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.
Join us to learn more about OMol25, a collection of more than 100 million 3D molecular snapshots whose properties have been calculated with density functional theory, and UMA, models trained on half a billion unique 3D atomic structures (the largest training runs to date).
Stay tuned for more information about this seminar. Speaker Bio: Rana X. Adhikari is an American experimental physicist. He is a professor of physics at the California Institute of Technology (Caltech) and an associate faculty member of the International Centre for Theoretical Sciences of Tata...
Stay tuned for more information about this seminar. Speaker Bio: Following a first degree in Astrophysics and an MSc in Information Processing and Neural Networks, he completed a PhD at Imperial College on geophysical data mining and inference. After that, he was awarded a Junior...
Stay tuned for more information about this seminar. Speaker Bio: Dr. İlkay Altıntaş, a research scientist at the University of California San Diego, is the Chief Data Science Officer of the San Diego Supercomputer Center as well as a Founding Fellow of the Halıcıoğlu Data...
Stay tuned for more information about this seminar. Speaker Bio: Professor Gómez-Bombarelli received his BS, MS, and PhD in chemistry from the University of Salamanca in Spain, followed by postdoctoral work at Heriot-Watt University in Scotland. As a postdoc at the Aspuru-Guzik lab at Harvard...
Featuring Josh Speagle: 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 Molecules using Nanopores.” He extensively used...
Stay tuned for more information about this seminar. Speaker Bio: I am an associate research scientist in the Department of Physics and Astronomy at Johns Hopkins University. Previously, I was a postdoctoral researcher at Columbia University and obtained my PhD from the University of Pennsylvania...
Stay tuned for more information about this seminar. Speaker Bio: Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling...
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...
Deep learning techniques are increasingly applied to scientific problems where the precision of networks is crucial. Despite being deemed as universal function approximators, neural networks, in practice, struggle to reduce prediction errors below a certain threshold even with large network sizes and extended training iterations...
Understanding how inorganic materials and minerals behave, evolve, and change in various environmental conditions is a core challenge in geophysical sciences, and an area where ab-initio simulation methods have long been helpful. The same processes can also be used to find and discover materials for...
We consider the following pervasive scenario in contemporary scientific discovery: we are faced with an enormous space of candidate designs for some task we must explore. However, our ability to explore this space is severely limited due to the significant inherent costs of experimentation and/or...
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...
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...
The field of computational chemistry increasingly relies on automated data-driven pipelines designed to facilitate and accelerate the discovery of molecules and materials with tailored properties (i.e., inverse design pipelines). These efforts require extensive infrastructure, integrating quantum chemistry, statistical models, data curation and software to enable...
Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical knowledge that we already have about many problems in the field, as well as...
Chemical and materials informatics leverages data to correlate a compound’s structure with its properties, enabling the exploration of vast chemical spaces through modeling. Although this approach has the potential to reveal novel materials with desired characteristics, it frequently generates designs that cannot be synthesized, leading...
Frank Noé will explore how AI-powered computational methods are transforming biomolecular research, emphasizing the role of AI in decoding complex biological functions. He will discuss how sophisticated machine learning techniques enhance the analysis of biomolecular dynamics, such as protein folding, offering unprecedented insights into molecular...
After reviewing and updating the theory of energy-conserving Hamiltonian dynamics for optimization and sampling, I'll explain a new application of Energy Conserving Descent (ECD) optimization to precision scientific data analysis in which NN initialization variance has been a bottleneck. Specifically, we choose a particular ECD...
The optimization of catalytic reactions for organic synthesis can be challenging as the interplay between the catalyst structure, reaction conditions, and substrates involved is a complex multidimensional problem. In other words, it is difficult to ascertain the pattern within the noise to offer a complete...
Dark matter is one of the greatest enduring mysteries of fundamental physics. Despite countless direct and indirect searches for dark matter, still, the only evidence we have for it is through its gravitational effects on astrophysical and cosmological scales. In this talk, I will describe...
Transformer-based large language models are making significant strides in various fields, such as natural language processing, biology, chemistry, and computer programming. Here, we show the development and capabilities of Coscientist, an artificial intelligence system that autonomously designs, plans, and performs complex experiments by incorporating large...
The size of chemical space is vast. This makes the application of the first principles of quantum mechanical and advanced statistical mechanics sampling methods to identify binding motifs, conformational equilibria, and reaction pathways extremely challenging, even when considering better physical models, algorithms, or future exascale...
Polycyclic aromatic systems (PASs) present a seemingly insurmountable challenge: vast chemical spaces, complex electronic structures, and elusive aromatic properties. Our mission, should we choose to accept it, is to harness the power of deep learning to decode these molecular mysteries. In this talk, we embark...
