Modeling Electron Transfers Using Quasidiabatic Hartree-Fock States.

Electron transfer processes are ubiquitous in chemistry and of great importance in many systems of biological and commercial interest. The ab initio description of these processes remains a challenge in theoretical chemistry, partly due to the high scaling of many post-Hartree-Fock computational methods. This poses a problem for systems of interest that are not easily investigated experimentally.

Geometry Optimization with Machine Trained Topological Atoms.

The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated.

The computational prediction of Raman and ROA spectra of charged histidine tautomers in aqueous solution.

Histidine is a key component of a number of enzymatic mechanisms, and undertakes a myriad of functionalities in biochemical systems. Its computational modelling can be problematic, as its capacity to take on a number of distinct formal charge states, and tautomers thereof, is difficult to capture by conventional techniques. We demonstrate a means for recovering the experimental Raman optical activity (ROA) spectra of histidine to a high degree of accuracy.

Prediction of conformationally dependent atomic multipole moments in carbohydrates.

The conformational flexibility of carbohydrates is challenging within the field of computational chemistry. This flexibility causes the electron density to change, which leads to fluctuating atomic multipole moments. Quantum Chemical Topology (QCT) allows for the partitioning of an "atom in a molecule," thus localizing electron density to finite atomic domains, which permits the unambiguous evaluation of atomic multipole moments.

Realistic sampling of amino acid geometries for a multipolar polarizable force field.

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB).

Multipolar electrostatics.

Atomistic simulation of chemical systems is currently limited by the elementary description of electrostatics that atomic point-charges offer. Unfortunately, a model of one point-charge for each atom fails to capture the anisotropic nature of electronic features such as lone pairs or π-systems. Higher order electrostatic terms, such as those offered by a multipole moment expansion, naturally recover these important electronic features.