2 citations · 2 across the 3 of their papers we have counts for
6 papers
PYTHIA, a transferable machine learning framework for simultaneous defect and adsorption site optimization
Ioannis Kouroudis, Zhaosu Gu, Poonam +2
Machine learning has permeated material optimization, providing high value. Nevertheless, its re- liance on a high number of samples limits its applicability to relatively explored…
Accelerated surrogate dynamics for dynamical, stochastic system evolution
Marco Jochum, Ioannis Kouroudis, Gohar Ali Siddiqui +3
Dynamic simulations are an entrenched way of gaining insight into the evolution of system dynamics. Their computational cost however is often prohibitively high, especially in case…
Probabilistic Deep Learning Framework for Phase Transformation Forecasting aided by In Situ High temperature Microscopy
Ioannis Kouroudis, Niki Balestrieri, Stefan Rotzsche +3
The mechanical performance of structural materials is governed by their microstructure, which is itself set by the thermal history imposed during processing. Predicting how that mi…
A green solvent screening tool for emerging materials via uncertainty aware, transformer enhanced transfer learning
Ioannis Kouroudis, Simon Ternes, Zhaosu Gu +5
Accurate prediction of solubility remains a central challenge across materials science and sustainable chemistry. In particular due to emerging technologies like organic and hybrid…
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites
Ioannis Kouroudis, Poonam, Neel Misciaci +4
In this paper, we propose a novel flexible optimization pipeline for determining the optimal adsorption sites, named AUGUR (Aware of Uncertainty Graph Unit Regression). Our model c…
Machine-Learning-Optimized Perovskite Nanoplatelet Synthesis
Carola Lampe, Ioannis Kouroudis, Milan Harth +3
With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. This can be an extremely tedious process, o…