Current trends in computational materials science: From low-dimensional systems to high-throughput materials discovery
Abstract
The special sessions on Current Trends in Computational Materials Science: From Low-Dimensional Systems to High-Throughput Materials Discovery aims to bring together leading computational scientists across Asia in partnership with the Asian Consortium on Computational Materials Science (ACCMS). These sessions will serve as a dynamic platform for fostering collaboration, exchanging ideas, and strengthening regional research networks in a field that continues to expand rapidly in both scope and impact. By convening experts from diverse sub-disciplines, the symposium seeks to highlight the role of computational approaches in addressing contemporary challenges in materials science.
In particular, the speakers in these sessions will showcase emerging and high-impact research directions that are shaping the future of the discipline. Topics of interest include the study of low-dimensional systems, advances in the integration of machine learning with density functional theory (DFT), and the development of high-throughput frameworks for accelerated materials discovery. This special session will bring fresh perspectives, discover innovative methodologies, and explore opportunities for interdisciplinary
collaboration, ultimately contributing to the advancement of next-generation materials design and discovery.
The ACCMS was founded in August 2000 by Prof. Yoshiyuki Kawazoe with the support of leading scientists in Asia and in the USA. Fast forward to the present, it is now a leading organization in Asia that promotes the advancement of computational science in general, and computational materials science in Asian countries. Some of its notable honorable members are pioneer scientists in the field, among them are Prof. Michele Parrinello of ETH Zürich (one of the founders of the Car-Parrinello Molecular Dynamics) and Prof. Kimihiko Hirao of RIKEN (who developed Multireference Moller-Plesset Theory).
