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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Computational Discovery and Design of Materials
Presentation Title	The Integration of VASP 6’s Machine Learning Algorithms into the Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces Code to for Melting Point Determination
Author(s)	Audrey CampBell, Qijun Hong
On-Site Speaker (Planned)	Audrey CampBell
Abstract Scope	We integrated the machine learning abilities of VASP version 6 into the first principles automated computational code, SLUSCHI, which was written to determine the melting temperature by calculating the solid-liquid phase boundary. Our results suggest that for some computationally expensive simulations the integrated machine learning version of SLUSCHI significantly reduces the total computation time for the simulation. The computation time is reduced because the ML force fields, produced by VASP 6, allow for the extrapolation between first principles DFT calculations. This extrapolation results in a decrease in the number of DFT calculations per trajectory. Each new DFT calculation updates the stored force field, increasing memory demand. To reduce the amount of stored data we ask VASP to overwrite the oldest data points, this can still be a limiting factor. Newer versions of VASP 6 should allow for further optimization to decrease memory demand and computational time.
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning

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