BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//aichemy - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:aichemy
X-ORIGINAL-URL:https://aichemy.ac.uk
X-WR-CALDESC:Events for aichemy
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20250219T140000
DTEND;TZID=UTC:20250219T150000
DTSTAMP:20260413T105822
CREATED:20241122T151601Z
LAST-MODIFIED:20250603T133054Z
UID:2329-1739973600-1739977200@aichemy.ac.uk
SUMMARY:AIchemy’s Monthly Webinar Series – February 2025
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE19 February\, 2025 TIME14:00 – 15:00 COSTFree \n\n\n\nREGISTRATION CLOSED\n\n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nData-driven Materials Discovery Machine Learning Potentials: Beyond Potential Energy \n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nDr. Fadwa El Mellouhi – Data-driven Materials Discovery \n\n\n\nThe webinar focuses on the prospects of Data-driven approaches in accelerating materials discovery. It is motivated by the upsurge of machine learning (ML) applications\, big data and the adoption of computer science tools in materials science. Fadwa will give an overview of the latest advances in this field with focus on advances made in the last few years applied to energy and environmental materials. Fadwa will show how the combination of high throughput density functional theory (DFT) calculation with machine learning can be useful to perform a systematic analysis of the structure-to- property relation enabling to explore different classes of materials. Fadwa will highlight how the approach offers an interesting guideline to engineer novel materials for light absorption\, green hydrogen production and CO2 reduction while enabling to reduce the huge space of experimental trial and error. \n\n\n\nRoss Urqurhart – Machine Learning Potentials: Beyond Potential Energy \n\n\n\nMachine learning potentials (MLPs) represent a transformative approach in computational chemistry\, combining the efficiency of molecular dynamics with the precision of quantum mechanical methods like density functional theory. While traditionally employed for predicting potential energy surfaces\, MLPs have the potential to go far beyond. In this talk\, I will explore how MLPs can be adapted to predict complex properties such as pKa values and reaction pathways\, highlighting their versatility in addressing challenges beyond conventional energy predictions. \n\n\n\nFollowing the presentations\, there will be time for questions from the audience. \n\n\n\nWe look forward to having you attend the event! \n\n\n\n\n\nSpeakers\n\n\n\n\n\nFadwa El MellouhiSenior Scientist\n\n\n\n\n\nRoss UrqurhartResearch Postgraduate\n\n\n\n\n\nKeith Butler – Webinar ChairAssociate Professor in Computational Materials Chemistry
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-february-2025/
CATEGORIES:Webinar
END:VEVENT
END:VCALENDAR