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Main Authors: Chen, Jihua, Christakopoulos, Panagiotis, Chen, Karuna D., Ivanov, Ilia N., Advincula, Rigoberto
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2601.09027
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author Chen, Jihua
Christakopoulos, Panagiotis
Chen, Karuna D.
Ivanov, Ilia N.
Advincula, Rigoberto
author_facet Chen, Jihua
Christakopoulos, Panagiotis
Chen, Karuna D.
Ivanov, Ilia N.
Advincula, Rigoberto
contents Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.
format Preprint
id arxiv_https___arxiv_org_abs_2601_09027
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications
Chen, Jihua
Christakopoulos, Panagiotis
Chen, Karuna D.
Ivanov, Ilia N.
Advincula, Rigoberto
Materials Science
Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.
title Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications
topic Materials Science
url https://arxiv.org/abs/2601.09027