5 research outputs found
Entropy and Energy Profiles of Chemical Reactions
The description of chemical processes at the molecular level is often
facilitated by use of reaction coordinates, or collective variables (CVs). The
CV measures the progress of the reaction and allows the construction of
profiles that track the evolution of a specific property as the reaction
progresses. Whereas CVs are routinely used, especially alongside enhanced
sampling techniques, links between profiles and thermodynamic state functions
and reaction rate constants are not rigorously exploited. Here, we report a
unified treatment of such reaction profiles. Tractable expressions are derived
for the free-energy, internal-energy, and entropy profiles as functions of only
the CV.We demonstrate the ability of this treatment to extract quantitative
insight from the entropy and internal-energy profiles of various real-world
physicochemical processes, including intramolecular organic reactions, ionic
transport in superionic electrolytes, and molecular transport in nanoporous
materials.Comment: 24 pages, 5 figures, 3 table
Entropy and Energy Profiles of Chemical Reactions
The description of chemical processes at the molecular level is often facilitated by use of reaction coordinates, or collective variables (CVs). The CV measures the progress of the reaction and allows the construction of profiles that track the evolution of a specific property as the reaction progresses. Whereas CVs are routinely used, especially alongside enhanced sampling techniques, links between profiles and thermodynamic state functions and reaction rate constants are not rigorously exploited. Here, we report a unified treatment of such reaction profiles. Tractable expressions are derived for the free-energy, internal-energy, and entropy profiles as functions of only the CV. We demonstrate the ability of this treatment to extract quantitative insight from the entropy and internal-energy profiles of various real-world physicochemical processes, including intramolecular organic reactions, ionic transport in superionic electrolytes, and molecular transport in nanoporous materials
Accelerating Hybrid Density Functional Theory Molecular Dynamics Simulations by Seminumerical Integration, Resolution-of-the-Identity Approximation, and Graphics Processing Units
The computationally
very demanding evaluation of the 4-center-2-electron
(4c2e) integrals and their respective integral derivatives typically
represents the major bottleneck within hybrid KohnâSham density
functional theory molecular dynamics simulations. Building upon our
previous works on seminumerical exact-exchange (sn-LinK) [Laqua, H.,
Thompsons, T. H., Kussmann, J., Ochsenfeld, C., J. Chem. Theory
Comput.2020,16, 1465] and
resolution-of-the-identity Coulomb (RI-J) [Kussmann, J., Laqua, H.,
Ochsenfeld, C., J. Chem. Theory Comput.2021,17, 1512], the expensive 4c2e integral evaluation
can be avoided entirely, resulting in a highly efficient electronic
structure theory method, allowing for fast ab initio molecular dynamics
(AIMD) simulations even with large basis sets. Moreover, we propose
to combine the final self-consistent field (SCF) step with the subsequent
nuclear forces evaluation, providing the forces at virtually no additional
cost after a converged SCF calculation, reducing the total runtime
of an AIMD simulation by about another 25%. In addition, multiple
independent MD trajectories can be computed concurrently on a single
node, leading to a greatly increased utilization of the available
hardwareespecially when combined with graphics processing
unit accelerationimproving the overall throughput by up to
another 5 times in this way. With all of those optimizations combined,
our proposed method provides nearly 3 orders of magnitude faster execution
times than traditional 4c2e integral-based methods. To demonstrate
the practical utility of the approach, quantum-mechanical/molecular-mechanical
dynamics simulations on double-stranded DNA were performed, investigating
the relative hydrogen bond strength between adenineâthymine
and guanineâcytosine base pairs. In addition, this illustrative
application also contains a general accuracy assessment of the introduced
approximations (integration grids, resolution-of-the-identity) within
AIMD simulations, serving as a protocol on how to apply these new
methods to practical problems
Chemically Realistic Tetrahedral Lattice Models for Polymer Chains: Application to Polyethylene Oxide
To speed up the generation
of an ensemble of polyÂ(ethylene oxide)
(PEO) polymer chains in solution, a tetrahedral lattice model possessing
the appropriate bond angles is used. The distance between noncovalently
bonded atoms is maintained at realistic values by generating chains
with an enhanced degree of self-avoidance by a very efficient Monte
Carlo (MC) algorithm. Potential energy parameters characterizing this
lattice model are adjusted so as to mimic realistic PEO polymer chains
in water simulated by molecular dynamics (MD), which serves as a benchmark.
The MD data show that PEO chains have a fractal dimension of about
two, in contrast to self-avoiding walk lattice models, which exhibit
the fractal dimension of 1.7. The potential energy accounts for a
mild hydrophobic effect (HYEF) of PEO and for a proper setting of
the distribution between trans and gauche conformers. The potential
energy parameters are determined by matching the Flory radius, the
radius of gyration, and the fraction of trans torsion angles in the
chain. A gratifying result is the excellent agreement of the pair
distribution function and the angular correlation for the lattice
model with the benchmark distribution. The lattice model allows for
the precise computation of the torsional entropy of the chain. The
generation of polymer conformations of the adjusted lattice model
is at least 2 orders of magnitude more efficient than MD simulations
of the PEO chain in explicit water. This method of generating chain
conformations on a tetrahedral lattice can also be applied to other
types of polymers with appropriate adjustment of the potential energy
function. The efficient MC algorithm for generating chain conformations
on a tetrahedral lattice is available for download at https://github.com/Roulattice/Roulattice
Predicting Fâ NMR Chemical Shifts: A Combined Computational and Experimental Study of a Trypanosomal OxidoreductaseâInhibitor Complex
The absence of fluorine from most biomolecules renders it an excellent probe for NMR spectroscopy to monitor inhibitorâprotein interactions. However, predicting the binding mode of a fluorinated ligand from a chemical shift (or vice versa) has been challenging due to the high electron density of the fluorine atom. Nonetheless, reliable F chemicalâshift predictions to deduce ligandâbinding modes hold great potential for inâ
silico drug design. Herein, we present a systematic QM/MM study to predict the Fâ
NMR chemical shifts of a covalently bound fluorinated inhibitor to the essential oxidoreductase tryparedoxin (Tpx) from African trypanosomes, the causative agent of African sleeping sickness. We include many proteinâinhibitor conformations as well as monomeric and dimeric inhibitorâprotein complexes, thus rendering it the largest computational study on chemical shifts of F nuclei in a biological context to date. Our predicted shifts agree well with those obtained experimentally and pave the way for future work in this area