Machine-learning predictions of polymer properties with Polymer Genome
Huan Doan Tran,Chiho Kim,Lihua Chen,Anand Chandrasekaran,Rohit Batra,Shruti Venkatram,Deepak Kamal,Jordan P. Lightstone,Rishi Gurnani,Pranav Shetty,Manav Ramprasad,Julia Laws,Madeline Shelton,Rampi Ramprasad +13 more
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TLDR
An overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage is provided, and a series of pedagogical examples are provided to demonstrate how PolymerGenome can be used to predict dozens of polymer properties, appropriate for a range of applications.Abstract:
Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.read more
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Polymer informatics: Current status and critical next steps
Lihua Chen,Ghanshyam Pilania,Rohit Batra,Tran Doan Huan,Chiho Kim,Christopher Kuenneth,Rampi Ramprasad +6 more
TL;DR: Emergent components of this polymer informatics ecosystem are reviewed and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions are discussed.
Journal ArticleDOI
Machine learning discovery of high-temperature polymers.
Lei Tao,Guang Chen,Ying Li +2 more
TL;DR: More than 65,000 promising candidates with Tg > 200°C are identified, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo).
Journal ArticleDOI
Machine-Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis.
Marcus H. Reis,Filipp Gusev,Nicholas G Taylor,Sang Hun Chung,Matthew D. Verber,Yueh Z. Lee,Olexandr Isayev,Frank A. Leibfarth +7 more
TL;DR: In this paper, 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.
Journal ArticleDOI
Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature.
Lei Tao,Vikas Varshney,Ying Li +2 more
TL;DR: In this paper, a comparison of different ML techniques and examine the key factors that affect the model performance was carried out by compiling 79 different ML models and training them on a large and diverse data set.
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Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks
TL;DR: This work introduces techniques for the machine-learned prediction of methane isotherms in MOFs and uses these models to search for novel (from both a structural and chemical point of view) and present potential MOF−methane uptake structure−property relationships.
References
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