Adventures in DFTB: Toward an Automatic Parameterization Scheme

Atomistic insights into the hydrodefluorination of PFAS using silylium catalysts

Fluorochemicals are a persistent environmental contaminant that require specialized techniques for degradation and capture. In particular, recent attention on per- and poly-fluoroalkyl substances (PFAS) has led to numerous explorations of different techniques for degrading the super-strong C-F bonds found in these fluorochemicals. In this study, we investigated the hydrodefluorination mechanism using silylium-carborane salts for the degradation of PFAS at the density functional theory (DFT) level. We find that the degradation process involves both a cationic silylium (Et₃Si⁺) and a hydridic silylium (Et₃SiH) to facilitate the defluorination and hydride-addition events. Additionally, the role of carborane ([HCB₁₁H₅F₆]^-) is to force unoccupied anti-bonding orbitals to be partially occupied, weakening the C-F bond. We also show that changing the substituents on carborane from fluorine to other halogens weakens the C-F bond even further, with iodic carborane ([HCB₁₁H₅I₆]^-) having the greatest weakening effect. Moreover, our calculations reveal why the C-F bonds are resistant to degradation, and how the silylium-carborane chemistry is able to chemically transform these bonds into C-H bonds. We believe that our results are further applicable to other halocarbons, and can be used to treat either our existing stocks of these chemicals or to treat concentrated solutions following filtration and capture.

BENCHMARKS OF THE DENSITY FUNCTIONAL TIGHT-BINDING METHOD FOR REDOX, PROTONATION AND ELECTRONIC PROPERTIES OF QUINONES

Organic materials with controllable molecular design and sustainable resources are promising electrode materials. Crystalline quinones have been investigated in a variety of rechargeable battery chemistries due to their ubiquitous nature,...

Curvature Constrained Splines for DFTB Repulsive Potential Parametrization

The Curvature Constrained Splines (CCS) methodology has been used for fitting repulsive potentials to be used in SCC-DFTB calculations. The benefit of using CCS is that the actual fitting of the repulsive potential is performed through quadratic programming on a convex objective function. This guarantees a unique (for strictly convex) and optimum two-body repulsive potential in a single shot, thereby making the parametrization process robust, and with minimal human effort. Furthermore, the constraints in CCS give the user control to tune the shape of the repulsive potential based on prior knowledge about the system in question. Herein, we developed the method further with new constraints and the capability to handle sparse data. We used the method to generate accurate repulsive potentials for bulk Si polymorphs and demonstrate that for a given Slater-Koster table, which reproduces the experimental band structure for bulk Si in its ground state, we are unable to find one single two-body repulsive potential that can accurately describe the various bulk polymorphs of silicon in our training set. We further demonstrate that to increase transferability, the repulsive potential needs to be adjusted to account for changes in the chemical environment, here expressed in the form of a coordination number. By training a near-sighted Atomistic Neural Network potential, which includes many-body effects but still essentially within the first-neighbor shell, we can obtain full transferability for SCC-DFTB in terms of describing the energetics of different Si polymorphs.