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.
Foundation models like GPT-4 have dramatically altered the modern work landscape for many industries reliant on language tasks, but no equivalent model exists yet for scientific applications. Incorporating foundation models into research workflows could enable unprecedented discoveries. However, mainstream foundation models trained on human-scale datasets...
Join us for a symposium exploring the transformative potential of AI to advance climate tech, hosted by Bakar Climate Labs and Bakar Institute of Digital Materials for the Planet. Objective: This symposium will highlight the appropriate use of AI to enable new climate technologies, showcasing...
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: Zachary W. Ulissi joined Meta’s Fundamental AI Research lab in 2023 to work on AI for chemistry and climate applications and is based in the San Francisco Bay Area. He is particularly excited about how AI and machine learning methods can enhance various quantum...
Biography: Dr. Ching-Yao Lai and her group attack fundamental questions in ice-dynamics, geophysics, and fluid dynamics by integrating mathematical and machine-learned models with observational data. They use their findings to address challenges facing the world, such as advancing our scientific knowledge of ice dynamics under...
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...
The future of chemistry is self-driving In this talk, I will overview the growing field of self-driving laboratories (SDLs). SDLs are systems that help accelerate the process of scientific discovery or scale-up by employing artificial intelligence and automation for experiment planning and execution. Several SDLs...
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...
