About the Group

Principle Investigator

Fang Liu

Hello! I am Fang Liu and I am excited to begin my faculty career as an Assistant Professor at Emory University. My research focuses on building computational tools to accelerate the design and discovery of functional molecules. I’m seeking creative chemists interested in developing new theoretical methods to join my group. I am a first generation college student and the only science major in my family. It took time for me to develop my interests and I can understand the same in my students. Computational expertise is welcomed, but not required. I didn’t do much undergraduate research in computational chemistry and I had never done any programming for chemistry before I joined my graduate lab! If you are curious about computational chemistry and interested in programming, I encourage you to apply. Graduate students will be the major power in our lab – we will learn from each other.

Education

• 2017-2020 Postdoctoral Scholar, Massachusetts Institute of Technology
• 2011-2017 Ph.D. in Chemistry, Stanford University
• 2007-2011 B.S. in Chemical Physics, University of Science and Technology of China

Core Interests

• quantum chemistry
• solvent effects
• machine learning
• excited states
• biking
• tea

Awards and Honors

• 2020 DCOMP Travel Award, APS March Meeting, American Physics Society
• 2019 MoISSI Investment Fellowship, National Science Foundation
• 2018 MoISSI Seed Fellowship, National Science Foundation
• 2018 NVIDIA GPU Award, American Chemical Society
• 2016 Evelyn Laing McBain Fellowship, Stanford University
• 2011 Guo Moruo Scholarship, University of Science and Technology of China

Postdoctoral Fellow

Eugen Hruska

My interests are at the intersection of ML and biophysics, with experience in developing sampling algorithms and automatic workflows including machine learning. To sample the chemical space efficiently for chemical discovery I’m working on automated workflows. By sampling efficiently, the large quantities of data for machine learning can be reduced, leading to faster and more accurate results. To develop higher accuracy solvent models I combine different machine learning and sampling approaches to understand better the behavior of solvents and their varied interactions. With machine learning the solvent behavior can be easier represented and allows to describe the interactions at a higher accuracy. This improved understanding of solvents allows us to develop new implicit solvent models.

Personal Homepage: euhruska.github.io

Graduate Student

Ariel Gale

I am from Knoxville, TN and graduated from Furman University with a B.S. in Chemistry and a Women’s, Gender, and Sexuality Studies minor in May 2020. During my undergraduate research, I studied the polymerization of glycine in Earth’s prebiotic atmosphere. Through this project modeling molecular clusters, along with others including a drug screening and characterizing metal-to-ligand charge transfers in dyes, I have studied the effects of solvation, conformation, and non-covalent interactions on the chemical and physical properties of important systems. Here at Emory, I am continuing that work by studying redox potentials in order to identify errors in electronic structure theory methods and the application of implicit solvents. When I am not working on my research, I enjoy singing in choir or getting outside and biking or playing disc golf.

Awards: Goldwater Scholarship 2019, Beckman Scholar 2019, WiNS Fellowship (Emory) 2020, Quayle Promising Theorist Award (Emory) 2020

Undergraduate Student

Xiao Huang

Xiao is an undergraduate student in the Department of Chemistry, Emory University. He joined the Liu Group in August 2020.

Undergraduate Student

Anjanay Nangia