BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//THOMAS YOUNG CENTRE - ECPv6.15.17//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:THOMAS YOUNG CENTRE
X-ORIGINAL-URL:https://thomasyoungcentre.org
X-WR-CALDESC:Events for THOMAS YOUNG CENTRE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20231029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20251026T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20240110T140000
DTEND;TZID=Europe/London:20240110T180000
DTSTAMP:20260513T194224
CREATED:20231214T171910Z
LAST-MODIFIED:20231221T181121Z
UID:4557-1704895200-1704909600@thomasyoungcentre.org
SUMMARY:TYC Recently Appointed Academic Talks 
DESCRIPTION:King’s Council Room\, King’s College London \n\n\n\n\n\n\n\n\n\n\n\n\nTYC Recently Appointed Academic Talks  Share on X\n\n\n\n\nSchedule \n\n\n\n14:00 Arrival: Tea\, coffee\, and sweet treats14:15 Jan Tomczak\, King’s College London: Simulating electronic structure and transport properties for correlated materials14:45 James Ewen\, Imperial College London: From silicon to silicone alternatives: towards virtual screening of hair care ingredients15:15 Break: Tea\, coffee and biscuits15:45 Ivana Savic\, King’s College London: Heat transport in strongly anharmonic materials from first principles using the Green-Kubo approach16:15 Venkat Kapil\, University College London: Machine Learning for full quantum first-principles simulations16:45 Drinks reception \n\n\n\nJan M. Tomczak – King’s College London: Simulating electronic structure and transport properties for correlated materialsOwing to strong Coulomb interactions\, electrons in correlated materials are in a collective state that is extremely sensitive to external perturbations\, resulting in rich phase-diagrams and a propensity for large response functions. This high sensitivity is a harbinger for many technological applications\, such as optoelectronic switches\, sensors\, memory storage\, and thermoelectrics. In this talk I will first highlight our efforts toward a fundamental understanding of correlated materials\, with a particular emphasis on ultra-thin oxide films [1-3]\, that could become elements in future oxide-electronics devices. \n\n\n\nElectronic correlations naturally lead to excitations with finite lifetimes. In a second part\, I will discuss lifetime-effects in transport properties of narrow-gap semiconductors. Our methodology [3\,4] and package [5] (https://github.com/LinReTraCe) is as efficient as popular codes based on Boltzmann theory in the relaxation time approximation\, but captures important corrections when lifetimes are finite. \n\n\n\nReferences:[1] M. Pickem\, J. Kaufmann\, K. Held\, JMT. Phys. Rev. B 104\, 024307 (2021)[2] M. Pickem\, J. M. Tomczak\, K. Held. Phys. Rev. Research 4\, 033253 (2022)[3] M. Pickem\, E. Maggio\, JMT. Communications Physics 4\, 226 (2021)[4] M. Pickem\, E. Maggio\, JMT. Phys. Rev. B 105\, 085139 (2022)[5] M. Pickem\, E. Maggio\, JMT. SciPost Phys. Codebases 16 (2023) \n\n\n\nJames P. Ewen – Imperial College London: From silicon to silicone alternatives: towards virtual screening of hair care ingredientsShampoos and conditioners form part of many people’s daily routine. These complex formulated products aim to cleanse and repair the hair surface to maintain a satisfactory look and feel. Huge volumes of these products are sold every year and the global hair care market is valued at close to $100B. There is currently a industry-wide drive to improve the environmental credentials (e.g. biodegradability\, biocompatibility\, and sustainability) of hair care products\, without compromising their performance. Molecular simulations are seen as an important tool with which to reduce the cost and increase the speed of R&D towards more eco-friendly products compared to laboratory-based methods and panel testing. In this talk\, I will present a coarse-grained molecular dynamics framework to study adsorption\, wettability [1]\, and friction [2] of hair care ingredients on biomimetic hair surfaces. I will present results for simple surfactants [3]\, polymers\, and polymer-surfactant complexes [4]. Ongoing work to generalise the methodology to enable virtual screening of the performance of potential new hair care ingredients and formulations will also be discussed. \n\n\n\nReferences[1] Weiand et al.\, Soft Matter\, 2022\, 18\, 1779 (https://doi.org/10.1039/d1sm01720a)[2] Weiand et al.\, Nanoscale\, 2023\, 15\, 7086 (https://doi.org/10.1039/d2nr05545g)[3] Weiand et al.\, PCCP\, 2023\, 25\, 21916 (https://doi.org/10.1039/D3CP02546B)[4] Weiand et al.\, ChemRxiv\, 2023 (https://doi.org/10.26434/chemrxiv-2023-9c6fz) \n\n\n\nIvana Savic – King’s College London: Heat transport in strongly anharmonic materials from first principles using the Green-Kubo approachOver the last 15 years\, there has been a great progress in the development of theoretical and computational tools to describe lattice thermal conductivity in realistic materials from first principles. Standard approaches are based on the phonon Boltzmann transport equation and a perturbative description of phonon-phonon interactions\, including only third order anharmonicity.  As a result\, they are appropriate only for weakly anharmonic materials. In this talk\, I will present a new method to simulate lattice thermal transport in strongly anharmonic materials\, based on the Green-Kubo formalism and a non-perturbative treatment of phonon-phonon interactions [1]. I will also present the application of this method to understand the lattice thermal conductivity of a well-known thermoelectric material\, GeTe\, near the ferroelectric phase transition. The limitations of the method will also be discussed [2]. \n\n\n\n[1] D. Dangic\, O. Hellman\, S. Fahy\, and I. Savic\, npj Comp. Mater. 7\, 57 (2021)[2] D. Dangic\, S. Fahy\, and I. Savic\, Phys. Rev. B 106\, 134113 (2022)  \n\n\n\nVenkat Kapil – University College London: Machine Learning for full quantum first-principles simulationsComputational chemistry and material science hinges on the precision and efficiency of first-principles simulations. Ideally\, these simulations should incorporate the quantum nature of all electrons and nuclei\, achieving predictive accuracy across areas from protein folding\, drug design\, and catalysis to nanoscale thermodynamics and quantum materials. Traditional “full quantum” simulations are\, however\, computationally prohibitive. This presentation introduces a modern first principles framework that significantly reduces these costs while maintaining high fidelity. Our method leverages physics-based machine learning to estimate the system’s Born-Oppenheimer potential energy surface and other essential electronic quantum effects\, such as polarization and polarisability. Demonstrating its efficacy\, we predict hitherto unfeasible first-principles phase diagrams of nanoscale systems [1] and relative stabilities of molecular crystal polymorphs [2]. \n\n\n\nFurther\, we address the challenge of modelling nuclear motion by mapping quantum dynamics to an effective classical correction akin to effective potentials by Feynman and Hibbs [3]. Our work translates quantum nuclear motion to simple classical molecular dynamics. To showcase our method’s capability\, we predict vibrational spectra of bulk and interfacial aqueous phases\, achieving quantitative agreement with experiments for the first time [4]. Our model offers a path for comprehensive quantum simulations\, combining accuracy with the ease of prevalent classical methods. \n\n\n\nReferencesV. Kapil\, C. Schran\, A. Zen\, J. Chen\, C. Pickard\, and A. Michaelides. Nature 2022\, 609\, 7927V. Kapil\, and E. Engel. Proc. Nat. Acad. Sci. 2022\, 119\, 6F. Musil\, I. Zaporozhets\, F. Noé\, C. Clementi\, and V. Kapil\, J. Chem. Phys. 2022\, 157\, 18V. Kapil\, D. Kovács\, G. Csányi\, and A. Michaelides\, Faraday Discuss.\, 2023 \n\n\n\nBio
: Venkat Kapil’s research develops machine learning-driven methods for finite-temperature first-principles materials modeling. His research aims to understand complex nanoscale systems’ thermodynamics\, transport\, and quantum mechanics. Venkat obtained his undergrad in Theoretical Chemistry from IIT Kanpur in 2015. He received a Ph.D. in Material Science from the Swiss Federal Institute of Technology Lausanne (EPFL) for developing several path-integral simulation methods to significantly reduce the cost of simulating quantum nuclear effects. He also developed and released v2.0 of the i-PI code. Venkat’s postdoctoral research\, hosted in Angelos Michaelides’ research group at the University of Cambridge\, was funded by the Swiss National Science Foundation’s “Mobility Fellowship\,” the “Early Career Oppenheimer Fellowship\,” and the “Sydney Harvey Junior Research Fellowship” by Churchill College. He developed new techniques at the intersection of machine learning and quantum statistical mechanics for full quantum first-principles simulations of materials. In January 2024\, Venkat will join University College London as a Lecturer (Assistant Professor) in the Department of Physics and Astronomy.
URL:https://thomasyoungcentre.org/event/tyc-recently-appointed-academic-talks/
CATEGORIES:Main event
ORGANIZER;CN="Martijn Zwijnenburg":MAILTO:m.zwijnenburg@ucl.ac.uk
END:VEVENT
END:VCALENDAR