For complex molecular systems, computational methods can provide fine details that are not readily available from experimental methods. However, some insight can only come from thorough exploration of the potential energy surface (PES) for a molecular system. Exhaustive PES exploration using quantum mechanical methods is very time- and resource-consuming.
Empirical reactive force fields can provide a more cost-effective alternative to quantum mechanical methods. However, parameterizing more general force fields is typically a very involved process. Machine-learned force fields are another low-cost method for exploring PES’s, relative to quantum mechanical methods.1 I will discuss how neural network algorithms have been used to parameterize reactive force fields with minimal user input.2 I will also discuss a Gaussian Process Regression model of a PES for a system that is relevant to astrochemistry and combustion chemistry.3 This PES has been used to predict the rate constant of a reaction.
Additionally, I will discuss a Gaussian Process Regression method that is transferable to larger systems.4 This method has recently been extended to inter- and intramolecular potentials separately, adaptively learn a PES, and has been applied to study the dimerization of cyclopentadiene.5
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