BIDMaP is happy to be jointly hosting this year's seminar series with colleagues in computational physical sciences, including collaborators from the departments of Physics and Astronomy, and the Lawrence Berkeley National Laboratory.
Optimal Transport: A Unifying Language for Physics and Machine Learning Optimal transport (OT) theory, first conceived to solve problems of moving dirt, has since evolved into a powerful mathematical framework with...
Machine learning in transition metal chemistry has historically lagged behind other areas of chemistry due to the combination of diverse chemical bonding and limitations in high-quality...
There will be tremendous advances in AI for Chemistry and Materials Science in the coming years. However, many AI models and simulations are currently not running efficiently on GPUs, which will continue to hinder...
Stay tuned for more information about this seminar. Speaker Bio: Veronique Van Speybroeck is full professor at the Ghent University within the Faculty of Engineering and Architecture, since October 2012. She also holds a position as Research Professor at the Ghent University since October 2007...
Stay tuned for more information about this seminar. Speaker Bio: John Kitchin completed his B.S. in Chemistry at North Carolina State University. He completed a M.S. in Materials Science and a PhD in Chemical Engineering at the University of Delaware in 2004 under the advisement...
Stay tuned for more information about this seminar. Speaker Bio: Rocio Semino is a researcher at the Institut Charles Gerhardt Montpellier. Last December, she was awarded a 1.35 million European Research Council (ERC) grant for her work on metallo-organic networks...
Stay tuned for more information about this seminar. Speaker Bio: Blakesley Burkhart is an associate professor with tenure at Rutgers in the Physics and Astronomy Department. She is also an associate research scientist at the Center for Computational Astrophysics in the Simons Foundation Flatiron Institute...
Stay tuned for more information about this seminar. Speaker Bio: Phiala Shanahan grew up in Adelaide, Australia, and obtained her BSc from the University of Adelaide in 2012 and her PhD, also from the University of Adelaide, in 2015. Before joining the MIT physics faculty in July 2018, Prof. Shanahan...
Anubhav Jain explores how high-throughput computation, automated experimentation, literature mining, and AI are reshaping the materials design process. His talk examines...
Berend Smit, a professor at EPFL and Foreign Member of the Royal Netherlands Academy of Arts and Sciences, is known for his pioneering work in molecular simulation techniques for energy applications. His career spans...
Artificial Intelligence and Machine Learning are transforming scientific discovery. Wahid Bhimji, who leads NERSC’s Data and AI Services Group, will discuss applying deep learning and generative models at scale to...
Shivam Pandey develops machine learning models that transform simple dark matter simulations into detailed galaxy maps with far greater speed and scale. His work enables...
Artificial Intelligence and Machine Learning (AI/ML), especially deep learning, are becoming increasingly popular across scientific fields, with many believing they will have transformational impacts...
Artificial intelligence has the potential to bring much-needed acceleration to the development of chemicals and materials for energy and sustainability, just as it has delivered intelligence gains...
How societal computing links data, infrastructure, and stakeholders to drive innovation and real-world impact in the AI era. Her talk highlights AI-enabled scientific workflows and digital twins from the WIFIRE Program for...
Today’s most sensitive physics experiments require optimization of experimental design, nonlinear feedback controls, and materials design, among other challenges. In this talk, Professor Rana Adhikari will describe recent work...
Deep learning techniques are increasingly applied to scientific problems where the precision of networks is crucial. Despite being deemed as universal function approximators, neural networks, in practice, struggle to reduce prediction errors below a certain threshold even with large network sizes and extended training iterations...
Understanding how inorganic materials and minerals behave, evolve, and change in various environmental conditions is a core challenge in geophysical sciences, and an area where ab-initio simulation methods have long been helpful. The same processes can also be used to find and discover materials for...
We consider the following pervasive scenario in contemporary scientific discovery: we are faced with an enormous space of candidate designs for some task we must explore. However, our ability to explore this space is severely limited due to the significant inherent costs of experimentation and/or simulation. Thus it is...
