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.
Biography: Professor Ho joined the Physics Department as a Research Professor and as an Affiliated Faculty at Center for Data Science at NYU in 2021. Ho joined Simons Foundation in 2018 as leader of the Cosmology X Data Science group at CCA and in 2021...
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 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...
Finding particle tracks is a central component of searching for new phenomena, but is very a challenging combinatorial problem. Traditionally, track finding codes assume that tracks must be helical, which simplifies the task but also restricts power to discover new physics which might produce non-helical...
At-scale Human - AI Teams In this talk, Dr. Franziska Bell will share the evolution of enterprise-scale human - AI teams, alongside corresponding examples and share her vision of the next generation of agentic-based AI. Biography Dr. Franziska Bell holds a PhD in theoretical chemistry...
Artificial intelligence (AI) and robotics have emerged as transformative tools to accelerate materials research, however, challenges remain in realizing the full potential of computational designs in laboratory settings. With the rise of self-driving laboratories powered by automated experiments and AI-driven guidance, a paradigm shift in...
Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using...
The past decade was marked by an exponential increase in the availability of experimental data in high energy physics, leading to unprecedented precision in the description of particle interactions. However, indirect evidence for new physics processes, such as the existence of dark matter, motivates the...
