Conference 2023

The 2023 annual MERCURY Conference for undergraduate computational chemistry was held July 19 -21 at Furman University in Greenville, SC. We were pleased to have six outstanding speakers, an undergraduate poster session and evening social networking events. The conference was preceded by a MOLSSI workshop July 17 – 19. The conference is an excellent forum for undergraduates to present their work and to learn from experts in the field, allowing them to put their own research into perspective. It is equally valuable as a networking event for faculty working with undergraduates. Undergraduates from all types of institutions are invited to come present their work.


Gregory Tschumper

Department of Chemistry and Biochemistry
University of Mississippi

A Hitchhiker’s Guide to High-Accuracy Computational Quantum Chemistry for
Hydrogen Bonding, Halogen Bonding and other Non-Covalent Interactions

The subjects of solvation, molecular recognition and supramolecular self-assembly provide some of the motivation and impetus for the work that is the focus of the talk. Convergent approaches to quantum mechanical (QM) ab initio electronic structure calculations have provided tremendous insight into the structures, energetics and spectroscopic signatures of molecular clusters held together by relatively weak, non-covalent interactions (London dispersion forces, hydrogen bonding, halogen bonding, π-stacking, etc.) [1-3]. Unfortunately, the computational demands associated with the most accurate and reliable QM methods often prohibit their application to large molecular systems. The first part of this talk will provide a very basic introduction to fundamental principles and concepts associated with QM electronic structure computations. It will focus on strategies that systematically converge toward exact numerical solutions of the electronic Schr¨odinger equation via methodical application of correlated wave function methods and Gaussian atomic orbital basis sets. This “crash course” in convergent computational quantum chemistry will use simple H2O clusters to provide concrete examples, and it will set the stage for an overview of computational techniques for non-covalent clusters that take advantage of the many-body expansion (MBE) of the total energy. A layered, ONIOM-like approach [4] to the MBE is one such technique that we have been using to extend demanding QM electronic structure computations, such as the CCSD(T) method, to larger systems [5]. If time permits, some recent applications of our 2-layer N-body:Many-body QM:QM method will also be discussed.

[1] “Reliable Electronic Structure Computations for Weak Noncovalent Interactions,” G.S. Tschumper in Reviews in Computational Chemistry; K.B. Lipkowitz and T.R. Cundari, Eds; Wiley: Hoboken, 26, 39–90 (2009).
[2] “Getting down to the Fundamentals of Hydrogen Bonding: Anharmonic Vibrational Frequencies of (HF)2 and (H2O)2 from Ab Initio Electronic Structure Computations,” J.C. Howard, J.L. Gray, A.J. Hardwick, L.T. Nguyen and G.S. Tschumper, J. Chem. Theory Comput., 10, 5426–5435 (2014).
[3] “Observation of the Low-Frequency Spectrum of the Water Trimer as a Sensitive Test of the Water-Trimer Potential and the Dipole-Moment Surface,” R. Schwan, C. Qu, D. Mani, N. Pal, G. Schwaab, J.M. Bowman, G.S. Tschumper and M. Havenith, em Angew. Chem. Int. Ed., 59, 11399–11407 (2020).
[4] “The ONIOM Method and Its Applications,” L.W. Chung, W.M.C. Sameera, R. Ramozzi, A.J.
Page, M. Hatanaka, G.P. Petrova, T.V. Harris, X. Li, Z. Ke, F. Liu, H.-B. Li, L. Ding, and K.
Morokuma Chem. Rev., 115, 5678–5796 (2015).
[5] “Benchmark Structures and Harmonic Vibrational Frequencies Near the CCSD(T) Complete Basis Set Limit for Small Water Clusters: (H2O)n=2,3,4,5,6,” J.C. Howard and G.S. Tschumper, J. Chem. Theory Comput., 11, 2126–2136 (2015).

Martin Head-Gordon

Kenneth S. Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, and, Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720, USA.

Beyond numerical experiments: Understanding physical and chemical driving forces by energy decomposition analysis of quantum chemistry calculations

Today, computational quantum chemistry calculations can provide near-quantitative answers to questions about the strength of covalent interactions, like chemical bonds, and non-covalent interactions like hydrogen bonds, with only modest compute requirements. This is a triumph of the quantum chemistry community, through development of state-of-the-art density functional theory (DFT) over the past 60 years. Yet the results of those calculations are like numerical experiments. They tell you the value of the quantity: for instance that the water-water hydrogen bond is 20 kJ/mol, or the H-H bond in hydrogen is over 400 kJ/mol. They do not tell you the driving force that causes the interaction. To fill this gap, it is desirable to have quantitative ways to associate contributions to binding with physical and chemical driving forces. This is the task of energy decomposition analysis (EDA), and although EDAs cannot be considered completely mature, they are increasingly used for applications such as understanding non-covalent interactions, chemical bonds, and even aspects of chemical catalysis and reactivity. After some review of DFT, I will describe the Absolutely Localized MO (ALMO) EDA, which attempts to decompose supermolecular calculations on molecular clusters into underlying physical driving forces, such as Pauli repulsions, permanent and induced electrostatics, dispersion, and charge-transfer [1]. I will also discuss its extension to treat chemical bonds, and what this reveals about the old yet not fully resolved question of the driving force behind covalent bonding [2]. If I have time, recent developments to refine the DFT-based ALMO-EDA, particularly for controversial questions such as charge-transfer versus polarization will be discussed.

[1] “From intermolecular interaction energies and observable shifts to component contributions and back again: A tale of variational energy decomposition analysis”, Y. Mao, M. Loipersberger, P.R. Horn, A. Das, O. Demerdash, D.S. Levine, S. Prasad Veccham, T. Head-Gordon, and M. Head-Gordon, Ann. Rev. Phys. Chem. 72, 641–66 (2021);

[2] “Clarifying the quantum mechanical origin of the covalent chemical bond”, D.S. Levine and M. Head-Gordon, Nature Comm. 11, 4893 (2020); doi: 10.1038/s41467-020-18670-8

Teresa Head-Gordon

Pitzer Theory Center
Departments of Chemistry, Bioengineering, and Chemical and Biomolecular
University of California, Berkeley, CA 94720

Physics-Inspired Machine Learning Methods: A Status Report on Predictive Chemistry

The combinatorial size of chemical molecule space, which compounds under variable
synthetic, catalytic, and/or non-equilibrium conditions, is vast. This makes application of
first principles quantum mechanical and advanced statistical mechanics sampling methods
to identify binding motifs, conformational equilibria, and reaction pathways extremely
challenging, even when considering better physical models, algorithms, or future exascale
computing paradigms. If we could develop new and robust machine learning approaches,
ideally grounded in physical principles, we would be able to better tackle many fascinating
but quite difficult chemical, biological, and materials systems. At present, the application
of machine learning to (bio)chemistry is still in its infancy, and I will describe applications
ranging from to chemical shift prediction, structural ensemble construction, new inhibitors
against SARS-Cov-2 target proteins, and gas phase reactions exemplified by hydrogen
combustion to see where machine learning is having impact.

Joan-Emma Shea

Department of Chemistry and Biochemistry
University of California, Santa Barbara

Simulations of Protein Folding and Assembly

In this lecture, I will introduce proteins, an important class of biomolecules, and present force fields and molecular dynamics simulation techniques used to investigate a protein’s conformational space. I will also discuss how proteins can self-assemble to form large aggregate structures under pathological conditions. As an application, I will present simulations of the self-assembly of the Tau protein. Aggregates of Tau have been implicated in a number of neurodegenerative diseases, including Alzheimer’s Disease.

Shikha Nangia

Department of Biomedical and Chemical Engineering
Syracuse Biomaterials Institute
Syracuse University

Introduction to Molecular Dynamics of Proteins: What can Jiggling and Wiggling of Molecules tell us?

Proteins are the workhorses of life—they perform most of the work in cells. In January 2023, the Protein Data Bank reported crossing 200,000 experimentally determined three-dimensional protein structures in the database. These proteins span unicellular species like bacteria to multicellular species, including humans. One thing that unites the proteins in all living organisms is that these biomolecules are in a state of constant motion or “jiggling or wiggling” at body temperatures. The laws of thermodynamics govern the thermal motion of atoms in the proteins. Although the protein can be large and complex, the physical movements of the atoms over time can be analyzed using Newton’s equation of motion in Molecular Dynamics (MD) simulations. The talk will focus on the fundamental concept of molecular dynamics, force fields, modeling proteins in atomistic and coarse grain resolutions, and applications of MD simulations for the structure and function of proteins at the blood-brain barrier interface and strategies for treating Alzheimer’s disease.

The dynamics of becoming a computational chemist in drug discovery: growth, development and change

The development of medicines is a complicated and expensive process.  Computational chemistry is at the core of this process, involved in many of the different aspects of drug discovery.  The collaboration between experimental and computational methods expedites drug development at different levels and an iterative process helps optimize molecules targeting the desired outcome.  Methods such as molecular dynamics (MD) and free energy perturbation (FEP) can help elucidate important protein-ligand interactions and aid in the understanding of the systems.  Professional and personal lives are intertwined, inevitably affecting one another. My professional life has been shaped by welcoming two baby girls during my PhD and COVID, and significantly impacted by a DOE fellowship. This presentation will include a discussion of both aspects and my journey to my present job at GSK.