The most important work I have done in my career is to warn the world about the dangers of nuclear weapons. While the Soviet‐American nuclear arms race has ended, and the 2017 United Nations Treaty on the Prohibition of Nuclear Weapons is on its way to ratification, there is still much more work to do. The smoke from fires ignited by nuclear weapons would block out the Sun, cooling Earth's surface. The United States and Russia can still produce a nuclear winter, killing most crops and producing a global famine. I have worked on this because I have been open to new opportunities, have used my scientific expertise to apply to important problems, and am sensitive to issues that present dangers to society. Having spent more than $20 million of the public's money on my research, I feel an obligation to warn society of dangers I discover, and focus on that communication as an intimate part of my scientific efforts. And nuclear winter research has been very good to me. It led to meeting Sherri West (my wife), Carl Sagan, and Fidel Castro; to getting my current job at Rutgers; and to being a participant in the International Campaign to Abolish Nuclear Weapons, which won the 2017 Nobel Peace Prize. Taking advantage of chance encounters at the Fall American Geophysical Union Meetings has been an important factor in this tale.
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.
more » « less- NSF-PAR ID:
- 10238083
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Israel Journal of Chemistry
- Volume:
- 62
- Issue:
- 1-2
- ISSN:
- 0021-2148
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localized
d orf electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes.This article is categorized under:
Electronic Structure Theory > Density Functional Theory
Software > Molecular Modeling
Computer and Information Science > Chemoinformatics
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null (Ed.)This is the story of a career in theoretical chemistry during a time of dramatic changes in the field due to phenomenal growth in the availability of computational power. It is likewise the story of the highly gifted graduate students and postdoctoral fellows that I was fortunate to mentor throughout my career. It includes reminiscences of the great mentors that I had and of the exciting collaborations with both experimentalists and theorists on which I built much of my research. This is an account of the developments of exciting scientific disciplines in which I was involved: vibrational spectroscopy, molecular reaction mechanisms and dynamics, e.g., in atmospheric chemistry, and the prediction of new, exotic molecules, in particular noble gas molecules. From my very first project to my current work, my career in science has brought me the excitement and fascination of research. What a wonderful pursuit!more » « less
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