(477c) Finetuna: Fine-Tuning Accelerated Molecular Simulations

cantly accelerated through the efficient simulation of atomistic systems. However, simulation

techniques based on first principles, such as Density Functional Theory (DFT), are limited

in their practical use due to their high computational expense. Machine learning approaches

have the potential to approximate DFT in a computationally efficient manner, which could

dramatically increase the impact of computational simulations on real-world problems.

However, they are limited by their accuracy and the cost of generating labeled data. Here,

we present an online active learning framework for accelerating the simulation of atomic

systems efficiently and accurately by incorporating prior physical information learned by

large-scale pre-trained graph neural network models from the Open Catalyst Project. Accel-

erating these simulations enables useful data to be generated more cheaply, allowing better

models to be trained and more atomistic systems to be screened. We also present a method

of comparing local optimization techniques on the basis of both their speed and accuracy.

Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer

learning to incorporate prior information from pre-trained models accelerates simulations by

reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of

0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive

functionality built in to VASP to efficiently compute single point calculations within our

online active learning framework without the significant startup costs. This allows VASP to

work in tandem with our framework while requiring 75% fewer self-consistent cycles than

conventional single point calculations.