10.1021/acs.jcim.8b00407.s002
Zheng Gong
Zheng
Gong
Yanze Wu
Yanze
Wu
Liang Wu
Liang
Wu
Huai Sun
Huai
Sun
Predicting Thermodynamic Properties of Alkanes by
High-Throughput Force Field Simulation and Machine Learning
American Chemical Society
2018
measurement
density
simulation protocol
Machine Learning Knowledge
chemical process design
high-throughput force field simulation
molecule
prediction
data
HT-FFS
High-Throughput Force Field Simulation
force field
procedure
2018-09-11 00:00:00
Journal contribution
https://acs.figshare.com/articles/journal_contribution/Predicting_Thermodynamic_Properties_of_Alkanes_by_High-Throughput_Force_Field_Simulation_and_Machine_Learning/7135796
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.