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.
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...
Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry/biology communities. The core task of AI for drug discovery is molecule representation learning, where the molecule knowledge can be naturally presented in different modalities: chemical...
