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<!DOCTYPE html>
<html lang='en'>
<head>
<base href=".">
<link rel="icon" href="assets/ML4QM_logo.svg">
<link rel="stylesheet" type="text/css" media="all" href="assets/main.css"/>
<meta name="description" content="Conference Template">
<meta name="resource-type" content="document">
<meta name="distribution" content="global">
<meta name="KeyWords" content="Conference">
<title>Conference Template</title>
</head>
<body>
<div class="banner">
<div class="banner-big-text">
<span class="title1">ML</span>
<span class="title2">for</span>
<span class="title1">Quantum Matter</span>
</div>
<img src="assets/banner.jpg" alt="Sunny White City">
<div class="banner-small-text">
Friday 3rd May 2024 <br>
Imperial College London, White City, UK
</div>
</div>
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<a class="current" title="Conference Home Page" href=".">Home</a>
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<a title="Register for the Conference" href="https://estore.imperial.ac.uk/conferences-and-events/faculty-of-natural-sciences/chemistry/workshop/ml4qm">Registration (closed)</a>
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<h2>Machine Learning for Quantum Matter</h2>
<p>
The Machine Learning for Quantum Matter workshops are intended to help
a build a research community in the area of applying machine learning to accelerating areas of quantum chemistry and computational physics.
We are a community of research groups interested in research in this area, organising events at cost price.
We are particularly interested in nurturing the early career researchers in this area: please do not feel put off by the sometimes high falutin nature of the research talks - you are very welcome here!
</p>
<p>Our next event will be on Friday 3rd May 2024 at Imperial College London.</p>
<p>Lectures will run from 10AM to 5PM. Talks will be 30 minutes, with 10 minutes for Q&A and AV faff.</p>
Our provisional timetable is:
<ul>
<li> <strong>10:00 Ilyses Batatia</strong> <em>(University of Cambridge)</em>
<br> Foundation models for materials and molecular chemistry </li>
<li> <strong>10:40 Gino Cassella</strong> <em>(Imperial College London)</em>
<br> Freedom from basis sets with Neural Network Variational Monte Carlo </li>
<li> <strong> 11:20 Twenty minute break.</strong></li>
<li> <strong>11:40 Livia Bartok-Partay</strong> <em>(Warwick)</em>
<br> Computational thermodynamics: What can the unbiased sampling of the configuration space tell us? </li>
<li> <strong>12:20 Evan Sheridan</strong> <em><a href="https://www.phasecraft.io/">(Phasecraft)</a></em>
<br> Variational quantum simulation of many body problems </li>
<li> <strong> 13:00 One hour lunch break.</strong></li>
<li> <strong>14:00 Emma King-Smith</strong> <em>(University of Cambridge)</em>
<br> Practical Machine Learning for Organic Small Molecule Modelling </li>
<li> <strong>14:40 Tong Wan 'Tonny' Lou</strong> <em>(Imperial College London)</em>
<br> Neural Wave Functions for Superfluids </li>
<li> <strong>15:40 Harry Moore</strong> <em>(University of Cambridge)</em>
<br> Transferrable machine learned forcefields for biomolecular simulation </li>
<li> <strong> 15:20 Twenty minute break.</strong></li>
<li> <strong>16:20 Zsuzsanna Koczor-Benda</strong> <em>(Warwick)</em>
<br> Generative inverse design of functional molecules for plasmonic nanodevices </li>
</ul>
<h2>Organisers</h2>
<p>
The Machine Learning for Quantum Matter workshops are organised by Roberto Bondesan, Jarvist Moore Frost, Mohammed Azzouzi, Alexander Ganose and Keith Butler.
</p>
<p>The workshops are supported by Imperial’s Department of Chemistry, and Imperial I-X.</p>
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| Design by <a href="https://github.com/mikepierce">Mike Pierce</a>
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