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.
In this talk, Dr. Franziska Bell will share the evolution of enterprise-scale human - AI teams, alongside corresponding examples and share her vision of the next generation of agentic-based AI.
Biography: Professor Whiteson’s research is in the field of Experimental High Energy Physics. He is interested in probing the structure of matter and the nature of its interactions at the very smallest scales, to understand the fundamental nature of our universe. Whiteson is part of...
Biography: Dr. Renana Gershoni Poranne is an Assistant Professor of Computational Chemistry at the Schulich Faculty of Chemistry at the Technion-Israel Institute of Technology, where she is a Branco Weiss Fellow , Horev Fellow, and Alon Scholarship recipient. Her appointment began in October 2021. Before...
Biography: Prof. Head-Gordon received her BS in Chemistry from Case Western Reserve University in 1983 and her Ph.D. in Theoretical Chemistry from Carnegie Mellon University in 1989. She was a postdoctoral member of technical staff at AT&T Bell Laboratories from 1990-1992. She currently is Chancellor’s...
Biography: Dr. Gomes joined Carnegie Mellon University in 2022 in the Departments of Chemistry and Chemical Engineering. The Gomes Group research program focuses on the development of new chemical reactions, catalysts, and materials using and developing state-of-the-art machine learning and automated synthesis. Gomes’ research rests...
Biography: Professor Shih is in the Department of Physics & Astronomy at Rutgers University and a member of the New High Energy Theory Center (NHETC). His primary research focus is theoretical particle physics: understanding the nature of the Universe at the most fundamental level. Currently...
Biography: Professor Sigman was born in Los Angeles, California in 1970. He received a B.S. in chemistry from Sonoma State University in 1992 before obtaining his Ph.D. at Washington State University with Professor Bruce Eaton in 1996 in organometallic chemistry. He then moved to Harvard...
Biography: Professor Shanahan’s research interests are focussed around theoretical nuclear and particle physics. In particular, she works to understand the structure and interactions of hadrons and nuclei from the fundamental (quark and gluon) degrees of freedom encoded in the Standard Model of particle physics. Shanahan’s...
Biography: Professor Yousung Jung teaches Chemical and Biomolecular Engineering at KAIST. His research background and interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems, and their applications towards the understanding of molecules...
Biography: Professor Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider (LHC) through new theoretical frameworks...
Biography: Professor Corminboeuf researches and focuses on electronic structure theory in the area of method development and conceptual work applied to the field of homogeneous catalysis and organic electronics. Her group has contributed to the establishment of quantum chemical approaches and is involved in injecting...
Biography: Professor Ho joined the Physics Department as a Research Professor and as an Affiliated Faculty at Center for Data Science at NYU in 2021. Ho joined Simons Foundation in 2018 as leader of the Cosmology X Data Science group at CCA and in 2021...
Biography: Dr. Chan is a scientist at the Center for Nanoscale Materials at Argonne National Laboratory who studies nanomaterials and renewable energy materials, including solar cells, batteries, thermoelectrics, and catalysts. Her particular focus is on using artificial intelligence/machine learning ( AI/ ML) for efficient materials...
Biography: Professor Garnett's primary research interest is Bayesian active learning, with a focus on applications in the natural sciences and engineering. A major theme in his research is automating scientific discovery, broadly interpreted to include both theory and practice and both policy design and modeling...
Biography: Klaus-Robert Müller has been a professor of computer science at Technische Universität Berlin since 2006; at the same time he is directing rsp. co-directing the Berlin Machine Learning Center and the Berlin Big Data Center and most recently BIFOLD . He studied physics in...
The past decade was marked by an exponential increase in the availability of experimental data in high energy physics, leading to unprecedented precision in the description of particle interactions. However, indirect evidence for new physics processes, such as the existence of dark matter, motivates the...
After decades of rather unsuccessful attempts, computers are finally making impact on the practice of synthetic chemistry. This change is made possible by the combination of increased computing power and, above all, new algorithms to encode and manipulate synthetic knowledge at various levels, from sequences...
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled...
Generative models are increasingly used to produce novel scientific data, including crystal structures. In this talk, I will present two methods leveraging generative models for materials discovery. First, I will talk about UniMat, a unified crystal structure representation, which enables scalable generation of high-fidelity crystal...
At least 50,000 papers each year report the results of Kohn-Sham density functional calculations for materials and molecular properties. This is a huge worldwide effort, growing rapidly with computer power and powerful machine-learning algorithms to search for desired properties. But all these calculations are limited...
In this talk, I present a selection of ideas and algorithms that are presented in my recently published textbook of the same title [1]. The book introduces information measurement methodologies for machine learning that reduce the reliance on hyperparameters and model-type biases. This information-driven perspective...
Neutron scattering is a versatile technique for studying the structure and dynamics of materials. Unfortunately, there are a limited number of neutron sources available in the world to perform scientific experiments. In this talk, I will discuss the use of artificial intelligence to more efficiently...
Despite the recent advances in physical simulations and machine learning, the exploration of novel inorganic crystals remains constrained by the expensive trial-and-error approaches. Recent developments in deep learning have shown that models can attain emergent predictive capabilities with increasing data and computation, in fields such...
We are excited to host the 1st BIDMaP Hackathon with the Fair Universe Higgs Uncertainty Challenge. The challenge uses the example of the rate at which Higgs bosons are produced in the ATLAS detector at the LHC, and poses the question of how to infer...
The efficient construction of hypothetical metal-organic framework (MOF) structures is essential for advancing MOF-related research under the data-driven paradigm. This seminar will introduce a new computational program that is capable of constructing new MOF structures rapidly based on existing MOF structures and organic ligand libraries...
The rapid advancement of artificial intelligence is transforming materials modeling, simulation, and design. This report explores breakthroughs in AI-assisted materials design, emphasizing the transition from multi-scale modeling to multi-scale pre-training. These pre-trained models integrate literature, simulation, and experimental data in a novel manner, paving the...
The recent shale gas boom in the US has made catalytic ethane dehydrogenation (EDH) an economically viable route to produce ethylene, a precursor for the synthesis of several commodity chemicals. Supported atomically dispersed metals and sub-nanometer clusters are an emerging class of catalysts that have...
The intersection of synthetic chemistry, condensed matter physics, electronic devices, and artificial intelligence (AI) holds the potential for paradigm-shifting scientific breakthroughs. Innovations in these interdisciplinary interfaces can lead to the development of novel quantum materials, advanced electronic circuits, and new methodologies for material discovery. In...
Registration is required for this Friday, May 3 seminar in Banatao Auditorium in Sutardja Dai Hall. This presentation will be about how the precision of manipulating molecules has led to several large classes of porous materials capable of carbon capture and water harvesting from desert...
Statistical inference is a crucial step in gleaning insights from experimental / observational data to build better physics models to describe our universe. Whether it is to unravel the mysteries of what is happening in the interior of neutron stars, or to uncover the secrets...
