Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

Mashrafee Aryan; Daniel Struble; Felix  A. Campbell; Saroj Upreti; Ashik Abedin; Aahil Khambhawla; Jeetain Mittal; Michael Dimitriyev; Emily Pentzer; Svetlana Sukhishvili; Xiaodan Gu; Boran Ma

doi:10.1039/d5lp00380f

January 1, 2026RSC Applied PolymersOpen Access

Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

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Key Points

This research aims to enhance the performance and explainability of machine learning models in polymer development.
Utilized high throughput experimentation to gather data for ML models.
Applied machine learning algorithms to analyze experimental results and improve predictions.
Focused on application-specific polymer properties to tailor development processes.
Observed significant improvements in model performance metrics, including accuracy and speed of predictions.
Demonstrated enhanced explainability of ML models in the context of polymer applications.

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Implication

Demonstrates improved model performance in polymer development, suggesting enhanced industry applications.

Abstract

Machine learning (ML) has heavily influenced the way scientific study is done with demonstrated successes in nearly every field. However, furthering performance and explainability of ML models in increasingly complex...

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Cite This Study

Aryan et al. (2026) studied this question.

synapsesocial.com/papers/6a1a7f760307b78509431b65 https://doi.org/10.1039/d5lp00380f

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Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

Key Points

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Implication

Abstract

Discussion

Cite This Study

Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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