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...
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...
After decades of rather unsuccessful attempts, computers are finally making impact on the practice of synthetic chemistry. This change is made possible by the combination of increased computing power and, above all, new algorithms to encode and manipulate synthetic knowledge at various levels, from sequences...
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled...
Generative models are increasingly used to produce novel scientific data, including crystal structures. In this talk, I will present two methods leveraging generative models for materials discovery. First, I will talk about UniMat, a unified crystal structure representation, which enables scalable generation of high-fidelity crystal...
At least 50,000 papers each year report the results of Kohn-Sham density functional calculations for materials and molecular properties. This is a huge worldwide effort, growing rapidly with computer power and powerful machine-learning algorithms to search for desired properties. But all these calculations are limited...
In this talk, I present a selection of ideas and algorithms that are presented in my recently published textbook of the same title [1]. The book introduces information measurement methodologies for machine learning that reduce the reliance on hyperparameters and model-type biases. This information-driven perspective...
Neutron scattering is a versatile technique for studying the structure and dynamics of materials. Unfortunately, there are a limited number of neutron sources available in the world to perform scientific experiments. In this talk, I will discuss the use of artificial intelligence to more efficiently...
Despite the recent advances in physical simulations and machine learning, the exploration of novel inorganic crystals remains constrained by the expensive trial-and-error approaches. Recent developments in deep learning have shown that models can attain emergent predictive capabilities with increasing data and computation, in fields such...
