Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Nikola Koutná

Tenure-track Assist. Prof

TU Wien

Austria

Research Interests

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Explore related searches

Professors in Austria Professors in Condensed Matter Physics Condensed Matter Physics professors in Austria

Articles (18)

Year:

2026

Year:

2026

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Collaborators (13)

zhuo chen

Erich Schmid Institut für Materialwissenschaft

AUSTRIA

View details

Alexander Kirnbauer

Senior Scientist

TU Wien

AUSTRIA

View details

Zaoli Zhang

Senior scientist/group leader

Austrian Academy of Sciences

AUSTRIA

View details

Paul Mayrhofer

University Professor

TU Wien

AUSTRIA

View details

James Kermode

Professor

University of Warwick

UNITED KINGDOM

View details

Davide Sangiovanni

Linköping University

SWEDEN

View details

Andreas Limbeck

TU Wien

AUSTRIA

View details

Lars Hultman

Linköping University

SWEDEN

View details

Rainer Hahn

TU Wien

AUSTRIA

View details

Martin Zelený

Brno University of Technology

CZECH REPUBLIC

View details

Shuyao Lin

TU Wien

AUSTRIA

View details

Tomasz Wojcik

TU Wien

AUSTRIA

View details

Lukas Zauner

TU Wien

AUSTRIA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Nikola Koutná

Research Interests

Make your next move.

Explore related searches

Articles (18)

Vacancy diffusion in transition metal diborides using diverse machine-learning potentials

Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB2

Machine-learning potentials predict orientation- and mode-dependent fracture in refractory diborides

Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity

Architecture-driven deformation and fracture behavior of nanolamellar TiN/Nb coatings

Microstructure, mechanical properties, thermal decomposition and oxidation sequences of crystalline AlB2 thin films

Strategic lattice manipulation in transition metal nitrides for improved solubility

Shear-activated phase transformations of diborides via machine-learning potential molecular dynamics

Influence of co-sputtering AlB 2 to TaB 2 on stoichiometry of non-reactively sputtered boride thin films

Author Correction: Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics

Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics

Bilayer period and ratio dependent structure and mechanical properties of TiN/MoN superlattices

Mechanical properties of VC/ZrC and VC/HfC superlattices

A Strategy to Enhance the B-Solubility and Mechanical Properties of Ti–B–N Thin Films

Predicting the formation enthalpy and phase stability of (Ti,Al,TM)N (TM = III-VIB group transition metals) by high-throughput ab initio calculations and machine learning

Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations

On energetics of allotrope transformations in transition-metal diborides via plane-by-plane shearing

Large mechanical properties enhancement in ceramics through vacancy-mediated unit cell disturbance

Collaborators (13)

zhuo chen

Alexander Kirnbauer

Zaoli Zhang

Paul Mayrhofer

James Kermode

Davide Sangiovanni

Andreas Limbeck

Lars Hultman

Rainer Hahn

Martin Zelený

Shuyao Lin

Tomasz Wojcik

Lukas Zauner

Make your next move.