Cost-efficient climate change

The Bakar Institute of Digital Materials for the Planet (BIDMaP) aims to speed up the development of reticular chemistry and modular structures for achieving cost-efficient, easily deployable ultra-porous metal-organic frameworks (MOFs) and covalent organic frameworks (COFs).

These programs will help limit and address the impacts of climate change and extend to downstream technologies like conversion of CO2 to clean fuels, biodegradable polymers, enzymes, and pharmaceuticals. BIDMaP brings together top computation and machine learning experts with chemistry and other physical science researchers to exploit the vast potential these reticular structures have in achieving clean air, clean energy, and clean water, see here for the official BIDMaP announcement.

A new frontier

MOFs are crystalline structures in which a combination of multi-metal units and organic linkers are stitched together by strong bonds to make frameworks encompassing ultra-high surface areas (up to 7,000 square meters per gram of MOF material), folded and compacted into tiny spaces.

Each of the more than 100,000 frameworks in existence can selectively attract, filter, store or release specific molecules like carbon dioxide and water, operating in different environments and with high precision.

COFs are yet another class of ultra-porous crystals made entirely from strongly bonded organic molecules with no metals; their versatility offers another frontier in applications for electronics and climate-related catalytic conversions of carbon dioxide.




Bridging Sustainable Tech and Climate Action

When: October 5th, 2023 | 1:30PM – 5:00PM
Location: Tan Hall 775

Event Flyer

EarthTech2023 is a Symposium organized by the College of Computing, Data
Science, and Society in partnership with the Bakar Institute of
Digital Materials for the Planet at UC Berkeley, with...

Picture of the Campanile at UC Berkeley

BIDMaP Faculty Recruitment

The University of California at Berkeley is recruiting two tenured or tenure-track professors with an expected start date of July 1, 2024; one in ML/AI for chemistry, materials science, and chemical biology, and one in ML/AI methods for science including software platforms for their implementation and dissemination. The first position will be jointly...

ChatGPT + Chemist

BIDMaP members utilize ChatGPT to drastically speed up chemical discovery

In a new paper published in ACS Editors' Choice, ChatGPT was trained by BIDMaP faculty and students via precise prompt engineering to efficiently text mine the academic literature on metal-organic frameworks (MOFS). The resulting "ChatGPT Chemistry Assistant" successfully produced highly accurate synthesis condition predictions for over 800 MOFs. Congratulations to Zhileng...

Seminars & Events

omar yaghi

May 3, 2024 - Omar Yaghi

Omar Yaghi is the James and Neeltje Tretter Chair Professor of Chemistry at the University of California, Berkeley, the Founding Director of the Berkeley Global Science Institute, and an elected member of the US National Academy of Sciences as well as the German National Academy...
Tess Smidt

Feb. 1, 2023 - Tess Smidt

Tess Smidt is an Assistant Professor of Electrical Engineering and Computer Science at MIT. Tess earned her SB in Physics from MIT in 2012 and her PhD in Physics from the University of California, Berkeley in 2018. Her research focuses on machine learning that incorporates...
Abby Doyle

Nov. 2, 2023 - Abby Doyle: Enabling chemical synthesis with machine learning

Abby Doyle is a professor of chemical and biomolecular engineering at the University of California, Los Angeles (UCLA). Her research is focused on tackling unresolved issues in the field of organic synthesis by creating innovative catalysts, catalytic reactions, and synthetic techniques. Recently, the Doyle group...
Jascha Sohl-Dickstein

Oct. 12, 2023 - Jascha Sohl-Dickstein

Jascha Sohl-Dickstein is a principal scientist in Google DeepMind. He is most (in)famous for inventing diffusion models. His recent work has focused on theory of overparameterized neural networks, meta-training of learned optimizers, and understanding the capabilities of large language models. Before working at Google, Jascha...