Seminars & Events

A central goal of computational chemistry is to predict material properties using first-principles methods based on the fundamental laws of quantum mechanics. However, the high computational costs of these methods typically prevent rigorous predictions of macroscopic quantities at finite temperatures. In this talk, I will...

Alessandra Lanzara is a prominent physicist who has contributed significantly to the field of materials science. After obtaining her PhD from Universita’ di Roma La Sapienza in 1999, she joined the faculty of the physics department at UC Berkeley as an assistant professor. She has...

Fernando Pérez's research focuses on creating tools for modern computational research and data science across domain disciplines, with an emphasis on high-level languages, interactive and literate computing, and reproducible research. Through tools like IPython and Project Jupyter, he builds foundational blocks that enable scientists to tackle all stages of computational research (from exploration through publication) with a coherent approach, thus improving scientific productivity, collaboration and reproducibility.

Molecular dynamics (MD) simulations offer valuable insights into the atomistic-level behavior of molecular systems. However, large-scale Molecular Dynamics simulations face some limitations. First, the high-dimensional nature of large simulation trajectories can make them difficult to interpret. Second, the accessible sampled timescale is often shorter than...

Electrocatalysis lies at the interface between chemistry and physics and represents one of the most promising approaches for enabling renewable energy technologies to mitigate carbon emissions through the use of hydrogen fuel cells and the electrochemical reduction of CO2. One of the key challenges is...

The number of chemically unique arrangements of 15 annulated benzene rings into a planar two-dimensional molecular hydrocarbon exceeds 74,000,000. How does one pick the right target structure? We herein present the development of a complete materials genome library that predicts key fundamental physical properties for...

Recently, artificial intelligence (AI) for drug discovery has raised increasing interest in both the machine learning (ML) and computational chemistry/biology communities. The core task of AI for drug discovery is molecule representation learning, where the molecule knowledge can be naturally presented in different modalities: chemical...

Kristin Persson is a professor in the Department of Materials Science and Engineering at UC Berkeley and runs a lab at the Lawrence Berkeley National Laboratory. The Persson group studies the physics and chemistry of materials using atomistic computational methods and high-performance computing technology, particularly...

Adaptive Experimental Design for Multi-Objective Optimization: An Output Space Entropy Search Framework. Syrine Belakaria is a PhD candidate in Computer Science at Washington State University advised by Prof. Jana Doppa. Prior to her PhD, she received a Master's degree in Electrical Engineering from the University...

Throughout the history of machine learning, we have seen more and more automation of tasks, such as using neural networks to replace the tedious process of manual feature design. One of the next major steps is to automatically design and tune neural networks themselves: neural...