Track D
Computer Modelling and Simulation of Materials Properties

David BELJONNE, University of Mons, Belgium

Jean-Luc BREDAS, University of Arizona, USA
Lidong CHEN, Shanghai Institute of Ceramics, CAS, China
Shiyou CHEN, Fudan University, China
Dmitry CHIGRIN, Leibniz Institute for Interactive Materials, Germany
Jacky EVEN, University Rennes 1, France
Yuan Ping FENG, National University of Singapore, Singapore
Adam GALI, Institute for Solid State Physics and Optics, Hungary
Xavier GONZE, UC Louvain, Belgium
Anderson JANOTTI, University of Delaware, USA
Samir LOUNIS, Forschungszentrum Juelich GmbH, Germany
Yuri MISHIN, George Mason University, USA
Osvaldo NOVAIS DE OLIVEIRA Jr. , University of Sao Paulo, Brazil
Massimo OLIVUCCI, Bowling Green State University, USA
Oleg V. PREZHDO, University of Southern California, USA
Lora RAMUNNO, University of Ottawa, Canada
Carsten ROCKSTUHL, Karlsruhe Institute of Technology, Germany
Harry E. RUDA, University of Toronto, Canada
Stefano SANVITO, Trinity College Dublin, Ireland
Zhigang SHUAI, Tsinghua University, China
Douglas SOARES GALVAO, University of Campinas, Brazil
Hongming WENG, Institute of Physics, CAS, China
Dmitry CHIGRIN, Leibniz Institute for Interactive Materials, Germany
Gabriele D'AVINO, CNRS Grenoble, France
Cristiana DI VALENTIN, Università degli Studi di Milano Bicocca, Italy
Adam GALI, Wigner Research Centre for Physics, Hungary
Hong LI, The University of Arizona, USA
Jun-Wei LUO, Institute of Semiconductors, CAS, China
Fabrizia NEGRI, University of Bologna, Italy
Yoann OLIVIER, University of Namur, Belgium
Claudio QUARTI, University of Mons, Belgium
Zhigang SHUAI, The Chinese University of Hong Kong, China
Karolina SLOWIK, Nicolaus Copernicus University, Poland
Alexandre TKATCHENKO, University of Luxembourg, Luxembourg
Chris G. VAN DE WALLE, University of California, Santa Barbara, USA
Linjun WANG, Zhejiang University, China
Hongjun XIANG, Fudan University, China
Relevant for this Track are advances in development and application of sequential (hierarchical) or concurrent multiscale computing strategies and machine learning methods for predicting materials properties, with applications for opto-electronics, photonics, energy generation/storage and quantum information; experimental validation of computational studies. Relevant are studies that combine data- and physics-driven models for the identification of structure-property relationships and the predictive design of novel materials. 
Session Topics

D-1 Materials for electronics, opto-electronics and photonics (including organic and inorganic semiconductors, halide perovskites, layered materials, soft and bio materials)

D-2 Energy generation and storage (including materials for supercapacitors, photovoltaics, thermoelectrics, ferroelectrics, piezoelectrics, batteries, osmosis, fuel cells, thermal energy)

D-3 Quantum information science (including defective solids, superconducting systems, trapped ions, magnets, molecular systems, topological defects)


Cimtec 2024

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