(6ci) Catalyst Design through Simulations and Machine Learning

A design of efficient catalysts comprised of earth-abundant materials is crucial for the development of new devices for the sustainable energy future. Unfortunately, no catalyst is available that satisfies activity, stability and cost criteria at the same time, sparking an immediate need for innovative catalyst design strategies. My research group will focus on a computational catalyst design: applying quantum-chemistry simulations and machine learning techniques to develop novel catalysts through understanding dynamics at heterogeneous interfaces and performing a high-throughput screening. Two research topics that my group would initially pursue are:

1. Integrated computational framework for an accurate prediction of catalytic properties
2. High-throughput computational screening of catalysts using automated density functional theory (DFT) calculations and machine-learning.

Beyond catalyst developments, I am also interested in applying simulations and machine learning to other energy conversion/storage technologies such as solar cells and secondary batteries.

Teaching Interests:

My teaching philosophy derives from past experiences in my graduate years. In graduate school classes, my interest in research was sparked by professors asking how scientific concepts related to real-world chemistry and encouraging us to tackle current challenges. My curiosity, originating from intriguing classes, became a strong force driving me to look up recent research articles and chat with students from different departments. These fruitful experiences have helped me to research enthusiastically and stay in academia, and as a faculty member I wish to spread similarly inspiring experiences to motivate students and undertake research with them.

With a strong background in core Chemical Engineering subjects and computational experiences, I will motive students to utilize computers to solve Chemical Engineering problems. I will also develop my own courses, particularly focusing on the introduction of atomic simulations in Chemical Engineering and their correlation to experimental observations.

Research Experience

1. Graduate Student (KAIST)

Graduate school of EEWS (Energy, Environment, Water and Sustainability), Korea Advanced Institute of Science and Technology (Sep. 2013 – Aug. 2017)

Supervisor: Yousung Jung

• Studied a selectivity determining factor in CO₂ electrochemical reduction reaction.
• Systematically studied effects of Au nanoparticle/nanowire size on CO₂ reduction reaction.
• Investigated single atom catalysts for applications in CO₂ reduction reaction.

2. Postdoctoral Researcher (Stanford University)

SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University (Sep. 2017 – Aug. 2018)

Supervisor: Jens K. Nørskov

• Studied oxide-metal interfaces for applications in O₂ reduction reaction
• Studied single metal atom catalysts for O₂ reduction reaction
• Collaborated with an experimental group (Prof. Xiaolin Zheng (Stanford Univ.)) to develop ZnO catalyst for H₂O oxidation to H₂O₂.
• Collaborated with an experimental group (Prof. Haotian Wang (Rice Univ.)) to study a local environment effect of single atom catalysts on O₂ reduction reaction.
  
  

3. Postdoctoral Researcher (Carnegie Mellon University)

Department of Chemical Engineering, Carnegie Mellon University (Sep.2018 – present)

Supervisor: Zachary W. Ulissi

• Developed a method to automate density functional theory (DFT) calculations of metal oxides for high-throughput catalyst screening applications.
• Developed a machine learning method to predict binding energies on various catalyst surfaces.

Selected Publications (Total Publications = 30, Total First Author Publications = 15, Total Citations = 795, h-index=14)

1. Back, K. Tran and Z. Ulissi, “Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning”, ACS Catal. (submitted),
2. Back, J. Yoon, N. Tian, W. Zhong, K. Tran, Z. Ulissi, “Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts”, J. Phys. Chem. Lett. (submitted)
3. Back, M. Yeom and Y. Jung, “Active Sites of Au and Ag Nanoparticle Catalysts for CO₂ Electroreduction to CO”, ACS Catal., 5, 9, 5089 (2015)
4. Back, J. Lim, N. Kim, Y. Kim, Y. Jung, “Single-atom Catalysts for CO₂ Electroreduction with Significant Activity and Selectivity Improvements”, Chem. Sci., 8, 2, 1090 (2017)­­

Honors

1. $ 80,000, Global Ph.D. Fellowship, National Research Foundation of Korea (NRF), 2014 – 2017
2. $ 40,000, Postdoctoral Fellowship, National Research Foundation of Korea (NRF), 2017 – 2018