Research: Methods and Software

Our group develops new approximations in electronic structure theory, implements these approximations as efficient computer programs (written in C++ and Python), and applies these methods to study challenging chemical problems. We are particularly interested in new methods for electron correlation and non-covalent interactions. We have developed the world's fastest wavefunction-based symmetry adapted perturbation theory (SAPT) program, which computes the strength of the interaction between two molecules, using reliable quantum mechanical methods. Moreover, SAPT not only provides the total interaction energy, but also how it is broken down into contributions from the fundamental forces of intermolecular interactions: elecrostatics, induction/polarization, London dispersion forces, and short-range exchange (steric) repulsion. We have also extended the theory of SAPT to allow it to compute interaction energies between pairs of atoms in a larger pair of molecules, or between pairs of functional groups (fragment-based SAPT, or F-SAPT). We also developed an intramolecular variant, ISAPT, capable of computing interactions between two parts of the same molecule.

We are one of the primary developers of the popular open-source quantum chemistry program Psi4. Our philosophy is to write program-flow and user-interaction parts of the code using Python, which is easy to write and maintain, and to write lower-level parts of the code, which are critical for fast execution, in C++. We also develop Psi4 in a way that encourages the community to create a software ecosystem. Instead of each quantum chemistry program, like Psi4, re-implementing basic functionality like geometry optimizers, density functionals, etc., we encourage and support the development of reusable software components that might be shared amongst multiple computational chemistry projects.

Representative Publications:

  • “QCManyBody: A Flexible Implementation of the Many-Body Expansion,” L. A. Burns, C. D. Sherrill, and B. P. Pritchard, J. Chem. Phys. 161, 152501 (2024) (doi: 10.1063/5.0231843)
  • “Electrostatically Embedded Symmetry Adapted Perturbation Theory,” C. S. Glick, A. Alenaizan, C. E. Cavender, and C. D. Sherrill, J. Chem. Phys. 161, 134112 (2024) (doi: 10.1063/5.0221974)
  • “Accurate and Efficient Open-Source Implementation of Domain-Based Local Pair Natural Orbital (DLPNO) Coupled-Cluster Theory Using a T1-Transformed Hamiltonian,” A. Jiang, Z. L. Glick, D. Poole, J. M. Turney, C. D. Sherrill, and H. F. Schaefer, J. Chem. Phys. 161, 082502 (2024) (doi: 10.1063/5.0219963)
  • “A Physics-Aware Neural Network for Protein-Ligand Interactions with Quantum Chemical Accuracy,” Z. L. Glick, D. P. Metcalf, C. T. Sargent, S. A. Spronk, A. Koutsoukas, D. L. Cheney, and C. D. Sherrill, Chem. Sci. 15 13313-13324 (2024) (doi: 10.1039/D4SC01029A)
  • “A Modular, Composite Framework for the Utilization of Reduced-Scaling Coulomb and Exchange Construction Algorithms: Design and Implementation,” D. Poole, D. B. Williams-Young, A. Jiang, Z. L. Glick, and C. D. Sherrill, J. Chem. Phys. 161, 052503 (2024) (doi: 10.1063/5.0216760)
  • “Broadening Access to Small-Molecule Parameterization with the Force Field Toolkit,” Y. Zeng, A. Pavlova, P, Nelson, Z. Glick, L. Yang, Y. T. Pang, M. Spivak, G. Licari, E. Tajkhorshid, C. D. Sherrill, and J. C. Gumbart, J. Chem. Phys. 160, 242501 (2024) (doi: 10.1063/5.0196848)
  • “Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction,” D. P. Metcalf, Z. L. Glick, A. Bortolato, A. Jiang, D. L. Cheney, and C. D. Sherrill, J. Chem. Inf. Model. 64, 1907-1918 (2024) (doi: 10.1021/acs.jcim.3c02054)
  • “Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and Interoperability Among Computational Chemistry Programs,” D. G. A. Smith, A. T. Lolinco, Z. L. Glick, J. Lee, A. Alenaizan, T. A. Barnes, C. H. Borca, R. Di Remigio, D. L. Dotson, S. Ehlert, A. G. Heide, M. F. Herbst, J. Hermann, C. B. Hicks, J. T. Horton, A. G. Hurtado, P. Kraus, H. Kruse, S. J. R. Lee, J. P. Misiewicz, L. N. Naden, F. Ramezanghorbani, M. Scheurer, J. B. Schriber, A. C. Simmonett, J. Steinmetzer, J. R. Wagner, L. Ward, M. Welborn, D. Atlarawy, J. Anwar, J. D. Chodera, A. Dreuw, H. T. Kulik, F. Liu, T. J. Martínez, D. A. Matthews, H. F. Schaefer, J. Šponer, J. T. Turney, L.-P. Wang, N. De Silva, R. A. King, J. F. Stanton, M. S. Gordon, T. L. Windus, C. D. Sherrill, and L. A. Burns, J. Chem. Phys. 155, 204801 (2021). (doi: 10.1063/5.0059356)
  • “CLIFF: A Component-Based, Machine-Learned, Intermolecular Force Field,” J. B. Schriber, D. R. Nascimento, A. Koutsoukas, S. A. Spronk, D. L. Cheney, and C. D. Sherrill, J. Chem. Phys. 154, 184110 (2021). (doi: 10.1063/5.0042989)
  • “Psi4 1.4: Open-Source Software for High-Throughput Quantum Chemistry,” D. G. A. Smith, L. A. Burns, A. C. Simmonett, R. M. Parrish, M. C. Schieber, R. Galvelis, P. Kraus, H. Kruse, R. Di Remigio, A. Alenaizan, A.M. James, S. Lehtola, J. P. Misiewicz, M. Scheurer, R. A. Shaw, J. B. Schriber, Y. Xie, Z. L. Glick, D. A. Sirianni, J. Senan O’Brien, J. M. Waldrop, A. Kumar, E. G. Hohenstein, B. P. Pritchard, B. R. Brooks, H. F. Schaefer, A. Yu. Sokolov, K. Patkowski, A. E. DePrince, U. Bozkaya, R. A. King, F. A. Evangelista, J. M. Turney, T. D. Crawford, and C. D. Sherrill, J. Chem. Phys. 152, 184108 (2020). (doi: 10.1063/5.0006002)