Work

Democratizing machine learning in chemistry with community-engaged test sets

Year: 2025

Type: article

Abstract: Machine learning (ML) is increasingly central to chemical discovery, yet most efforts remain confined to distributed and isolated research groups, limiting external validation and community engagement.

Source: Digital Discovery

DOI: 10.1039/d5dd00424a

Authors: Jason L. Wu, David M. Friday, Changhyun Hwang, Seungjoo Yi, Tiara C. Torres-Flores, Martin D. Burke, Ying Diao, Charles M. Schroeder, Nicholas E. Jackson

Institutions: University of Illinois Urbana-Champaign, Carl R. Woese Institute for Genomic Biology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: Molecule Maker Lab Institute (MMLI): An AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing, AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing: Molecule Maker Lab Institute (MMLI)

OpenAlex ID: W7105999032

FWCI: 0.416

Cites: 25

Cited by: 2

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