The computer science community’s contributions to the Nobel Prizes in Chemistry and Physics take center stage in this month’s Focus issue.
Each year, as October approaches, the excitement and speculation surrounding the announcement of new Nobel Laureates ripples through the scientific community. By highlighting work that has had “the greatest benefit to mankind”, the Nobel Prize never fails to inspire scientists in all fields and rekindle the passion for research and scientific progress. For computer scientists, this inspiration can take many forms, because the field of application possibilities of computer tools is constantly expanding.
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Last year, we looked at the history of the Nobel Prize and wondered how computer science had influenced, directly and indirectly, previously awarded subjects, as well as how it had been explicitly recognized by awards. In this Focus issue, we have extended these discussions by speaking with various experts – including Nobel laureates, researchers who have worked with Nobel laureates in the past, and a member of the Nobel Prize Committee – so as not only to celebrate the diverse contributions of computer science to the fields of chemistry and physics, but also to look ahead to the future and the challenges ahead. While many influential models have been awarded a Nobel Prize, we focus here on contributions that made the existing theory practically computable, sometimes with only limited computing power available at the time.
One of the awards we highlight is the 1998 Nobel Prize in Chemistry, which was awarded to two pioneers in the field of quantum chemistry: Walter Kohn and John Pople. Kohn was recognized for the development of density functional theory (DFT), while Pople was recognized for the development of computational methods in quantum chemistry. In practice, Kohn’s work has made quantum chemical calculations computationally feasible. Pople’s quantum chemistry software – Gaussian – transformed the field of computational chemistry, as it allowed researchers to theoretically study molecules, their properties, and their interactions, bringing Kohn’s DFT to life in a practical and efficient way. . We had the opportunity to chat with two researchers who have worked with these Nobel laureates in the past and who, in turn, have also greatly influenced the field: Lu Sham and Martin Head-Gordon.
Lu Sham worked closely with Kohn on what are known as the Kohn-Sham equations, which offered a simplified approach to approximating kinetic energy and made DFT a more convenient tool for answering questions materials science and chemistry. This work was a big contributor to Kohn’s Nobel Prize, and Sham didn’t expect it to be so influential when they were originally working on this theory: “At first I didn’t think it would bloom so much. that she did,” Sham noted. during our conversation with him. Indeed, their work has been an important and fundamental contribution to the scientific community and, as such, there are still many challenges ahead. For example, one of these challenges is the use of DFT methods in quantum materials and strongly correlated systems. A commentary by Alex Zunger discusses this challenge in more detail, as well as the potential and opportunities for bridging the gap between DFT and quantum materials.
Martin Head-Gordon – who was mentored by Pople during his PhD – is known in the field of chemistry for his work on DFT and density functionals, as well as for his contributions to the development of Gaussian and, later, of Q-Chem. Head-Gordon’s career was deeply influenced by his time working with Pople: “He was the biggest scientific influence on me,” Head-Gordon said. Today, Gaussian and Q-Chem are still two of the most widely used commercially available quantum chemistry software packages. For Q-Chem, there are still many untapped horizons to explore, as explained by Head-Gordon: “We are moving towards more complex systems in various ways, while we seek to improve the basic algorithms as our mission main.”
We also highlight, in this Focus issue, the 2013 Nobel Prize in Chemistry, which was awarded to Arieh Warshel, Michael Levitt and Martin Karplus for their development of multi-scale models for complex chemical systems. The establishment of quantum mechanical/molecular mechanical (QM/MM) methods has enabled scientists in the field to accurately model large systems computationally. We spoke with Warshel and discussed his current research, as well as some of the challenges he has faced in his research career. For example, he noted that it has been difficult to “convince people that computers are the only way to definitively understand how enzymes work.” Nevertheless, the work of Warshel, Levitt and Karplus has been widely accepted by the research community: “What captivated the public relatively quickly was the simple idea of separating QM and MM, rather than a way to make it more accurate,” Warshel said. Indeed, this simplicity has allowed QM/MM methods to remain indispensable for computer scientists of various disciplines.
The important contributions of the computational science community to the field of physics are also discussed in this issue. We had the opportunity to speak with Saul Perlmutter, winner of the 2011 Nobel Prize in Physics. Perlmutter received the Nobel Prize for the discovery of the acceleration of the expansion of the Universe through the observation of distant supernovae. Part of the work involved identifying tens of thousands of galaxies from wide-field images and then identifying supernova appearances in those galaxies. As Perlmutter discussed with Computational science of nature, “It was a perfect job for a computer to do.” The job required a feat of computing prowess, and for Perlmutter, the timing couldn’t have been better: “It was just the right time, technologically speaking, to do this,” Perlmutter asserted. “Computer technology was a key part of this work.”
More recently, in 2021, the Nobel Prize awarded Giorgio Parisi, Syukuro Manabe and Klaus Hasselmann for their work on understanding complex physical systems, such as the Earth’s climate, and making them practically computable. We had the opportunity to speak with a member of the Nobel Committee for Physics, John Wettlaufer, who took a look behind the curtain of the Nobel Committee’s decision by discussing how their contributions stood out. To advance the field and address the challenges of climate change, Wettlaufer stressed that the focus of computing should be on data and data-driven approaches, which requires multidisciplinary collaborations: “It sounds like a cliché, but it really doesn’t work if people don’t speak each other’s language,” Wettlaufer noted. A commentary by Mojib Latif also discusses current challenges and how advanced Earth system models and global climate can answer pressing questions to mitigate anthropogenic effects on climate change and global warming.
Other Nobel Prize winners may not have directly recognized the contributions of the computer science community, but their corresponding research has been greatly enriched by computation, such as the 2020 Nobel Prize in Chemistry, which honored Emmanuelle Charpentier and Jennifer Doudna for their development of a method for genome editing using CRISPR-Cas9 genetic scissors. A commentary by Lei Stanley Qi – who had Doudna as one of his academic advisors during his PhD – discusses how computational analysis aided in the discovery of CRISPR systems through understanding CRISPR’s generic immunity function against viral infection, and how computer science is enabling further development of CRISPR as a genome editing tool.
Interestingly, there were common themes discussed in these conversations and plays. One of the recurring messages was the importance of a collaborative relationship between experimenters and theorists. For example, Sham said DFT can be used as a first run to further guide experimental work, and Perlmutter noted that experiments and observations have helped solidify computational predictions in his field. But, as Head-Gordon noted, while a feedback cycle between theory and experimentation is important, it doesn’t come without challenges, like trying to ensure as much as possible that what’s being modeled is also seen experimentally. Another recurring message was the importance of multidisciplinary research: for example, Wettlaufer stressed that multidisciplinary collaborations are needed to get the most out of data-driven climate research, while Qi noted that the computational tools developed in other areas, such as protein structure prediction algorithms, could significantly increase the potential of CRISPR technology. These commonalities in the discussions of this issue suggest that, despite the fact that the contributions highlighted here come from different fields and are varied in nature (from modeling and theory to software development), they all have common characteristics. related and face similar challenges, which reflect the nature of computational science research.
In anticipation of the 2022 Nobel Prize announcements, which will take place October 3-10, we invite you to explore our Focus issue and its many conversations and commentaries on how the contributions of computational science have shaped science and paved the way. to future developments.