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.
How societal computing links data, infrastructure, and stakeholders to drive innovation and real-world impact in the AI era. Her talk highlights AI-enabled scientific workflows and digital twins from the WIFIRE Program for wildfire management and the NSF-funded National Data Platform, which expands access to AI-ready data and computing to foster collaboration, reproducibility, and innovation across scientific domains.
Artificial intelligence has the potential to bring much-needed acceleration to the development of chemicals and materials for energy and sustainability, just as it has delivered intelligence gains in other fields...
Artificial Intelligence and Machine Learning (AI/ML), especially deep learning, are becoming increasingly popular across scientific fields, with many believing they will have transformational impacts. But important questions remain...
Stay tuned for more information about this seminar. Speaker Bio: Amir Barati Farimani received his Ph.D. in 2015 in mechanical science and engineering from the University of Illinois at Urbana-Champaign. His Ph.D. thesis was titled “Detecting and Sensing Biological...
Developing fast and efficient methods for simulating our observable Universe is a key challenge in maximizing information extraction from cosmological datasets...
Stay tuned for more information about this seminar. Speaker Bio: Wahid Bhimji leads NERSC’s Data and AI Services Group. His interests include machine learning and data management. Recently, he has led several projects that apply AI to science, including deep learning at scale, generative models...
Stay tuned for more information about this seminar. Speaker Bio: Berend Smit received an MSc in Chemical Engineering and Physics from the Technical University in Delft, and a Ph.D. in Chemistry from Utrecht University. He was a (senior) Research Physicist at Shell Research from 1988-1997...
Stay tuned for more information about this seminar. Speaker Bio: Anubhav Jain leads a research group studying new materials design using a mix of theory, computing, and artificial intelligence. Jain's group develops, evaluates, and applies models for predicting materials properties to applications such as electrocatalysis...
Stay tuned for more information about this seminar. Speaker Bio: Wen Jie Ong is the senior product manager for NVIDIA ALCHEMI. He is an organic and polymer chemist by training, and received his PhD at MIT where he discovered a new class of dynamic covalent...
Machine learning methods for improving molecular simulations Molecular simulations aim to model the spatiotemporal behavior of atomistic systems throughout biology, chemistry, and materials science. Given the computational burden of running such simulations for long timescales, machine learning force fields, and particularly neural network interatomic potentials...
Current and future weak lensing surveys contain significant information about our universe, but their optimal cosmological analysis is challenging, with traditional analyses often resulting in information loss due to reliance on summary statistics like two-point correlation functions. While deep learning methods offer promise in capturing...
The analysis of particle collisions at the Large Hadron Collider at CERN helps us to understand the fundamental building blocks of our universe. After years of data taking, the extraction of fundamental insights often requires intricate data analyses. Due to the large volume and complex...
Some of the most powerful techniques developed in ML are rooted in physics, such as MCMC, Belief Propagation, and Diffusion based Generative AI. We have recently witnessed that the flow of information has also reversed, with new tools developed in the ML community impacting physics...
Atomic systems (molecules, crystals, proteins, etc.) are naturally represented by a set of coordinates in 3D space labeled by atom type. This poses a challenge for machine learning due to the sensitivity of coordinates to 3D rotations, translations, and inversions (the symmetries of 3D Euclidean...
Density functional theory (DFT) lies at the heart of all practical applications of theoretical chemistry. However, it is well-known that DFT often provides unsatisfactory descriptions of many important systems: non-equilibrium geometries, such as transition states, and strongly correlated systems, such as transition metals and excited...
Please join us for an interdisciplinary discussion of scientific themes addressed in this series. We'll have lightning talks by Saumil and Théo (BIDMaP Fellows) to start, and then an informal meeting in the seminar space with tables for small or large group discussions.
Propylene is an important building block for the manufacturing of various chemicals and plastic products. The ever-increasing propylene demand is hardly met by traditional oil-based cracking processes, known for their high energy consumption and substantial CO 2 emissions. Leveraging the abundance of light alkanes from...
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...
