Presentation given at National Alliance for Water Innovation (NAWI) webinar, August 2020

1. Machine Learning Platform for Catalyst
Design
1
Zachary Ulissi, CMU Wei Tong, LBNL
Anubhav Jain, LBNL
with participation from:

2. Project overview
2
• Our challenge: design and screen
new materials for water purification
faster than ever before
• Our approach:
• Materials theory
• High performance computing
• Automated experiments
• Our 1-year outcome: demonstrate
commercially viable catalysts for
oxyanion reduction

3. Background
• New advancements in
materials theory allow us to
perform computer-aided-
design of materials, at the
level of atoms and electrons
• Prior work strongly suggests
that oxyanion reduction (in
particular NO3
-) on metal
surfaces can be predicted
from computer models
3
Jain, A., Shin, Y. & Persson, K. A. Computational predictions of energy materials
using density functional theory. Nature Reviews Materials 1, 15004 (2016).
Liu, J.-X., Richards, D., Singh, N. & Goldsmith, B. R. Activity and Selectivity Trends in
Electrocatalytic Nitrate Reduction on Transition Metals. ACS Catal. 9, 7052–7064 (2019).

4. • By leveraging
supercomputing and
machine learning, we will
virtually screen >1000 alloys
for nitrate reduction potential
• The most promising
candidates will be studied
experimentally at facilities
being developed at LBNL
Incorporating into a screening
framework
4

5. Overall vision and opportunity
• The short-term goal is to demonstrate experimental success of
a new intermetallic alloy for nitrate reduction
• However, the long-term vision is to develop a general, flexible
capability that can discover new materials for many different
scenarios – a materials discovery platform
• Although limitations certainly exist, developing such a platform
could have large long-term value for the industry
5