Fang Liu

Assistant Professor

Emory University

United States

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About

Fang Liu is an Assistant Professor at Emory University in the United States. His research primarily focuses on the integration of machine learning in quantum chemistry, the study of excited state properties, and educational advancements in coding for chemistry. Recent articles authored by Professor Liu explore topics such as thermodynamic phase transitions, molecular electronic structures, and the application of graph neural networks in explaining activity cliffs.

Articles (19)

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2025

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2021

Collaborators (13)

Devin Matthews

Assistant Professor

Southern Methodist University

UNITED STATES

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Vivek Dixit

Clemson University

UNITED STATES

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Michael Taylor

University of Pittsburgh

UNITED STATES

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Sihong Wang

Assistant Professor

University of Chicago

UNITED STATES

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Fangning Ren

Emory University

UNITED STATES

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Theresa Windus

Iowa State University

UNITED STATES

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Asem Alenaizan

SAUDI ARABIA

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Jinjia XU

Assistant Professor

University of Missouri

UNITED STATES

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Rollin King

Professor

UNITED STATES

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Haowei Xu

Massachusetts Institute of Technology

UNITED STATES

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Yao Wang

Assistant Professor

Clemson University

UNITED STATES

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Ana Maria Porras

Assistant Professor

University of Florida

UNITED STATES

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YU He

University of California, Berkeley

UNITED STATES

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Fang Liu

Research Interests

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About

Articles (19)

Streamlining Coding Assignments and Grading on the Cloud: A Preconfigured JupyterHub Image for Chemistry Education

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Stimulus-Driven Tuning of Multipathway Emission in 9-Fluorenone Derivatives: Elucidating Charge Transfer Dynamics in Higher Excited Singlet and Triplet States

Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model

Author Correction: Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning

ACES-GNN: can graph neural network learn to explain activity cliffs?

Chatbot-assisted quantum chemistry for explicitly solvated molecules

Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning

∆-Machine Learning for Quantum Chemistry Prediction of Solution-phase Molecular Properties at the Ground and Excited States

Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states

Data-driven insights into the fluorescence of asphaltene aggregates using extended Frenkel exciton model

Impacts of polarizable continuum models on the SCF convergence and DFT delocalization error of large molecules

AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules

Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models

35 challenges in materials science being tackled by PIs under 35(ish) in 2021

Quantum chemistry common driver and databases (qcdb) and quantum chemistry engine (qce ngine): Automation and interoperability among computational chemistry programs

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Quantum chemistry for molecules at extreme pressure on graphical processing units: Implementation of extreme-pressure polarizable continuum model

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Collaborators (13)

Devin Matthews

Vivek Dixit

Michael Taylor

Sihong Wang

Fangning Ren

Theresa Windus

Asem Alenaizan

Jinjia XU

Rollin King

Haowei Xu

Yao Wang

Ana Maria Porras

YU He

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