48 citations · 77 across the 6 of their papers we have counts for
6 papers
A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth
Jyri Kimari, Flyura Djurabekova, Kostas Sarakinos
We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an inter…
Effect of surface orientation on blistering of copper under high fluence keV hydrogen ion irradiation
A. Lopez-Cazalilla, C. Serafim, J. Kimari +6
Copper and hydrogen are among the most common elements that are widely used in industrial and fundamental research applications. Copper surfaces are often exposed to hydrogen in th…
Biased self-diffusion on Cu surface due to electric field gradients
Jyri Kimari, Ye Wang, Andreas Kyritsakis +2
Under strong electric fields, an arc of strong current flowing through plasma can link two metal surfaces even in ultra high vacuum. Despite decades of research, the chain of event…
Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics
Jussi Määttä, Viacheslav Bazaliy, Jyri Kimari +3
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for t…
The Compact Linear ee Collider (CLIC): Physics Potential
P. Roloff, R. Franceschini, U. Schnoor +1
The Compact Linear Collider, CLIC, is a proposed ee collider at the TeV scale whose physics potential ranges from high-precision measurements to extensive direct sensitivit…
Data sets and trained neural networks for Cu migration barriers
Jyri Kimari, Ville Jansson, Simon Vigonski +4
Kinetic Monte Carlo (KMC) is an efficient method for studying diffusion. A limiting factor to the accuracy of KMC is the number of different migration events allowed in the simulat…