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Center to advance predictive simulation research established at MIT Schwarzman College of Computing

MIT is among nine universities selected as part of a program sponsored by the DoE to support science-based modeling and simulation and exascale computing technologies.
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Artist's rendering of a hypersonic vehicle glowing with heat
Caption:
CESMIX will focus on exascale simulation of materials in hypersonic flow environments. It will also drive the development of new predictive simulation paradigms and computer science tools for the exascale. Pictured is an illustration of a hypersonic vehicle, where materials needed for thermal protection are subject to extreme loading.
Credits:
Image courtesy of the U.S. Defense Advanced Research Projects Agency.

Understanding the degradation of materials in extreme environments is a scientific problem with major technological applications, ranging from spaceflight to industrial and nuclear safety. Yet it presents an intrinsic challenge: Researchers cannot easily reproduce these environments in the laboratory or observe essential degradation processes in real-time. Computational modeling and simulation have consequently become indispensable tools in helping to predict the behavior of complex materials across a range of strenuous conditions.

At MIT, a new research effort aims to advance the state-of-the-art in predictive simulation as well as shape new interdisciplinary graduate education programs at the intersection of computational science and computer science.

Strengthening engagement with the sciences

The Center for Exascale Simulation of Materials in Extreme Environments (CESMIX) — based at the Center for Computational Science and Engineering (CCSE) within the MIT Stephen A. Schwarzman College of Computing — will bring together researchers in numerical algorithms and scientific computing, quantum chemistry, materials science, and computer science to connect quantum and molecular simulations of materials with advanced programming languages, compiler technologies, and software performance engineering tools, underpinned by rigorous approaches to statistical inference and uncertainty quantification.

“One of the goals of CESMIX is to build a substantive link between computer science and computational science and engineering, something that historically has been hard to do, but is sorely needed,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. “The center will also provide opportunities for faculty, researchers, and students across MIT to interact intellectually and create a new synthesis of different disciplines, which is central to the mission of the college.”

Leading the project as principal investigator is Youssef Marzouk, professor of aeronautics and astronautics and co-director of CCSE, which was renamed from the Center of Computational Engineering in January to reflect its strengthening engagement with the sciences at MIT. Marzouk, who is also a member of the Statistics and Data Science Center, notes that “CESMIX is trying to do two things simultaneously. On the one hand, we want to solve an incredibly challenging multiscale simulation problem, harnessing quantum mechanical models of complex materials to achieve unprecedented accuracy at the engineering scale. On the other hand, we want to create tools that make development and holistic performance engineering of the associated software stack as easy as possible, to achieve top performance on the coming generation of exascale computational hardware.”

The project involves participation from an interdisciplinary cohort of eight faculty members, serving as co-PIs, and research scientists spanning multiple labs and departments at MIT. The full list of participants includes:

  • Youssef Marzouk, PI, professor of aeronautics and astronautics and co-director of CCSE;
  • Saman Amarasinghe, co-PI, professor of computer science and engineering;
  • Alan Edelman, co-PI, professor of applied mathematics;
  • Nicolas Hadjiconstantinou, co-PI, professor of mechanical engineering and co-director of CCSE;
  • Asegun Henry, co-PI, associate professor of mechanical engineering;
  • Heather Kulik, co-PI, associate professor of chemical engineering;
  • Charles Leiserson, co-PI, the Edwin Sibley Webster Professor of Electrical Engineering;
  • Jaime Peraire, co-PI, the H.N. Slater Professor of Aeronautics and Astronautics;
  • Cuong Nguyen, principal research scientist of aeronautics and astronautics;
  • Tao B. Schardl, research scientist in the Computer Science and Artificial Intelligence Laboratory; and
  • Mehdi Pishahang, research scientist of mechanical engineering.

MIT was among a total of nine universities selected as part of the Predictive Science Academic Alliance Program (PSAAP) III to form a new center to support science-based modeling and simulation and exascale computing technologies. This is the third time that PSAAP centers have been awarded by the U.S. Department of Energy’s National Nuclear Security Administration (DoE/NNSA) since the program launched in 2008 and is the first time that the Institute has ever been selected. MIT is one of just two institutions nationwide chosen to establish a Single-Discipline Center in this round and will receive up to $9.5 million in funding through a cooperative agreement over five years.

Advancing predictive simulation

CESMIX will focus on exascale simulation of materials in hypersonic flow environments. It will also drive the development of new predictive simulation paradigms and computer science tools for the exascale. Researchers will specifically aim to predict the degradation of complex (disordered and multi-component) materials under extreme loading inaccessible to direct experimental observation — an application representing a technology domain of intense current interest, and one that ­­­exemplifies an important class of scientific problems involving material interfaces in extreme environments.

“A big challenge here is in being able to predict what reactions will occur and what new molecules will form under these conditions. While quantum mechanical modeling will enable us to predict these events, we also need to be able to address the times and length scales of these processes,” says Kulik, who is also a faculty member of CCSE. “Our efforts will be focused on developing the needed software and machine learning tools that tell us when more affordable physical models can address the length scale challenge and when we need quantum mechanics to address the accuracy challenge.”

CESMIX researchers plan on disseminating their results via multiple open-source software projects, engaging their developer and user communities. The project will also support the work of postdocs, graduate students, and research scientists at MIT with the overarching goal of creating new paradigms of practice for the next generation of computational scientists.

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