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Bac, Selin

Publications and source records attributed to Bac, Selin.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Controlling selectivity for dechlorination of poly(vinyl chloride) with (xantphos)RhCl

Reaction of poly(vinyl chloride) (PVC) with 5 equiv. of triethyl silane in THF, in the presence of in situ generated (xantphos)RhCl catalyst, results in partial reduction of PVC via hydrodechlorination to yield poly(vinyl chloride-co-ethylene). Increasing catalyst loading or using N,N-dimethylacetamide (DMA) as a solvent both diminished selectivity for hydrodechlorination, promoting competitive dehydrochlorination reactions. Reaction of PVC with 2 equiv. of sodium formate in THF in the presence of (xantphos)RhCl affords excellent selectivity for hydrodechlorination along with complete PVC dechlorination, yielding polyethylene-like polymers. Higher catalyst loadings were necessary to activate PVC towards reduction in this case. In contrast, reaction of PVC with 1 equiv. of NaH in DMA, in the presence of (xantphos)RhCl, exhibited good selectivity for dehydrochlorination, as well as much higher reaction rates. These results combined shed light on the interplay between critical reaction parameters that control PVC's mode of reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The harmonic variety-based matrix completion (HVMC) algorithm, developed in a previous study (https://doi.org/10.1063/5.0018326), exploits the low-rank character of the polynomial expansion of potential energy to recover, using a small sample, vibrational frequencies (square roots of nuclear Hessian eigenvalues) constituting the reaction path. Furthermore, these frequencies are essential for calculating rate coefficients using variational transition state theory with multidimensional tunneling (VTST-MT). HVMC performance is examined for four SN2 reactions and five hydrogen transfer reactions, with each H-transfer reaction consisting of at least one vibrational mode strongly coupled to the reaction coordinate. HVMC is robust and captures zero-point energies, vibrational free energies, zero-curvature tunneling, and adiabatic ground state and free energy barriers as well as their positions on the reaction coordinate. For medium to large reactions involving H-transfer, with the exception of the most complex Ir catalysis system, less than 35% of total eigenvalue information is necessary for accurate recovery of key VTST-MT observables.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗