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.
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...
We are excited to host the 1st BIDMaP Hackathon with the Fair Universe Higgs Uncertainty Challenge. The challenge uses the example of the rate at which Higgs bosons are produced in the ATLAS detector at the LHC, and poses the question of how to infer...
The efficient construction of hypothetical metal-organic framework (MOF) structures is essential for advancing MOF-related research under the data-driven paradigm. This seminar will introduce a new computational program that is capable of constructing new MOF structures rapidly based on existing MOF structures and organic ligand libraries...
The rapid advancement of artificial intelligence is transforming materials modeling, simulation, and design. This report explores breakthroughs in AI-assisted materials design, emphasizing the transition from multi-scale modeling to multi-scale pre-training. These pre-trained models integrate literature, simulation, and experimental data in a novel manner, paving the...
The recent shale gas boom in the US has made catalytic ethane dehydrogenation (EDH) an economically viable route to produce ethylene, a precursor for the synthesis of several commodity chemicals. Supported atomically dispersed metals and sub-nanometer clusters are an emerging class of catalysts that have...
The intersection of synthetic chemistry, condensed matter physics, electronic devices, and artificial intelligence (AI) holds the potential for paradigm-shifting scientific breakthroughs. Innovations in these interdisciplinary interfaces can lead to the development of novel quantum materials, advanced electronic circuits, and new methodologies for material discovery. In...
Registration is required for this Friday, May 3 seminar in Banatao Auditorium in Sutardja Dai Hall. This presentation will be about how the precision of manipulating molecules has led to several large classes of porous materials capable of carbon capture and water harvesting from desert...
Statistical inference is a crucial step in gleaning insights from experimental / observational data to build better physics models to describe our universe. Whether it is to unravel the mysteries of what is happening in the interior of neutron stars, or to uncover the secrets...
The need for new materials to tackle societal challenges in energy and sustainability is widely acknowledged. As demands for performance increase while resource constraints narrow available options, the vastness of composition, structure and process parameter space make the apparently simple questions of where to look...
Composite materials are known for their customizable properties and superior performance characteristics. However, the design of these materials is inherently complex, as it involves navigating through an extensive array of possible material combinations and configurations. In this talk, I will first present novel computational approaches...
Studying low-likelihood high-impact climate events in a warming world requires massive ensembles of hindcasts to capture their statistics. It is currently not feasible to generate these ensembles using traditional weather or climate models, especially at sufficiently high spatial resolution. We describe how to bring the...
Metal-organic frameworks are ultraporous materials that can exhibit selective and cooperative CO2 adsorption chemistry, with potential for future reversible carbon capture applications. While CO2 adsorption enthalpies are relatively well documented, many temperature-dependent and chemical dynamical phenomena related to CO2 and other competing molecular species remain...
