Are we there yet? Adventures on a road trip through machine learning as a computational chemist
Name
020902_1_5.0297853.pdf
Description
Published version
Size
5.88 MB
Format
Adobe PDF
Checksum (MD5)
75f7353904cdbabb81da02b9abfc2daa
Author(s)
Kulik, Heather J
Date Issued
October 8, 2025
Journal
APL Computational Physics
Publisher
AIP Publishing
Citation
Heather J. Kulik; Are we there yet? Adventures on a road trip through machine learning as a computational chemist. APL Computational Physics 1 December 2025; 1 (2): 020902.
Version
Final published version
Abstract
Over the past two decades, the integration of machine learning (ML) into theoretical and computational chemistry has transformed the scale and scope of discovery that is possible on a computer. In this Perspective, I share my personal journey from early density functional theory method development for small transition metal complexes to the building of automated workflows and ML models for catalysis, redox chemistry, and materials design. I will describe my unlikely path toward machine learning starting from minimal models of catalysts and the use of structural databases to gain data-driven insights. I will then describe our experiences in training machine learning models for discovery, including active learning and descriptor-based approaches that enabled data-driven exploration despite limited experimental reference data for open-shell transition metal complexes. As an example of overcoming these limitations, I will describe our more recent efforts that culminated in experimental validation of our computational predictions. Along the way, challenges in data curation, DFT method sensitivity, and synthetic realism have shaped the trajectory of the field. I conclude with reflections on the rapid rise of generative AI, agentic workflows, and the enduring need to ask questions that truly advance molecular discovery.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/5.0297853
