Statistical analysis of activation and reaction energies with quasi-variational coupled-cluster theory

A General Nucleation Model for Semiconductor Nanocrystals

We introduce a nonclassical model for nanocrystal nucleation in solution which centers on the dynamic interplay of chemical bond breakage and formation coupled with the desolvation of precursor molecules, which we term the molecular chemistry (MC) model. Departing from classical theory, our model employs the bond count as the key variable rather than particle size, thereby redefining the role of supersaturation and its role in determining the so-called critical nucleus size. We apply the model to CdSe nanocrystal formation in nonpolar solvents and showcase its efficacy in predicting solvent dynamics, precursor characteristics, crystal phase, stoichiometry, "magic number" behavior, and transition states. While the coupled-cluster method is used to determine the bond energy, we show that it is possible to derive reaction pathways by reducing the calculations to algebraic approximations for the nucleation energy. This singular set of bond energy parameters allows nanocrystal nucleation and growth to be conceptualized as a straightforward chemical reaction.

Orbital optimisation in xTC transcorrelated methods

We present a combination of the bi-orthogonal orbital optimisation framework with the recently introduced xTC version of transcorrelation. This allows us to implement non-iterative perturbation based methods on top of the transcorrelated Hamiltonian. Additionally, the orbital optimisation influences results of other truncated methods, such as the distinguishable cluster with singles and doubles. The accuracy of these methods in comparison to standard xTC methods is demonstrated, and the advantages and disadvantages of the orbital optimisation are discussed.

Transcorrelated coupled cluster methods

Transcorrelated coupled cluster and distinguishable cluster methods are presented. The Hamiltonian is similarity transformed with a Jastrow factor in the first quantization, which results in up to three-body integrals. The coupled cluster with singles and doubles equations on this transformed Hamiltonian are formulated and implemented. It is demonstrated that the resulting methods have a superior basis set convergence and accuracy to the corresponding conventional and explicitly correlated methods. Additionally, approximations for three-body integrals are suggested and tested.

Comprehensive Understanding of Fluoroacetate Dehalogenase-Catalyzed Degradation of Fluorocarboxylic Acids: A QM/MM Approach

Fluorochemicals are persistent, bioaccumulative, and toxic compounds that are widely tributed in the environment. Developing efficient biodegradation strategies to decompose the fluorochemicals via breaking the inert C-F bonds presents a holistic challenge. As a promising biodegradation enzyme candidate, fluoroacetate dehalogenase (FAcD) has been reported as the only non-metallic enzyme to catalyze the cleavage of the strong C-F bond. Here, we systematically investigated the catalytic actions of FAcD toward its natural substrate fluoroacetate using molecular dynamics simulations and quantum mechanism/molecular mechanism calculations. We propose that the enzymatic transformation involves four elementary steps, (I) C-F bond activation, (II) nucleophilic attack, (III) C-O bond cleavage, and (IV) proton transfer. Our results show that nucleophilic attack is the rate-determining step. However, for difluoroacetate and trifluoroacetate, C-F bond activation, instead of nucleophilic attack, becomes the rate-determining step. We show that FAcD, originally recognized as α-fluorocarboxylic acid degradation enzyme, can catalyze the defluorination of difluoroacetate to glyoxylate, which is captured by our high-resolution mass spectrometry experiments. In addition, we employed amino acid electrostatic analysis method to screen potential mutation hotspots for tuning FAcD's electrostatic environment to favor substrate conversion. The comprehensive understanding of catalytic mechanism will inform a rational enzyme engineering strategy to degrade fluorochemicals for benefits of environmental sustainability.

FCIQMC-Tailored Distinguishable Cluster Approach

The tailored approach is applied to the distinguishable cluster method together with a stochastic FCI solver (FCIQMC). It is demonstrated that the new method is more accurate than the corresponding tailored coupled cluster and the pure distinguishable cluster methods. An F12 correction for tailored methods and FCIQMC is introduced, which drastically improves the basis set convergence. A new black-box approach to define the active space using the natural orbitals from the distinguishable cluster is evaluated and found to be a convenient alternative to the usual CASSCF approach.

The Molpro quantum chemistry package

Molpro is a general purpose quantum chemistry software package with a long development history. It was originally focused on accurate wavefunction calculations for small molecules but now has many additional distinctive capabilities that include, inter alia, local correlation approximations combined with explicit correlation, highly efficient implementations of single-reference correlation methods, robust and efficient multireference methods for large molecules, projection embedding, and anharmonic vibrational spectra. In addition to conventional input-file specification of calculations, Molpro calculations can now be specified and analyzed via a new graphical user interface and through a Python framework.

Harnessing fluoroacetate dehalogenase for defluorination of fluorocarboxylic acids: in silico and in vitro approach

Widely distributed fluorocarboxylic acids have aroused worldwide environmental concerns due to its toxicity, persistence, and bioaccumulation. Enzyme-based eco-friendly biodegradation techniques have become increasingly important in treating fluorocarboxylic acids. Here we utilized in silico and in vitro approaches to investigate the defluorination mechanism of fluoroacetate dehalogenase (FAcD) toward monofluoropropionic acids at atomic-level. The experimentally determined k_cat and k_M for defluorination of 2-fluoropropionic acid are 330 ± 60 min^-1 and 6.12 ± 0.13 mM. The in silico results demonstrated positive/negative correlations between activation barriers and structural parameters (e.g. distance and angle) under different enzymatic conformations. We also screened computationally and tested in vitro (enzyme assay and kinetic study) the catalytic proficiency of FAcD toward polyfluoropropionic acids and perfluoropropionic acids which are known to be challenging for enzymatic degradation. The results revealed potential degradation activity of FAcD enzyme toward 2,3,3,3-tetrafluoropropionic acids. Our work will initiate the development of a new "integrated approach" for enzyme engineering to degrade environmentally persistent fluorocarboxylic acids.