*corresponding author, grad. student, postdoc fellow^PD, undergrad. student^U

Preprints

45.* Fangning Ren, Xu Chen , F. Liu,* Size-transferable prediction of excited state properties for molecular assemblies with machine-learned exciton model ChemRxiv Preprint: DOI: 10.26434/chemrxiv-2024-x5ljd (2024)

44.* Xu Chen, Yuanjie Sun, Eugen Hruska,^PD Vivek Dixit, Jinming Yang, Yu He,* Yao Wang,* Fang Liu*, “Explainable Machine Learning Identification of Superconductivity from Single-Particle Spectral Functions”, Preprint: arXiv: 2406.04445 (2024)

43.* Rohit SK Gadde, Sreelaya Devaguptam, Fangning Ren, Rajat Mittal, Lechen Dong, Yao Wang, Fang Liu*, “Chatbot-Assisted Quantum Chemistry for Explicitly Solvated Molecules”, Preprint: ChemRxiv https://doi.org/10.26434/chemrxiv-2024-35f9j (2024)

42.* Sangni Xun,^PD Fang Liu*, “Comparison of Machine-Learning and Classical Force Fields in Simulating the Solvation of Small Organic Molecules in Acetonitrile”, Preprint: ChemRxiv https://doi.org/10.26434/chemrxiv-2023-sd4b2-v2 (2023)

41.* Eugen Hruska,^PD Liang Zhao, Fang Liu*, “Ground truth explanation dataset for chemical property prediction on molecular graphs”, Preprint: ChemRxiv: https://doi.org/10.26434/chemrxiv-2022-96slq-v2. (2022)

2024

40. Hao Tang, Brian Xiao, Wenhao He, Pero Subasic, Avetik R Harutyunyan, Yao Wang, Fang Liu, Haowei Xu, Ju Li, “Approaching coupled-cluster accuracy for molecular electronic structures with multi-task learning”, Nat. Comput. Sci. (2024) accepted

2023

39.* Fangning Ren^, Fang Liu*, “Data-Driven Insights into the Fluorescence of Asphaltene Aggregates Using Extended Frenkel Exciton Model”, Chem. Phys. Rev. 4, 041401 (2023)

38.* Xu Chen, Pinyuan Li, Eugen Hruska^PD, Fang Liu* “Δ-Machine Learning for Quantum Chemistry Prediction of Solution-phase Molecular Properties at the Ground and Excited States”, Phys. Chem. Chem. Phys. (2023), DOI: 10.1039/D3CP00506B

2022

37.* Fangning Ren, Fang Liu*, “Impacts of Polarizable Continuum Models on the SCF Convergence and DFT Delocalization Error of Large Molecules”, J. Chem. Phys. 157, (2022): 184106

36.* Eugen Hruska,^PD Ariel Gale, Xiao Huang,^U Fang Liu*, “AutoSolvate: A Toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules” J. Chem. Phys. 156, (2022): 124801

35.* Eugen Hruska,^PD Fang Liu*. “Machine Learning: An Overview” In Pavlo Dral (Eds.), Quantum Chemistry in the Age of Machine Learning (2022)

34.* Eugen Hruska,^PD Ariel Gale, Fang Liu*, “Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models” J. Chem. Theory Comput. 18, (2022): 1096-1108

2021

33. (non-peer reviewed) Brian Aguado, Laura J. Bray, Sabina Caneva, Juan-Pablo Correa-Baena, Giuliana Di Martino, Chengcheng Fang, Yin Fang, Pascal Gehring, Gabriele Grosso, Xiaodan Gu, Peijun Guo, Yu He, Thomas J. Kempa, Matthew Kutys, Jinxing Li, Tian Li, Bolin Liao, Fang Liu, Francisco Molina-Lopez, Andrea Pickel, Ana M. Porras, Ritu Raman, Ellen M. Sletten, Quinton Smith, Chaoliang Tan, Haotian Wang, Huiliang Wang, Sihong Wang, Zhongrui Wang, Geoffrey Wehmeyer, Lu Wei, Yuan Yang, Lauren D. Zarzar, Meiting Zhao, Yuqing Zheng, Steve Cranford, “35 challenges in materials science being tackled by PIs under 35(ish) in 2021”, Matter, 4, (2021): 3804-3810

32. Daniel Smith, Annabelle Lolinco, Zachary Glick, Jiyoung Lee, Asem Alenaizan, Taylor Barnes, Carlos Borca, Roberto Di Remigio, David Dotson, Sebastian Ehlert, Alexander Heide, Michael Herbst, Jan Hermann, Colton Hicks, Joshua Horton, Adrian Hurtado, Peter Kraus, Holger Kruse, Sebastian Lee, Jonathon Misiewicz, Levi Naden, Farhad Ramezanghorbani, Maximilian Scheurer, Jeffrey Schriber, Andrew Simmonett, Johannes Steinmetzer, Jeffrey Wagner, Logan Ward, Matthew Welborn, Doaa Altarawy, Jamshed Anwar, John Chodera, Andreas Dreuw, Heather Kulik, Fang Liu, Todd Martinez, Devin Matthews, Henry Schaefer, Jiri Šponer, Justin Turney, Lee Ping Wang, Nuwan De Silva, Rollin King, John Stanton, Mark Gordon, Theresa Windus, C. David Sherrill, and Lori Burns. “Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and Interoperability among Computational Chemistry Programs” J. Chem. Phys. 155, (2021): 204801

31. Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik. “Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles” Chem. Sci. 12, (2021): 13021-13036

30. Aditya Nandy, Chenru Duan, Michael Taylor, Fang Liu, Adam Steeves, and Heather Kulik, “Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning” Chem. Rev. 121, (2021): 9927–10000

29.* Ariel Gale, Eugen Hruska,^PD and Fang Liu,* “Quantum Chemistry for Molecules at Extreme Pressure on Graphical Processing Units: Implementation of Extreme Pressure Polarizable Continuum Model” J. Chem. Phys. 154 (2021): 244103

28. Chenru Duan, Fang Liu, Aditya Nandy, and Heather J. Kulik, “Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery”,  J. Phys. Chem. Lett. 12 (2021): 4628–4637

27. Ruibin Liang, Jimmy Yu, Jan Meisner, Fang Liu, and Todd. J. Martínez. “Electrostatic Control of Photoisomerization in Channelrhodopsin 2.” J. Am. Chem. Soc. 143, (2021): 5425-5437

26.* Fang Liu,* Michael Filatov, and Todd J. Martínez. “Analytical Derivatives of the Individual State Energies in Ensemble Density Functional Theory Method: II. Implementation on Graphical Processing Units (GPUs).”  J. Chem. Phys. 154, (2021): 104108 .

25. Jon Paul Janet, Chenru Duan, Aditya Nandy, Fang Liu, and Heather J. Kulik, “Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design,” Acc. Chem. Res. 54, 3, (2021): 532–545

2020

24. Fang Liu, Chenru Duan, and Heather Kulik. “Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening.” J. Phys. Chem. Lett. 11, no. 19 (2020): 8067-8076

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Before Joining Emory in Aug 2020

23. Chenru Duan, Fang Liu, Aditya Nandy, and Heather Kulik. “Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost.” J. Phys. Chem. Lett. no. 11 (2020): 6640–6648

22. Chenru Duan, Fang Liu, Aditya Nandy, and Heather Kulik. “Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics.” J. Chem. Theory Comput. 16, no. 7 (2020): 4373-4387.

21. Fang Liu and Heather Kulik. “Impact of Approximate DFT Density Delocalization Error on Potential Energy Surfaces in Transition Metal Chemistry.” J. Chem. Theory Comput. 6 (2020): 264.

20. Seritan, Stefan, Christoph Bannwarth, Bryan S. Fales, Edward G. Hohenstein, Christine M. Isborn, Sara I. L. Kokkila-Schumacher, Xin Li, Fang Liu, Nathan Luehr, James W. Snyder Jr. et al. “TeraChem: A graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics.” WIREs Computational Molecular Science (2020): e1494.

19. Akash Bajaj, Fang Liu, and Heather Kulik. “Uncovering Alternate Pathways to Nafion Membrane Degradation in Fuel Cells with First-Principles Modeling.” The Journal of Physical Chemistry C 124, no. 28 (2020): 15094-15106.

2019

18. Yao Wang, Juan Pablo Dehollain, Fang Liu, Uditendu Mukhopadhyay, Mark S. Rudner, Lieven M. K. Vandersypen, and Eugene Demler. “Ab Initio Exact Diagonalization Simulation of the Nagaoka Transition in Quantum Dots.” Phys. Rev. B 100 (2019): 155133.

17. Fang Liu, Tzuhsiung Yang, Jing Yang, Eve Xu, Akash Bajaj, and Heather Janine Kulik. “Bridging the homogeneous-heterogeneous divide: modeling spin and reactivity in single atom catalysis.” Front. Chem. 7 (2019): 219.

16. Jimmy Yu, Ruibinng Liang, Fang Liu, and Todd J. Martínez. “Characterization of the Elusive I Fluorescent State and the Ultrafast Photoisomerization of Retinal Protonated Schiff Base in Bacteriorhodopsin by Nonadiabatic Dynamics Simulation.” J. Am. Chem. Soc. 141 (2019): 18193.

15. Jon Paul Janet, Fang Liu, Aditya Nandy, Chenru Duan, Tzuhsiung Yang, Sean Lin, and Heather J. Kulik. “Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.” Inorg. Chem. 58 (2019): 10592.

14. Fang Liu, David M. Sanchez, Heather J. Kulik, and Todd J. Martínez. “Exploiting graphical processing units to enable quantum chemistry calculation of large solvated molecules with conductor-like polarizable continuum models.” Int. J. Quantum Chem. 119 (2019): e25760.

13. Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, and Heather J. Kulik. “Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.” J. Chem. Theory Comput. 15 (2019): 2331.

12. Ruibin Liang, Fang Liu, and Todd J. Martínez. “Nonadiabatic Photodynamics of Retinal Protonated Schiff Base in Channelrhodopsin 2.” J. Phys. Chem. Lett. 10 (2019): 2862.

11. Akash Bajaj, Fang Liu, and Heather J. Kulik. “Non-empirical, low-cost recovery of exact conditions with model-Hamiltonian inspired expressions in jmDFT.” J. Chem. Phys. 150 (2019): 154115.

10. Zhongyue Yang, Fang Liu, Adam H. Steeves, and Heather J. Kulik. “A Quantum Mechanical Description of Electrostatics Provides a Unified Picture of Catalytic Action Across Methyltransferases.” J. Phys. Chem. Lett. 10 (2019): 3779.

2018

9. Margaux M. Pinney, Aditya Natarajan, Filip Yabukarski, David M. Sanchez, Fang Liu, Ruibin Liang, Tzanko Doukov, Jason P. Schwans, Todd J. Martínez, and Daniel Herschlag. “Structural coupling throughout the active site hydrogen bond networks of ketosteroid isomerase and photoactive yellow protein.” J. Am. Chem. Soc. 140 (2018): 9827.

2017

8. Xin Li, Robert M. Parrish, Fang Liu, Sara I. L. Kokkila Schumacher, and Todd J. Martínez. “An ab initio exciton model including charge-transfer excited states.” J. Chem. Theory Comput. 13 (2017): 3493.

7. Michael Filatov, Fang Liu, and Todd J. Martínez. “Analytical derivatives of the individual state energies in ensemble density functional theory method. I. General formalism.” J. Chem. Phys. 147 (2017): 034113.

6. Shibdas Banerjee,^§ Fang Liu,^§ David M. Sanchez, Todd J. Martínez, and Richard N. Zare. “Pomeranz–Fritsch Synthesis of Isoquinoline: Gas-Phase Collisional Activation Opens Additional Reaction Pathways.” J. Am. Chem. Soc. 139 (2017): 14352-14355. [§ These two authors contribute equally]

2016

5. Robert M. Parrish, Fang Liu, and Todd J. Martínez. “Communication: A difference density picture for the self-consistent field ansatz.” J. Chem. Phys. 144 (2016): 131101.

4. Michael Filatov, Fang Liu, Kwang S. Kim, and Todd J. Martínez. “Self-consistent implementation of ensemble density functional theory method for multiple strongly correlated electron pairs.” Journal Chem. Phys. 145 (2016): 244104.

2015

3. Brendan D. Mar, Helena W. Qi, Fang Liu, and Heather J. Kulik. “Ab Initio screening approach for the discovery of lignin polymer breaking pathways.” J. Phys. Chem. A 119 (2015): 6551.

2. Fang Liu, Nathan Luehr, Heather J. Kulik, and Todd J. Martínez. “Quantum chemistry for solvated molecules on graphical processing units using polarizable continuum models.” J. Chem. Theory Comput. 11 (2015): 3131.

2014

1. Lee-Ping Wang, Alexey Titov, Robert McGibbon, Fang Liu, Vijay S. Pande, and Todd J. Martínez. “Discovering chemistry with an ab initio nanoreactor.” Nat. Chem. 6 (2014): 1044.