Machine Learning Approach for Advanced Materials


17 Sept.			09:00 - 10:30

ROOM 17

ENERGY & ENVIRONMENT

ADVANCED MATERIALS
TT.I - Technical Parallel Track Sessions
Machine Learning Approach for Advanced Materials
Session organized by iENTRANCE Chair: Giuseppe Zollo, Sapienza University of Rome
Data-driven methods are used to address complex scientific challenges, such as designing and managing solutions to global issues related to climate change, energy production and consumption, and developing new materials. This session explores the use of machine learning in engineering and material science fields. Topics include supervised/unsupervised learning, neural networks, and AI tools. Both theoretical and experimental contributions are welcome.
*The symposium is part of iENTRANCE (FE.I) and YoungInnovation (FE.II)*
TT.I.H.1 FE.I.2.1 FE.II.1.1	Introductive Keynote Massimo CELINO - CV ENEA Data-Driven Nanoscience: Accelerating Materials Innovation through Machine Learning
TT.I.H.2 FE.I.2.2 FE.II.1.2	Leila SOHRABI-KASHANI - CV School of Metallurgy and Materials Engineering, Iran University of Science and Technology, Tehran, Iran Effect of Additives on the Microscructure and structure of alumina nanofibers as catalyst support for methane combustion: a Machine Learning approach to Additive Selection
TT.I.H.3 FE.I.2.3 FE.II.1.3	Andrea CORRADINI - CV University of Trento Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy
TT.I.H.4 FE.I.2.4 FE.II.1.4	Sara SHAHBAZI FASHTALI - CV Sapienza University of Rome From Classical Force Fields to Machine Learning Potentials: A Case Study on Graphene Oxide
TT.I.H.5 FE.I.2.5 FE.II.1.5	Ambra GUARNACCIO CNR-ISM Accelerating materials discovery by automation in organic synthesis