3 research outputs found
Molecular Dynamics and Quantum Chemical Approach for the Estimation of an Intramolecular Hydrogen Bond Strength in Okadaic Acid
We have evaluated the strength of intramolecular hydrogen
bond in a protein based on molecular dynamics and quantum chemical
calculation. To estimate the intramolecular hydrogen bond strength
in okadaic acid (OA), we analyzed the influence of solvent and protonation
states on the hydrogen bond and the entire structure. We performed
molecular dynamics calculation and analyzed the strength of the hydrogen
bond by measuring bond length and bond angle. The stable structure
differs depending on the kind of solvent used and the protonation
state of OA. Using the mean interaction energy from the quantum chemical
calculation, hydrogen bond length and angle were investigated against
bond energy. Although dielectric constant slightly depends on bond
energy, the estimation of the intramolecular hydrogen bond strength
in OA is possible even in a protein environment. The Coulomb interaction
between OA and surrounding arginine produced a more negatively charged
O1 in OA. The hydrogen bond energy in the deprotonated state is larger
than that in the protonated state
Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules
Density functional theory (DFT) is a significant computational
tool that has substantially influenced chemistry, physics, and materials
science. DFT necessitates parametrized approximation for determining
an expected value. Hence, to predict the properties of a given molecule
using DFT, appropriate parameters of the functional should be set
for each molecule. Herein, we optimize the parameters of range-separated
functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO)
to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness
of the BO in optimizing functional parameters. Particularly, Koopmans’
theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to
the experimental UV-absorption values. Furthermore, we prepared an
optimized parameter dataset of KTLC-BLYP for over 3000 molecules through
BO for satisfying Koopmans’ theorem. We have developed a machine
learning model on this dataset to predict the parameters of the LC-BLYP
functional for a given molecule. The prediction model automatically
predicts the appropriate parameters for a given molecule and calculates
the corresponding values. The approach in this paper would be useful
to develop new functionals and to update the previously developed
functionals
Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules
Density functional theory (DFT) is a significant computational
tool that has substantially influenced chemistry, physics, and materials
science. DFT necessitates parametrized approximation for determining
an expected value. Hence, to predict the properties of a given molecule
using DFT, appropriate parameters of the functional should be set
for each molecule. Herein, we optimize the parameters of range-separated
functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO)
to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness
of the BO in optimizing functional parameters. Particularly, Koopmans’
theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to
the experimental UV-absorption values. Furthermore, we prepared an
optimized parameter dataset of KTLC-BLYP for over 3000 molecules through
BO for satisfying Koopmans’ theorem. We have developed a machine
learning model on this dataset to predict the parameters of the LC-BLYP
functional for a given molecule. The prediction model automatically
predicts the appropriate parameters for a given molecule and calculates
the corresponding values. The approach in this paper would be useful
to develop new functionals and to update the previously developed
functionals