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...
The pursuit of carbon neutrality has become a global imperative in the face of climate change, driving the transition to renewable energy sources and the widespread adoption of electric vehicles. Designing new cathode materials for energy storage is one promising avenue. Modern battery materials such...
Perovskite is one of the most promising photovoltaic materials for the future. While low stability has long been the bottleneck issue limiting their commercialization. In the past, by using enhanced sampling coupled with machine learning (ML) potential model, I have unraveled the degradation mechanism of...
The advent of advanced large language models like ChatGPT marks a transformative era in scientific research, particularly in the field of reticular chemistry. This seminar focuses on how ChatGPT's natural language processing capabilities enable scientists to accelerate and innovate in their research endeavors. We will...
Seismic networks have consistently improved across extensive temporal and spatial scales, enhancing our capability to record subtle shaking signals of the Earth. The vast seismic archive poses a challenge for efficient data analysis, but also presents an opportunity to uncover many hidden signals from small...
Abby Doyle is a professor of chemical and biomolecular engineering at the University of California, Los Angeles (UCLA). Her research is focused on tackling unresolved issues in the field of organic synthesis by creating innovative catalysts, catalytic reactions, and synthetic techniques. Recently, the Doyle group...
Bio Alán Aspuru-Guzik is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute. He is a CIFAR Lebovic Fellow in the...
In silico materials design often involves the exploration of vast, diverse chemical spaces. While ab initio methods have been phenomenally successful in materials simulations, their scope of application has always been constrained by their high cost and poor scaling. In this talk, I will highlight...
The success of deep learning has hinged on learned functions dramatically outperforming hand-designed functions for many tasks. However, we still train models using hand designed optimizers acting on hand designed loss functions. I will argue that these hand designed components are typically mismatched to the...
Please join for this EECS Colloquium (not part of the regular BIDMaP series; please note different day/time/location). Many thanks to EECS for welcoming the BIDMaP community to this exciting talk. See more on the EECS website, including a remote participation option.
I will describe a research program aimed at advancing the potential for discovery and interdisciplinary collaboration by approaching fundamental physics challenges through the lens of modern machine learning (ML). This research program has two complementary components. Ab initio simulations are a powerful tool of fundamental...
A central goal of computational chemistry is to predict material properties using first-principles methods based on the fundamental laws of quantum mechanics. However, the high computational costs of these methods typically prevent rigorous predictions of macroscopic quantities at finite temperatures. In this talk, I will...
Alessandra Lanzara is a prominent physicist who has contributed significantly to the field of materials science. After obtaining her PhD from Universita’ di Roma La Sapienza in 1999, she joined the faculty of the physics department at UC Berkeley as an assistant professor. She has...
Fernando Pérez's research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. Through tools like IPython and Project Jupyter, he builds foundational blocks that enable scientists to tackle all stages of computational research (from exploration through publication) with a coherent approach, thus improving scientific productivity, collaboration and reproducibility.
Molecular dynamics (MD) simulations offer valuable insights into the atomistic-level behavior of molecular systems. However, large-scale Molecular Dynamics simulations face some limitations. First, the high-dimensional nature of large simulation trajectories can make them difficult to interpret. Second, the accessible sampled timescale is often shorter than...
Electrocatalysis lies at the interface between chemistry and physics and represents one of the most promising approaches for enabling renewable energy technologies to mitigate carbon emissions through the use of hydrogen fuel cells and the electrochemical reduction of CO2. One of the key challenges is...
