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.
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 accelerate science on supercomputers.
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 roles at Shell, the University of Amsterdam, UC Berkeley, and Lawrence Berkeley National Laboratory, and he co-authored key textbooks on molecular simulations and carbon capture.
Anubhav Jain explores how high-throughput computation, automated experimentation, literature mining, and AI are reshaping the materials design process. His talk examines both the progress and challenges in using theory, data, and automation to accelerate discovery highlighting how machine learning, LLM-based intuition, and AI “co-scientists” may transform how we design, synthesize, and understand new materials.
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...
The number of chemically unique arrangements of 15 annulated benzene rings into a planar two-dimensional molecular hydrocarbon exceeds 74,000,000. How does one pick the right target structure? We herein present the development of a complete materials genome library that predicts key fundamental physical properties for...
