Emerging Trends in Machine Learning: A Polymer Perspective

ACS Polymers Au, Journal Year: 2023, Volume and Issue: 3(3), P. 239 - 258

Published: Jan. 18, 2023

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight unique challenges presented by polymers how field is addressing them. We focus on emerging trends with an emphasis topics that have received less attention review literature. Finally, provide outlook for field, outline important areas science discuss advances from greater material community.

Language: Английский

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Applied machine learning as a driver for polymeric biomaterials design

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Aug. 10, 2023

Abstract Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity commercial polymers used medicine stunningly low. Considerable time resources have been extended over years towards development new polymeric biomaterials which address unmet needs left by current generation medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity this field bypass need for trial-and-error synthesis, thus reducing invested into discoveries critical advancing treatments. Current efforts pioneering applied ML polymer design employed combinatorial high throughput experimental data availability concerns. However, lack available standardized characterization parameters relevant medicine, including degradation biocompatibility, represents a nearly insurmountable obstacle ML-aided biomaterials. Herein, we identify gap at intersection biomedical design, highlight works junction more broadly provide outlook on challenges future directions.

Language: Английский

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Revolutionizing drug formulation development: The increasing impact of machine learning

Advanced Drug Delivery Reviews, Journal Year: 2023, Volume and Issue: 202, P. 115108 - 115108

Published: Sept. 27, 2023

Language: Английский

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Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning

ACS Applied Bio Materials, Journal Year: 2023, Volume and Issue: 7(2), P. 510 - 527

Published: Jan. 26, 2023

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, potential as functional materials is also inhibited by complexity, which complicates rational or brute-force design realization. In recent years, machine learning has emerged a useful tool for facilitating via efficient modeling structure–property relationships domain interest. this Spotlight, we discuss emergence data-driven polymers that can be deployed biomaterials particular emphasis complex copolymer systems. We outline developments, well our own contributions takeaways, related high-throughput data generation polymer systems, methods surrogate learning, paradigms property optimization design. Throughout discussion, highlight key aspects successful strategies other considerations will relevant future polymer-based target properties.

Language: Английский

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Machine Learning in Polymer Research

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Machine learning is increasingly being applied in polymer chemistry to link chemical structures macroscopic properties of polymers and identify patterns the that help improve specific properties. To facilitate this, a dataset needs be translated into machine readable descriptors. However, limited inadequately curated datasets, broad molecular weight distributions, irregular configurations pose significant challenges. Most off shelf mathematical models often need refinement for applications. Addressing these challenges demand close collaboration between chemists mathematicians as must formulate research questions terms while are required refine This review unites both disciplines address curation hurdles highlight advances synthesis modeling enhance data availability. It then surveys ML approaches used predict solid-state properties, solution behavior, composite performance, emerging applications such drug delivery polymer-biology interface. A perspective field concluded importance FAIR (findability, accessibility, interoperability, reusability) integration theory discussed, thoughts on machine-human interface shared.

Language: Английский

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Advancements in Machine Learning and Artificial Intelligence in Polymer Science: A Comprehensive Review

Macromolecular Symposia, Journal Year: 2025, Volume and Issue: 414(1)

Published: Feb. 1, 2025

Abstract Technology, health care, and transport are merely some of the industries that historically rely on polymer‐based materials. In past centuries, creation innovative polymer materials has been dependent upon extensive experiments error procedures require an number resources as well time. With objective to explore transformative potential machine learning (ML) artificial intelligence (AI) in material discovery, design, optimization, this paper explores integration ML AI research. Researchers able speed development new with improved properties functionalities by utilizing sophisticated algorithms computational models. The use research is examined, a focus how these technologies may stimulate innovation expand science

Language: Английский

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Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical conductivity and gas sensing performance

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 12(4), P. 2209 - 2236

Published: Dec. 11, 2023

This study employs various machine learning algorithms to model the electrical conductivity and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a comprehensive dataset gathered from over 100 references.

Language: Английский

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Machine learning in drug delivery

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 373, P. 23 - 30

Published: June 27, 2024

For decades, drug delivery scientists have been performing trial-and-error experimentation to manually sample parameter spaces and optimize release profiles through rational design. To enable this approach, spend much of their career learning nuanced drug-material interactions that drive system behavior. In relatively simple systems, design criteria allow us fine tune efficacious therapies. However, as materials drugs become increasingly sophisticated non-linear compounding effects, the field is suffering Curse Dimensionality which prevents from comprehending complex structure-function relationships. past, we embraced complexity by implementing high-throughput screens increase probability finding ideal compositions. brute force method was inefficient led many abandon these fishing expeditions. Fortunately, methods in data science including artificial intelligence / machine (AI/ML) are providing analytical tools model ascertain quantitative Oration, I speak potential value with particular focus on polymeric systems. Here, do not suggest AI/ML will simply replace mechanistic understanding Rather, propose should be yet another useful tool lab navigate spaces. The recent hype around breathtaking potentially over inflated, but poised revolutionize how perform science. Therefore, encourage readers consider adopting skills applying own problems. If done successfully, believe all realize a paradigm shift our approach delivery.

Language: Английский

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Machine learning for analyses and automation of structural characterization of polymer materials

Progress in Polymer Science, Journal Year: 2024, Volume and Issue: 153, P. 101828 - 101828

Published: May 3, 2024

Language: Английский

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Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

Skin Research and Technology, Journal Year: 2024, Volume and Issue: 30(9)

Published: Aug. 27, 2024

Abstract Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged lost tissues organs due accidents, diseases, aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, evaluating scaffolds, cells, tissues, organs. Artificial intelligence (AI) machines software can be effective in all areas where computers play a role. Methods The “artificial intelligence,” “machine learning,” “tissue engineering,” “clinical evaluation,” “scaffold” keywords used for searching various databases articles published from 2000 2024 were evaluated. Results combination tissue AI has created new generation technological advancement biomedical industry. Experience been refined using advanced design manufacturing techniques. Advances AI, particularly deep learning, offer opportunity improve scientific understanding clinical outcomes TERM. Conclusion findings research show high potential machine robots selection, design, fabrication organs, their analysis, characterization, evaluation after implantation. tool accelerate introduction products bedside. Highlights capabilities artificial ways stages not only solve existing limitations, but also processes, increase efficiency precision, reduce costs, complications transplantation. ML predicts which technologies have most efficient easiest path enter market clinic. use along with these imaging techniques lead improvement diagnostic information, reduction operator errors when reading images, image analysis (such as classification, localization, regression, segmentation).

Language: Английский

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