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X-WR-CALDESC:Events for SRMAP Events
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TZID:Asia/Kolkata
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DTSTART:20220101T000000
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DTSTART;TZID=Asia/Kolkata:20230818T190000
DTEND;TZID=Asia/Kolkata:20230818T200000
DTSTAMP:20260421T110440
CREATED:20230818T001600Z
LAST-MODIFIED:20250806T120024Z
UID:142279-1692385200-1692388800@events.srmap.edu.in
SUMMARY:Explore A Semester at University of Portsmouth
DESCRIPTION:SRM University-AP has always proffered vital importance to semester abroad programmes providing students with remarkable opportunities to pursue part of their academics at international universities of repute. Students receive international exposure in their chosen field of study as well as developing global networks with faculty\, scholars and students from around the world. \nThe Directorate of International Relations and Higher Studies is organising a webinar titled “Exploring Spring 2024: Semester Abroad Opportunities at the University of Portsmouth” on August 18\, 2023. Ms Joanna Paraskelidis\, Global Mobility Manager\, University of Portsmouth\, UK will be the speaker for the session. The University of Portsmouth is among the 30 universities from around the world that will be delivering insightful sessions on the Semester Abroad Programmes organised in association with SRM University-AP.
URL:https://events.srmap.edu.in/event/explore-a-semester-at-university-of-portsmouth/
LOCATION:Zoom
CATEGORIES:Departmental Events,Events,IR-Events,Webinars
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DTSTART;TZID=Asia/Kolkata:20230830T150000
DTEND;TZID=Asia/Kolkata:20230830T173000
DTSTAMP:20260421T110440
CREATED:20230828T055200Z
LAST-MODIFIED:20230828T055200Z
UID:142144-1693407600-1693416600@events.srmap.edu.in
SUMMARY:Prof. Abhishek Singh on Machine Learning Techniques in Material Sciences.
DESCRIPTION:The Department of Physics is organising the ninth session of its Eminent Guest Lecture Series: An Odyssey of Physics on the topic “A Machine Learning Framework for Knowledge Discovery” on August 30\, 2023. The session will be delivered by Prof. Abhishek Singh\, Indian Institute of Science\, Bengaluru. He will discuss the application of ML in establishing the complex structure-property relations in alloys. \nJoin the talk for an insight session on Machine Learning in Material Sciences! \nAbstract \nData-driven machine learning methods in materials science are emerging as one of the promising tools for expanding the discovery domain of materials to unravel useful knowledge. In this talk\, the power of these methods will be illustrated by covering two major aspects\, namely\, the development of prediction models and the establishment of hidden connections. For the first aspect\, we have developed accurate prediction models for various computationally expensive physical properties such as band gap\, band edges and lattice thermal conductivity. The prediction model for band gap and band edges is developed on a 2D family of materials -MXene\, which are very promising for a wide range of electronic to energy applications\, which rely on accurate estimation of band gap and band edges. These models are developed with GW-level accuracy and hence can accelerate the screening of desired materials by estimating the band gaps and band edges in a matter of minutes. For the lattice thermal conductivity prediction model\, an exhaustive database of bulk materials is prepared. By employing the high-throughput approach\, several ultra-low and ultra-high lattice thermal conductivity compounds are predicted. The property map is generated from the high-throughput approach\, and four simple features directly related to the physics of lattice thermal conductivity are proposed. The performance of the model is far superior to the physics-based Slack model\, highlighting the simplicity and power of the proposed machine learning models. For the second aspect\, we have connected the otherwise independent electronic and thermal transport properties. \nThe role of bonding attributes in establishing this relationship is unravelled by machine learning. An accurate machine learning model for thermal transport properties is proposed\, where electronic transport and bonding characteristics are employed as descriptors.
URL:https://events.srmap.edu.in/event/prof-abhishek-singh-on-machine-learning-techniques-in-material-sciences/
LOCATION:Tiered Classroom\, 5th Floor\, Admin Block
CATEGORIES:Departmental Events,Events,Physics
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