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Andraju N, Curtzwiler GW, Ji Y, Kozliak E, Ranganathan P. Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review. ACS APPLIED MATERIALS & INTERFACES 2022; 14:42771-42790. [PMID: 36102317 DOI: 10.1021/acsami.2c08301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Machine-learning (ML) algorithms are gaining attention as cost-effective scalable solutions to exploit the physical and chemical structures of polymers. The basic steps for applying ML in polymer science such as fingerprinting, algorithms, open-source databases, representations, and polymer design are detailed in this review. Further, a state-of-the-art review of the prediction of various polymer material properties using ML is reviewed. Finally, we discuss open-ended research questions on ML application to PCR polymers as well as potential challenges in the prediction of their properties using artificial intelligence for more efficient and targeted PCR polymer discovery and development.
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Affiliation(s)
- Nagababu Andraju
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, North Dakota 58202, United States
| | - Greg W Curtzwiler
- Polymer and Food Protection Consortium, Department of Food Science and Human Nutrition, Iowa State University, Ames, Iowa 50011, United States
| | - Yun Ji
- Department of Chemical Engineering, University of North Dakota, Grand Forks, North Dakota 58202, United States
| | - Evguenii Kozliak
- Department of Chemistry, University of North Dakota, Grand Forks, North Dakota 58202, United States
| | - Prakash Ranganathan
- School of Electrical Engineering and Computer Science (SEECS), University of North Dakota, Grand Forks, North Dakota 58202, United States
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20
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Nguyen T, Bavarian M. A Machine Learning Framework for Predicting the Glass Transition Temperature of Homopolymers. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c01302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tung Nguyen
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
| | - Mona Bavarian
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United States
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21
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Morphology and crystallization behaviour of polyhydroxyalkanoates-based blends and composites: A review. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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22
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Tao L, Byrnes J, Varshney V, Li Y. Machine learning strategies for the structure-property relationship of copolymers. iScience 2022; 25:104585. [PMID: 35789847 PMCID: PMC9249671 DOI: 10.1016/j.isci.2022.104585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/26/2022] [Accepted: 06/07/2022] [Indexed: 11/15/2022] Open
Abstract
Establishing the structure-property relationship is extremely valuable for the molecular design of copolymers. However, machine learning (ML) models can incorporate both chemical composition and sequence distribution of monomers, and have the generalization ability to process various copolymer types (e.g., alternating, random, block, and gradient copolymers) with a unified approach are missing. To address this challenge, we formulate four different ML models for investigation, including a feedforward neural network (FFNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, and a combined FFNN/RNN (Fusion) model. We use various copolymer types to systematically validate the performance and generalizability of different models. We find that the RNN architecture that processes the monomer sequence information both forward and backward is a more suitable ML model for copolymers with better generalizability. As a supplement to polymer informatics, our proposed approach provides an efficient way for the evaluation of copolymers.
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Affiliation(s)
- Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | | | - Vikas Varshney
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433, USA
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
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23
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Wang R, Liu J, He X, Xie W, Zhang C. Decoding hexanitrobenzene (HNB) and 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as two distinctive energetic nitrobenzene compounds by machine learning. Phys Chem Chem Phys 2022; 24:9875-9884. [PMID: 35415730 DOI: 10.1039/d2cp00439a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Energetic materials (EMs) are a group of special energy materials, and it is generally full of safety risks and generally costs much to create new EMs. Thus, machine learning (ML)-aided discovery becomes highly desired for EMs, as ML is good at risk and cost reduction. This work decodes hexanitrobenzene (HNB) and 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as two distinctive energetic nitrobenzene compounds by ML, in combination with theoretical calculations. Based on a series of highly accurate models of density, heat of formation, bond dissociation energy and molecular flatness, the ML predictions show that HNB is the most energetic among ∼370 000 000 single benzene ring-containing compounds, while TATB possesses a moderate energy content and very high safety, as determined experimentally. This work exhibits the significant power of ML and presents an instructive procedure for using it in the field of EMs. The ML-aided design and highly efficient synthesis and fabrication combined strategy is expected to accelerate the discovery of new EMs.
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Affiliation(s)
- Rong Wang
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), PO Box 919-311, Mianyang, Sichuan 621900, China.
| | - Jian Liu
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), PO Box 919-311, Mianyang, Sichuan 621900, China.
| | - Xudong He
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), PO Box 919-311, Mianyang, Sichuan 621900, China.
| | - Weiyu Xie
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), PO Box 919-311, Mianyang, Sichuan 621900, China.
| | - Chaoyang Zhang
- Institute of Chemical Materials, China Academy of Engineering Physics (CAEP), PO Box 919-311, Mianyang, Sichuan 621900, China. .,Beijing Computational Science Research Center, Beijing 100048, China.
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24
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Pugar JA, Gang C, Huang C, Haider KW, Washburn NR. Predicting Young's Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2022; 14:16568-16581. [PMID: 35353501 DOI: 10.1021/acsami.1c24715] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Using a data set of 63 industrially relevant and complex elastomers, we demonstrate that accurate machine learning predictions are possible when monomer chemistry is used to estimate interactions at interchain length scales. Here, these features were used to accurately (r2 = 0.91) predict the Young's modulus of polyurethane and polyurethane-urea elastomers. Furthermore, by a query of the trained model for compositions that yield a target modulus within the range of accessible values, the capabilities of using this methodology as a design tool are demonstrated. The presented methodology could become increasingly useful in building models for materials with small data sets and may guide the interpretation of the underlying physicochemical forces.
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Affiliation(s)
- Joseph A Pugar
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Calvin Gang
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Christine Huang
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Karl W Haider
- Covestro LLC, 1 Covestro Circle, Pittsburgh, Pennsylvania 15205, United States
| | - Newell R Washburn
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, United States
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
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25
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Kuo PH, Du J. Atomistic Understanding of Ion Exchange Strengthening of Boroaluminosilicate Glasses: Insights from Molecular Dynamics Simulations and QSPR Analysis. J Phys Chem B 2022; 126:2060-2072. [PMID: 35201778 DOI: 10.1021/acs.jpcb.1c10928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Ion exchange (IOX) is an effective and widely used method to enhance mechanical properties of various glass products ranging from the touch screen of consumer electronics to window shields of airplanes and spacecrafts. IOX or chemical strengthening is achieved through the creation of a compressive surface layer on the glass product. Although widely studied experimentally, the fundamental understanding of the IOX strengthening process is still limited. In this work, we have applied large-scale atomistic simulations to understand IOX-induced mechanical property changes and their relation to the glass composition and structural characteristics. Two series of borosilicate glasses are studied to elucidate the composition effect, with boron oxide for silica and alumina for silica substitutions, respectively, on the mechanical properties of different levels of K+ to Na+ ion exchanges by using molecular dynamics (MD) simulations with a set of recently developed effective partial charge potentials. The linear network dilation coefficient (LNDC), a common measure of IOX behaviors, was calculated for each of the glass compositions. Quantitative structural property relationship (QSPR) analysis based on the MD-generated structural features was used to establish the structure-property correlations of mechanical and other properties. The results show strong composition dependence of the LNDC, hence the suitability of IOX strengthening. This behavior is discussed based on glass structure features of the glasses. It was found that glass compositions with a higher amount of mixed glass formers, higher network connectivity, and less complex components tend to show higher calculated LNDC and higher surface compressive stress. MD simulations, in combination with QSPR analysis, can thus provide atomistic insights into how the glass composition and structural characteristics affect IOX behaviors.
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Affiliation(s)
- Po-Hsuen Kuo
- Department of Material Science and Engineering, University of North Texas, Denton, Texas 76203, United States
| | - Jincheng Du
- Department of Material Science and Engineering, University of North Texas, Denton, Texas 76203, United States
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26
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Kadulkar S, Sherman ZM, Ganesan V, Truskett TM. Machine Learning-Assisted Design of Material Properties. Annu Rev Chem Biomol Eng 2022; 13:235-254. [PMID: 35300515 DOI: 10.1146/annurev-chembioeng-092220-024340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Sanket Kadulkar
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Zachary M Sherman
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Venkat Ganesan
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA;
| | - Thomas M Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA; .,Department of Physics, University of Texas at Austin, Austin, Texas, USA
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27
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Xu P, Chen H, Li M, Lu W. New Opportunity: Machine Learning for Polymer Materials Design and Discovery. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202100565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Pengcheng Xu
- Materials Genome Institute Shanghai University Shanghai 200444 China
| | - Huimin Chen
- Department of Mathematics College of Sciences Shanghai University Shanghai 200444 China
| | - Minjie Li
- Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China
| | - Wencong Lu
- Materials Genome Institute Shanghai University Shanghai 200444 China
- Department of Chemistry College of Sciences Shanghai University Shanghai 200444 China
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28
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Abstract
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
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Affiliation(s)
- Tarak K. Patra
- Department of Chemical Engineering,
Center for Atomistic Modeling and Materials Design and Center for
Carbon Capture Utilization and Storage, Indian Institute of Technology Madras, Chennai, TN 600036, India
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29
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Gianti E, Percec S. Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go? Biomacromolecules 2022; 23:576-591. [PMID: 35133143 DOI: 10.1021/acs.biomac.1c01436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.
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Affiliation(s)
- Eleonora Gianti
- Institute for Computational Molecular Science (ICMS), Temple University, Philadelphia, Pennsylvania 19122, United States.,Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Simona Percec
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
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Nguyen D, Tao L, Li Y. Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design. Front Chem 2022; 9:820417. [PMID: 35141207 PMCID: PMC8819075 DOI: 10.3389/fchem.2021.820417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/31/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics-polymeric configuration characterization, feed-forward property prediction, and inverse design-in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.
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Affiliation(s)
- Danh Nguyen
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
| | - Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Mansfield, CT, United States
- Polymer Program, Institute of Materials Science, University of Connecticut, Mansfield, CT, United States
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31
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Bejagam KK, Lalonde J, Iverson CN, Marrone BL, Pilania G. Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers. J Phys Chem B 2022; 126:934-945. [DOI: 10.1021/acs.jpcb.1c08354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Karteek K. Bejagam
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Jessica Lalonde
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Center for Biomolecular and Tissue Engineering, Duke University, Durham, North Carolina 27708, United States
| | - Carl N. Iverson
- Chemistry Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Babetta L. Marrone
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ghanshyam Pilania
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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Suwardi A, Wang F, Xue K, Han MY, Teo P, Wang P, Wang S, Liu Y, Ye E, Li Z, Loh XJ. Machine Learning-Driven Biomaterials Evolution. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2102703. [PMID: 34617632 DOI: 10.1002/adma.202102703] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.
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Affiliation(s)
- Ady Suwardi
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - FuKe Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Kun Xue
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ming-Yong Han
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Peili Teo
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Pei Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Shijie Wang
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Ye Liu
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Enyi Ye
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zibiao Li
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
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Cencer MM, Moore JS, Assary RS. Machine learning for polymeric materials: an introduction. POLYM INT 2021. [DOI: 10.1002/pi.6345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Morgan M Cencer
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Materials Science Division Argonne National Laboratory Lemont IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Jeffrey S Moore
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Rajeev S Assary
- Materials Science Division Argonne National Laboratory Lemont IL USA
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Characterization of Polyhydroxybutyrate-Based Composites Prepared by Injection Molding. Polymers (Basel) 2021; 13:polym13244444. [PMID: 34960995 PMCID: PMC8704503 DOI: 10.3390/polym13244444] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 11/24/2022] Open
Abstract
The waste generated by single-use plastics is often non-recyclable and non-biodegradable, inevitably ending up in our landfills, ecosystems, and food chain. Through the introduction of biodegradable polymers as substitutes for common plastics, we can decrease our impact on the planet. In this study, we evaluate the changes in mechanical and thermal properties of polyhydroxybutyrate-based composites with various additives: Microspheres, carbon fibers or polyethylene glycol (2000, 10,000, and 20,000 MW). The mixtures were injection molded using an in-house mold attached to a commercial extruder. The resulting samples were characterized using microscopy and a series of spectroscopic, thermal, and mechanical techniques. We have shown that the addition of carbon fibers and microspheres had minimal impact on thermal stability, whereas polyethylene glycol showed slight improvements at higher molecular weights. All of the composite samples showed a decrease in hardness and compressibility. The findings described in this study will improve our understanding of polyhydroxybutyrate-based composites prepared by injection molding, enabling advancements in integrating biodegradable plastics into everyday products.
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Tao L, Varshney V, Li Y. Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature. J Chem Inf Model 2021; 61:5395-5413. [PMID: 34662106 DOI: 10.1021/acs.jcim.1c01031] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model's generalization ability on an unlabeled data set is especially focused, and the model's sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks.
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Affiliation(s)
- Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Vikas Varshney
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright Patterson Air Force Base, Ohio 45433, United States
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States.,Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269, United States
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36
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Reis M, Gusev F, Taylor NG, Chung SH, Verber MD, Lee YZ, Isayev O, Leibfarth FA. Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis. J Am Chem Soc 2021; 143:17677-17689. [PMID: 34637304 PMCID: PMC10833148 DOI: 10.1021/jacs.1c08181] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure-property relationships. To tackle this challenge in the context of 19F magnetic resonance imaging (MRI) agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software-controlled, continuous polymer synthesis platform was developed to enable iterative experimental-computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The nonintuitive design criteria identified by ML, which were accomplished by exploring <0.9% of the overall compositional space, lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials.
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Affiliation(s)
- Marcus Reis
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Filipp Gusev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Nicholas G Taylor
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Sang Hun Chung
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Matthew D Verber
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Yueh Z Lee
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Olexandr Isayev
- Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Frank A Leibfarth
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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Zhong S, Zhang K, Bagheri M, Burken JG, Gu A, Li B, Ma X, Marrone BL, Ren ZJ, Schrier J, Shi W, Tan H, Wang T, Wang X, Wong BM, Xiao X, Yu X, Zhu JJ, Zhang H. Machine Learning: New Ideas and Tools in Environmental Science and Engineering. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:12741-12754. [PMID: 34403250 DOI: 10.1021/acs.est.1c01339] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.
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Affiliation(s)
- Shifa Zhong
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Kai Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Majid Bagheri
- Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Joel G Burken
- Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - April Gu
- Department of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14850, United States
| | - Baikun Li
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xingmao Ma
- Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas, 77843, United States
| | - Babetta L Marrone
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Joshua Schrier
- Department of Chemistry, Fordham University, The Bronx, New York 10458 United States
| | - Wei Shi
- School of Environment, Nanjing University, Nanjing, 210093 China
| | - Haoyue Tan
- School of Environment, Nanjing University, Nanjing, 210093 China
| | - Tianbao Wang
- Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xu Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bryan M Wong
- Department of Chemical & Environmental Engineering, Materials Science & Engineering Program, University of California-Riverside, Riverside, California 92521 United States
| | - Xusheng Xiao
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Xiong Yu
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States
| | - Huichun Zhang
- Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Sanches-Neto FO, Dias-Silva JR, Keng Queiroz Junior LH, Carvalho-Silva VH. " pySiRC": Machine Learning Combined with Molecular Fingerprints to Predict the Reaction Rate Constant of the Radical-Based Oxidation Processes of Aqueous Organic Contaminants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:12437-12448. [PMID: 34473479 DOI: 10.1021/acs.est.1c04326] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
We developed a web application structured in a machine learning and molecular fingerprint algorithm for the automatic calculation of the reaction rate constant of the oxidative processes of organic pollutants by •OH and SO4•- radicals in the aqueous phase-the pySiRC platform. The model development followed the OECD principles: internal and external validation, applicability domain, and mechanistic interpretation. Three machine learning algorithms combined with molecular fingerprints were evaluated, and all the models resulted in high goodness-of-fit for the training set with R2 > 0.931 for the •OH radical and R2 > 0.916 for the SO4•- radical and good predictive capacity for the test set with Rext2 = Qext2 values in the range of 0.639-0.823 and 0.767-0.824 for the •OH and SO4•- radicals. The model was interpreted using the SHAP (SHapley Additive exPlanations) method: the results showed that the model developed made the prediction based on a reasonable understanding of how electron-withdrawing and -donating groups interfere with the reactivity of the •OH and SO4•- radicals. We hope that our models and web interface can stimulate and expand the application and interpretation of kinetic research on contaminants in water treatment units based on advanced oxidative technologies.
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Affiliation(s)
| | | | | | - Valter Henrique Carvalho-Silva
- Instituto de Química, Universidade de Brasília, Caixa Postal 4478, Brasília 70904-970, Brazil
- Modeling of Physical and Chemical Transformations Division, Theoretical and Structural Chemistry Group, Goiás State University, Anápolis 75132-903, Brazil
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Xue K, Wang F, Suwardi A, Han MY, Teo P, Wang P, Wang S, Ye E, Li Z, Loh XJ. Biomaterials by design: Harnessing data for future development. Mater Today Bio 2021; 12:100165. [PMID: 34877520 PMCID: PMC8628044 DOI: 10.1016/j.mtbio.2021.100165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 01/18/2023] Open
Abstract
Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.
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Affiliation(s)
| | | | | | | | | | | | | | - Enyi Ye
- Institute of Materials Research and Engineering, A∗STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Zibiao Li
- Institute of Materials Research and Engineering, A∗STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering, A∗STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore
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40
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Kuenneth C, Schertzer W, Ramprasad R. Correction to “Copolymer Informatics with Multitask Deep Neural Networks”. Macromolecules 2021. [DOI: 10.1021/acs.macromol.1c01539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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41
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Jadoun S, Rathore DS, Riaz U, Chauhan NPS. Tailoring of conducting polymers via copolymerization – A review. Eur Polym J 2021. [DOI: 10.1016/j.eurpolymj.2021.110561] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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42
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Bejagam KK, Iverson CN, Marrone BL, Pilania G. Composition and Configuration Dependence of Glass-Transition Temperature in Binary Copolymers and Blends of Polyhydroxyalkanoate Biopolymers. Macromolecules 2021. [DOI: 10.1021/acs.macromol.1c00135] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Karteek K. Bejagam
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Carl N. Iverson
- Chemistry Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Babetta L. Marrone
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ghanshyam Pilania
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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43
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Predicting Polymers' Glass Transition Temperature by a Chemical Language Processing Model. Polymers (Basel) 2021; 13:polym13111898. [PMID: 34200505 PMCID: PMC8201381 DOI: 10.3390/polym13111898] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/03/2021] [Accepted: 06/04/2021] [Indexed: 12/14/2022] Open
Abstract
We propose a chemical language processing model to predict polymers' glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of a polymer's repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point '*'. Results show that the trained model demonstrates reasonable prediction performance on unseen polymer's Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of polymer Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer properties.
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Wang S, Cheng M, Zhou L, Dai Y, Dang Y, Ji X. QSPR modelling for intrinsic viscosity in polymer-solvent combinations based on density functional theory. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:379-393. [PMID: 33823697 DOI: 10.1080/1062936x.2021.1902387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Linear and nonlinear quantitative structure-property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers and solvents. Considering the strong correlation between intrinsic viscosity and weight, size, shape as well as topological structure of polymers and solvents, topological descriptors were also applied in this work. Meanwhile, the most appropriate polymer structure representation was investigated by considering 1-5 monomeric repeating units. The molecular descriptors were first screened by using the genetic algorithms-multiple linear regression (GA-MLR), with coefficient of determinations (r2) of 0.78 and 0.83 for the training set and the prediction set, respectively. The support vector machine model (SVM) model based on the selected descriptors subset showed a r2 value of 0.95 for the training set and 0.93 for the prediction set. All statistical results suggest that the established QSPR models have good predictability. Furthermore, a new test set obtained from the literature was used for further validation. The r2 values were 0.81 for the MLR model and 0.90 for the SVM model.
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Affiliation(s)
- S Wang
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
| | - M Cheng
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
| | - L Zhou
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
| | - Y Dai
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
| | - Y Dang
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
| | - X Ji
- Department of Chemical Engineering, Sichuan University, Chengdu, PR China
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Polymer informatics with multi-task learning. PATTERNS (NEW YORK, N.Y.) 2021; 2:100238. [PMID: 33982028 PMCID: PMC8085610 DOI: 10.1016/j.patter.2021.100238] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/12/2021] [Accepted: 03/16/2021] [Indexed: 12/25/2022]
Abstract
Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives. We overcome data scarcity in polymer datasets using multi-task models Our approach is expected to become the preferred training method for materials data We derive chemical guidelines for the design of application specific polymers
Polymers display extraordinary diversity in their chemistry, structure, and applications. However, finding the ideal polymer possessing the right combination of properties for a given application is non-trivial as the chemical space of polymers is practically infinite. This daunting search problem can be mitigated by surrogate models, trained using machine learning algorithms on available property data, that can make instantaneous predictions of polymer properties. In this work, we present a versatile, interpretable, and scalable scheme to build such predictive models. Our “multi-task learning” approach is used for the first time within materials informatics and efficiently, effectively, and simultaneously learns and predicts multiple polymer properties. This development is expected to have a significant impact on data-driven materials discovery.
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Tao L, Chen G, Li Y. Machine learning discovery of high-temperature polymers. PATTERNS (NEW YORK, N.Y.) 2021; 2:100225. [PMID: 33982020 PMCID: PMC8085602 DOI: 10.1016/j.patter.2021.100225] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/21/2021] [Accepted: 03/02/2021] [Indexed: 01/26/2023]
Abstract
To formulate a machine learning (ML) model to establish the polymer's structure-property correlation for glass transition temperatureT g , we collect a diverse set of nearly 13,000 real homopolymers from the largest polymer database, PoLyInfo. We train the deep neural network (DNN) model with 6,923 experimentalT g values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknownT g values of polymers with distinct molecular structures, in comparison with molecular dynamics simulations and experimental results. With the validated transferability and generalization ability, the ML model is utilized for high-throughput screening of nearly one million hypothetical polymers. We identify more than 65,000 promising candidates withT g > 200°C, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo). The discovery of this large number of promising candidates will be of significant interest in the development and design of high-temperature polymers.
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Affiliation(s)
- Lei Tao
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Guang Chen
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
| | - Ying Li
- Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA
- Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA
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Luengo GS, Fameau AL, Léonforte F, Greaves AJ. Surface science of cosmetic substrates, cleansing actives and formulations. Adv Colloid Interface Sci 2021; 290:102383. [PMID: 33690071 DOI: 10.1016/j.cis.2021.102383] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 02/04/2021] [Accepted: 02/06/2021] [Indexed: 12/22/2022]
Abstract
The development of shampoo and cleansing formulations in cosmetics is at a crossroads due to consumer demands for better performing, more natural products and also the strong commitment of cosmetic companies to improve the sustainability of cosmetic products. In order to go beyond traditional formulations, it is of great importance to clearly establish the science behind cleansing technologies and appreciate the specificity of cleansing biological surfaces such as hair and skin. In this review, we present recent advances in our knowledge of the physicochemical properties of the hair surface from both an experimental and a theoretical point of view. We discuss the opportunities and challenges that newer, sustainable formulations bring compared to petroleum-based ingredients. The inevitable evolution towards more bio-based, eco-friendly ingredients and sustainable formulations requires a complete rethink of many well-known physicochemical principles. The pivotal role of digital sciences and modelling in the understanding and conception of new ingredients and formulations is discussed. We describe recent numerical approaches that take into account the specificities of the hair surface in terms of structuration, different methods that study the adsorption of formulation ingredients and finally the success of new data-driven approaches. We conclude with practical examples on current formulation efforts incorporating bio-surfactants, controlling foaming and searching for new rheological properties.
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A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers. MATERIALS 2020; 13:ma13245701. [PMID: 33327598 PMCID: PMC7765086 DOI: 10.3390/ma13245701] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/06/2020] [Accepted: 12/09/2020] [Indexed: 12/20/2022]
Abstract
The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure-property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.
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Tu KH, Huang H, Lee S, Lee W, Sun Z, Alexander-Katz A, Ross CA. Machine Learning Predictions of Block Copolymer Self-Assembly. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2005713. [PMID: 33206426 DOI: 10.1002/adma.202005713] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Directed self-assembly of block copolymers is a key enabler for nanofabrication of devices with sub-10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self-assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self-assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.
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Affiliation(s)
- Kun-Hua Tu
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hejin Huang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sangho Lee
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Wonmoo Lee
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Zehao Sun
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alfredo Alexander-Katz
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Caroline A Ross
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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50
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Machine learning glass transition temperature of styrenic random copolymers. J Mol Graph Model 2020; 103:107796. [PMID: 33248342 DOI: 10.1016/j.jmgm.2020.107796] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 11/01/2020] [Accepted: 11/02/2020] [Indexed: 12/18/2022]
Abstract
For styrenic random copolymers, the glass transition temperature, Tg, is an important thermophysical parameter, which is sometimes difficult to measure and determine by experiments. Approaches based on data-driven modeling provide alternative methods to predict Tg in a fast and robust way. The Gaussian process regression (GPR) model is investigated to present the statistical relationship between important quantum chemical descriptors and glass transition temperature for styrenic random copolymers. 48 samples with Tg that have been measured experimentally are explored, which range from 246 K to 426 K. The modeling approach demonstrates high accuracy and stability, and provides a novel and promising tool for efficient and low-cost estimations of copolymer Tg values.
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