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.
Gradient-based Markov Chain Monte Carlo (MCMC) methods are an essential tool for high dimensional sampling, used in a number of applications, from Chemistry and Materials Science to Statistical Physics, Solid State Physics and Lattice QCD, as well as in Bayesian Inference...
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...
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...
Computational materials science has seen tremendous progress since the early days of density functional theory. Stable algorithms enabled high-throughput computing, which in turn enabled machine-learned potentials (MLPs) that...
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...
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...
The design of new materials has always been a slow process taking decades from problem formulation to initial discovery to commercialization. Today, advances in high-throughput...
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...
Machine-learning techniques are often applied to perform "end-to-end" predictions, making black-box estimates of a property of interest using only a coarse description of...
Developing fast and efficient methods for simulating our observable Universe is a key challenge in maximizing information extraction from cosmological datasets. Current simulations are too slow to scale...
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...
