معرفی
Prof. Dr. Marjolein Dijkstra is a Professor of Computational Condensed Matter in the Faculty of Science at Utrecht University, affiliated with the Debye Institute for Nanomaterials Science and the Soft Condensed Matter group. Her research focuses on theory and computer simulations of soft matter systems, including colloidal suspensions, liquid crystals, and nanoparticles, with emphasis on phase transitions, nucleation, and self-assembly phenomena. She employs advanced computational techniques such as Monte Carlo, molecular dynamics, and machine learning to explore the equilibrium and non-equilibrium behavior of these systems.
Education:
MSc in Physics (Utrecht University, 1991), MSc in Molecular Sciences (Wageningen University, 1990), PhD (FOM Institute AMOLF, 1994).
Research Interests:
Her work spans the structure and dynamics of colloids, crystal prediction, jamming transitions, and the development of inverse design strategies for novel materials. Recent efforts integrate machine learning to predict complex phase behaviors and optimize interactions for targeted self-assembly.
Publication Trends:
Her 15 most recent articles (2024–2025) highlight innovations in machine learning potentials, inverse design of photonic materials, colloidal crystallization mechanisms, and active matter dynamics. Cross-disciplinary themes include robotics applications, liquid crystal topology, and nanocrystal engineering.
Awards and Honors:
- Physica Prize (2025)
- ERC Advanced Grant (2020)
- Elected Member of the Royal Netherlands Academy of Arts and Sciences (KNAW, 2020)
- NWO-VICI Grant (2006)
- NWO-Aspasia Premie (2007)
- UU High Potential Grant (2004)
- FOM Minerva Prize (2000)
Academic Leadership:
She leads the Computer Simulation group, advising PhD and master’s students on projects involving soft matter simulations. She has secured significant grants (e.g., ERC Advanced, NWO-VICI) and serves on editorial boards (e.g., Reviews of Modern Physics). Her lab collaborates extensively with experimental groups to validate computational predictions.
