Structure Conformation, Vibrational Properties, NLO Activity, HOMO-LUMO and Thermodynamic Parameters of Dinicotinic Acid Using Experimental and Theoretical Approach

In the present study, the Dinicotinic acid was characterized by FTIR and FT-Raman spectra in the range of 4000-450 and 4000-50 cm-1. The most stable molecular structure and optimized geometrical parameters are calculated using DFT studies. Normal Co-ordinate Analysis (NCA) was studied out by solving Inverse Vibrational Problem using 74-valence force field calculations using overlay least square technique. It reproduces into 35 fundamental frequencies with an rms error of 9.28 cm-1 in the zero-order calculations. Based on PED, vibrational modes are assigned for this molecule. The energy of HOMO & LUMO, NLO parameters, and thermodynamic parameters were computed.

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

<p>High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au₁₃⁺ and Au₁₁⁺ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.</p>

Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

<p>High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au₁₃⁺ and Au₁₁⁺ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.</p>