TeraChem is general purpose quantum chemistry software designed to run on NVIDIA GPU architectures under a 64-bit Linux operating system. Our contribution to this software was the development of:
- GPU-accelerated implicit pressure model, XP-PCM, for describing quantum chemistry at extremely high pressure on the order of GPa.
- GPU-accelerated implicit solvent model, C-PCM, for ground state calculation (HF/DFT) and the excited state calculations (TDDFT)
- GPU-accelerated ensemble density functional theory, REKS, for energy, analytical gradient, and non-adiabatic coupling vector evaluations, which enables large scale non-adiabatic dynamics simulation of photochemical processes.
- GPU-accelerated restrained electrostatic potential fit charge model (RESP)
- Analytical 3rd derivatives of exchange correlation (XC) functionals in spin-unrestricted Kohn-Sham density functional theory (DFT).
Ariel Gale, Eugen Hruska, 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
F. Liu,* M. Filatov, and T. 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
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.
F. Liu, D. Sanchez, H. Kulik, and T. J. Martínez, Exploiting Graphical Processing Units to Enable Quantum Chemistry Calculation of Large Molecules in Polarizable Continuum Models, Int. J. Quantum Chem. 119, e25760 (2019) (Cover for special issue “Advances in Simulating Solvation”)
X. Li, R. M. Parrish, F. Liu, S. I. L. K. Schumacher, and T. J. Martínez, An ab initio Exciton Model Including Charge-Transfer Excited States, J. Chem. Theory Comput. 13, 3493 (2017)
M. Filatov, F. Liu, K. S. Kim, T. J. Martínez, Analytical Derivatives of the Individual State Energies in Ensemble Density Functional Theory Method. I. General formalism, J. Chem. Phys. 147, 034113 (2017)
R. M. Parrish, F. Liu, and T. J. Martínez, Communication: A Difference Density Picture for the Self-Consistent Field Ansatz., J. Chem. Phys. 144, 131101 (2016)
M. Filatov, F. Liu, K. S. Kim, and T. J. Martínez, Self-Consistent Implementation of Ensemble Density Functional Theory Method for Multiple Strongly Correlated Electron Pairs, J. Chem. Phys. 145, 244104 (2016)
F. Liu, N. Luehr, H. J. Kulik, and T. J. Martínez, Quantum Chemistry for Solvated Molecules on Graphical Processing Units (GPUs) using Polarizable Continuum Models, J. Chem. Theory Comput. 11, 3131 (2015)
AutoNEB is an automated toolkit for the first-principles studies of reaction pathways, especially in complicated reaction networks involving multiple conformers of the reaction species. Challenges remain in the automatic generation of conformers of an arbitrary molecule due to the high variability in bonding and charge state and the limited availability of suitable force field parameters needed in conventional conformer search methods, especially for charged species or radicals. We overcome this through automated enumeration of all possible combination of rotatable dihedrals, followed by automated pruning to generate featured conformers.
The open-source version of this toolkit is coming soon.
S. Banerjee,§F. Liu,§ D.M. Sanchez, T. J. Martínez, and R. N. Zare, Pomeranz-Fritsch Synthesis of Isoquinoline: Gas-Phase Collisional Activation Opens Additional Reaction Pathways, J. Am. Chem. Soc. 139, 14352 (2017) [ §These two authors contribute equally ]
MultirefPredict is an automated workflow to predict multireference character of molecules in quantum chemistry calculation
F. Liu, C. Duan, and H. J. 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