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Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry.
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Comparative Cognition: Perspectives, Challenges, and Prospects
The publication of the centennial year of the Journal of Comparative Psychology is an occasion to reflect on the state of our discipline. In this article, I focus on one aspect of comparative psychology, namely comparative cognition. This focus stems from my long-standing interest in comparative cognition. The trends and challenges in comparative cognition share many of the trends and challenges in the broader field of comparative psychology. In the first part of this article, I outline my perspective on the field. Next, I consider challenges. I end with a section on prospects for the future.
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- Award ID(s):
- 1946039
- PAR ID:
- 10208302
- Date Published:
- Journal Name:
- Journal of comparative psychology
- ISSN:
- 1939-2087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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