معرفی
Dr. Ivor Lončarić is a Senior Research Associate at the Ruđer Bošković Institute in Zagreb, Croatia, working within the Division of Theoretical Physics, specifically in the Laboratory of Condensed Matter and Statistical Physics. His research spans multiple areas of condensed matter physics, materials science, and computational chemistry, with a particular focus on molecular dynamics, surface science, and machine learning applications in materials science.
Dr. Lončarić's research interests primarily focus on condensed matter physics and statistical physics, with specific expertise in nanoporous graphene, perovskite materials, thermosalient phase transitions, and crystal structure prediction. His work often combines experimental approaches with computational modeling, particularly utilizing machine learning potentials to study complex molecular systems and surface phenomena. He has made significant contributions to understanding molecular dynamics on metal surfaces, light-induced processes in coordination polymers, and the structural properties of various crystalline materials.
His publication record demonstrates a strong trend toward integrating machine learning with traditional computational methods to tackle complex problems in materials science. Recent work shows increasing focus on perovskite solar cell materials, thermosalient crystals that exhibit jumping behavior during phase transitions, and surface science phenomena. His research often bridges fundamental physics with practical applications in energy materials and nanotechnology.
Dr. Lončarić has been actively involved in major collaborative research efforts, including the CCDC seventh blind test of crystal structure prediction. His work appears in leading journals across physics, chemistry, and materials science disciplines, demonstrating the interdisciplinary nature of his research.
He regularly presents his research at international conferences on surface science, condensed matter physics, and materials science, with recent presentations focusing on machine learning applications in materials research and molecular dynamics simulations. His laboratory work involves both computational modeling and experimental characterization of materials, particularly focusing on surface phenomena and phase transitions in crystalline systems.
