Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning

Knowledge of the thermodynamic properties of molecules is essential for chemical process design and the development of new materials. Experimental measurements are often expensive and not environmentally friendly. In the past, studies using molecular simulations have focused on a specific class of molecules, owing to the lack of a consistent force field and simulation protocol. To solve this problem, we have developed a high-throughput force field simulation (HT-FFS) procedure by combining a recently developed general force field with a validated simulation protocol to calculate thermodynamic properties for large number of molecules. This procedure is applied to calculate liquid densities, heats of vaporization, heat capacities, vapor-liquid equilibrium curves, critical temperatures, critical densities and surface tensions for a wide range of alkanes. The predictions agree well with available experimental data in terms of accuracy and precision, demonstrating that HT-FFS is a valid approach to supplementing experimental measurements. Furthermore, the large amount of data generated by HT-FFS lays a foundation for machine learning. We have developed an artificial neural network that demonstrates the feasibility of expanding predictions beyond simulation using a machine learning model.