Global ETD Search

A scalable machine learning approach to thermal and non-thermal order-disorder phase transitions with ab initio accuracy

Corradini, AndreaUniversità degli studi di Trento2026

… methodological frameworks based on high-fidelity machine learning interatomic potentials. These tools are utilized to investigate three distinct out-of- equilibrium regimes: • Ultrafast non-thermal melting in silicon: a novel framework based on constrained density functional perturbation theory …

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Simulations of silicon-graphene anodes using machine-learning-based interatomic potentials

Liepuoniūtė, GuodaUniversity of Cambridge2025

… into silicon anodes has emerged as a potential solution to these challenges, but a complete simulation of the silicon-carbon systems with a full lithiation-delithiation cycle remains unexplored. This research approaches the aim by developing a Machine Learning Interatomic Potential …

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Investigations into liquid state physics in energy applications with machine-learning interatomic potentials: An active learning framework utilizing subascent

Lee, Shao-ChunUniversity of Illinois at Urbana-Champaign2024

Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01

uiuc Repository record for Investigations into liquid state physics in energy applications with machine-learning interatomic potentials: An active learning framework utilizing subascent (opens in a new tab)

Machine Learning Force Fields for Modelling Reactions at Complex Interfaces

Schaaf, LarsUniversity of Cambridge2025

… accuracy and computational cost. Recently, machine learning (ML) architectures have been trained on highly accurate quantum mechanical calculations to sidestep this constraint~\cite{behlerFirstPrinciplesNeural2017, bartokGaussianApproximationPotentials2010}. So-called machine learning force …

cambridge Repository record for Machine Learning Force Fields for Modelling Reactions at Complex Interfaces (opens in a new tab)

Application of machine learning potential to predict grain boundary properties and development of its performant implementation

Nishiyama, TakayukiKyoto University2022

kyoto Repository record for Application of machine learning potential to predict grain boundary properties and development of its performant implementation (opens in a new tab)