An official website of the United States government
In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just step into this field and deepen the understanding for the people who are already in this field.
more »
« less
Machine Learning for Developing Sustainable Polymers
Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial‐and‐error methods remains inefficient and resource‐intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field.
more »
« less
- Award ID(s):
- 2404069
- PAR ID:
- 10614243
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Chemistry – A European Journal
- Volume:
- 31
- Issue:
- 32
- ISSN:
- 0947-6539
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Abstract Over 8 billion tons of plastic have been produced to date, and a 100% reclamation recycling strategy is not foreseeable. This review summarizes how the mechanochemistry of polymers may contribute to a sustainable polymer future by controlling the degradation not only of de novo developed designer polymers but also of plastics in existing waste streams. The historical development of polymer mechanochemistry is presented while highlighting current examples of mechanochemically induced polymer degradation. Additionally, theoretical and computational frameworks are discussed that may lead to the discovery and better understanding of new mechanochemical reactions in the future. This review takes into account technical and engineering perspectives converging the fields of trituration and polymer mechanochemistry with a particular focus on the fate of commodity polymers and potential technologies to monitor mechanochemical reactions while they occur. Therefore, a unique perspective of multiple communities is presented, highlighting the need for future transdisciplinary research to tackle the high-leverage parameters governing an eventually successful mechanochemical degradation approach for a circular economy.more » « less
-
Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the “next-best experiment.” In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures ( T g ). Starting from an initial small dataset of polymer T g measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add T g measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the “next-best experiment.” The active-learning workflow terminates once 10 polymers possessing a T g greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high- T g polymers for this particular demonstrative materials design challenge.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/chem.202500718
