{
  "annotation_id": "db890e1f-bcb7-4ea8-8d10-3fda52e83433",
  "date_created": "2026-02-17T05:53:20.469000Z",
  "date_modified": "2026-02-17T05:53:20.469000Z",
  "file_hash": "83ed690f8a669dd8a7665986c77828d406e2908198be63947605db21bf8b941b",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.09027",
    "authors": [
      "Jihua Chen",
      "Panagiotis Christakopoulos",
      "Karuna D. Chen",
      "Ilia N. Ivanov",
      "Rigoberto Advincula"
    ],
    "categories": [
      "cond-mat.mtrl-sci"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications",
    "url": "https://arxiv.org/abs/2601.09027",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ef96218c-2a69-46a0-9f7e-99d988ccdb0b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}