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Updated: Sep 12, 2025
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
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Decoding Temperature-Dependent Conformer Populations and Dipole Moments in Liquid Ethylene Glycol with Classical and
Anjali Gaur1, Nikhil V S Avula1, Sundaram Balasubramanian1
1Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560 064, India.
Journal of Chemical Theory and Computation
|August 7, 2025
Summary
Accurate simulation of liquid ethylene glycol (EG) conformations was achieved using deep neural network potentials. The study confirms the trans conformer population increases with temperature, reaching 24% at 400 K.
Area of Science:
- Computational Chemistry
- Molecular Dynamics
- Materials Science
Background:
- Accurately determining conformer populations in liquid ethylene glycol (EG) is difficult due to experimental resolution limits and simulation inaccuracies.
- Previous ab initio MD (AIMD) simulations suggested a ~20% trans conformer population but suffered from limited sampling.
- Deep neural network potentials offer a promising avenue for overcoming these simulation challenges.
Purpose of the Study:
- To develop and validate deep neural network-based potentials (DP) for accurate simulation of liquid ethylene glycol.
- To determine the conformer populations of liquid EG across a range of temperatures.
- To investigate the factors influencing the temperature dependence of conformer populations and molecular dipole moment.
Main Methods:
- Development of deep neural network potentials (DP) fitted to density functional theory (DFT) data.
- Running extensive molecular dynamics (MD) simulations using the developed DP models.
- Utilizing Wannier Centroid analysis with a Deep Wannier model for dipole moment calculations.
Main Results:
- The DP models accurately reproduced structural properties and provided well-converged conformer populations.
- The trans conformer population was confirmed to be 22% at ambient conditions, increasing with temperature.
- The trans population saturates at 24% around 400 K, influenced by intermolecular distances and hydrogen bonding.
Conclusions:
- Deep neural network potentials significantly improve the accuracy and efficiency of simulating liquid ethylene glycol.
- Temperature plays a crucial role in governing the conformational landscape and polarity of liquid EG.
- The study provides reliable data on conformer populations and dipole moments, essential for understanding EG's behavior.
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