Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials

A Lanjan and Z Moradi and S Srinivasan, JOURNAL OF PHYSICAL CHEMISTRY A, 127, 6603-6613 (2023).

DOI: 10.1021/acs.jpca.3c02887

Thedesign and evaluation of future nanomaterials withspecificproperties is a challenging task as the current traditional methodsrely on trial and error approaches that are time-consuming and expensive.On the computational front, design tools such as molecular dynamics(MD) simulations help us reduce the costs and times. However, nonbondedpotential parameters, the key input parameters for an MD simulation,are usually not available for designing and studying new materials.Resolving this, quantum mechanics (QM) calculations could be usedto evaluate the system's energy as a function of the nonbondeddistances, and the resulting data set could be fit to a generic potentialequation to obtain the fitting constants (potential parameters). However,fitting this massive data set containing thousands of unknown parametersusing traditional mathematical formulations is not feasible. Hence,most computational frameworks in the literature utilize several simplifications,leading to a severe loss of accuracy. Addressing this deficiency,in this work, we propose a multi-scale framework that couples QM calculationsand MD with advanced deep neural networks to determine the potentialparameters. This advanced framework has been extensively validatedby employing it to predict properties such as the density, boilingpoint, and melting point of five different types of molecules thatare well-understood, namely, the polar molecule H2O, ioniccompound LiPF6, ethanol (C2H5OH),long-chain molecule C8H18, and the complex molecularsystem ethylene carbonate (EC).

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