BEGIN:VCALENDAR PRODID:-//SiteBuilder 2//ÉñÂí¸£ÀûӰƬ ITS Web Team//EN VERSION:2.0 CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Predictive Modelling » Seminars X-WR-TIMEZONE:Europe/London X-LIC-LOCATION:Europe/London BEGIN:VTIMEZONE TZID:Europe/London LAST-MODIFIED:20201010T011803Z TZURL:http://tzurl.org/zoneinfo/Europe/London X-LIC-LOCATION:Europe/London X-PROLEPTIC-TZNAME:LMT BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+000115 TZOFFSETTO:+0000 DTSTART:18471201T000000 END:STANDARD BEGIN:DAYLIGHT TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 DTSTART:19160521T020000 RDATE:19170408T020000 RDATE:19180324T020000 RDATE:19190330T020000 RDATE:19200328T020000 RDATE:19210403T020000 RDATE:19220326T020000 RDATE:19230422T020000 RDATE:19240413T020000 RDATE:19270410T020000 RDATE:19300413T020000 RDATE:19330409T020000 RDATE:19340422T020000 RDATE:19350414T020000 RDATE:19380410T020000 RDATE:19390416T020000 RDATE:19400225T020000 RDATE:19460414T020000 RDATE:19470316T020000 RDATE:19480314T020000 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,23,24,25;BYDAY=SU END:DAYLIGHT BEGIN:STANDARD TZNAME:BST TZOFFSETFROM:+0100 TZOFFSETTO:+0100 DTSTART:19681026T230000 END:STANDARD BEGIN:DAYLIGHT TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 DTSTART:19720319T020000 RRULE:FREQ=YEARLY;UNTIL=19800316T020000Z;BYMONTH=3;BYMONTHDAY=16,17,18,19 ,20,21,22;BYDAY=SU END:DAYLIGHT BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 DTSTART:19721029T030000 RRULE:FREQ=YEARLY;UNTIL=19801026T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2 6,27,28,29;BYDAY=SU END:STANDARD BEGIN:DAYLIGHT TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 DTSTART:19810329T010000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 DTSTART:19811025T020000 RRULE:FREQ=YEARLY;UNTIL=19891029T010000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2 6,27,28,29;BYDAY=SU END:STANDARD BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 DTSTART:19901028T020000 RRULE:FREQ=YEARLY;UNTIL=19951022T010000Z;BYMONTH=10;BYDAY=4SU END:STANDARD BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+0000 TZOFFSETTO:+0000 DTSTART:19960101T000000 END:STANDARD BEGIN:STANDARD TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 DTSTART:19961027T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260608T093958Z DTSTART;VALUE=DATE-TIME:20260615T130000 DTEND;VALUE=DATE-TIME:20260615T140000 SUMMARY:WCPM\, Fraser Birks TZID:Europe/London UID:20260615-8ac672c49da8d90f019daa204e20016d@warwick.ac.uk CREATED:20260604T083509Z DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: Und erstanding Plasticity in Amorphous Carbon with Machine-Learned Interatom ic Potentials Abstract: Amorphous carbon (a-C) is a coating material wit h many applications\, commonly selected for its high hardness\, low fric tion coefficient and high wear resistance. Generally\, amorphous carbon is considered a brittle material\, with cracks propagating at relatively low tensile stresses due to the presence of structural defects. However \, when defect-free thin films are studied\, experiments have shown that a-C can exhibit an unusual combination of high stiffness (~210 GPa) and unexpectedly large failure strain (~11%) [1]. At present\, it is still unclear what structures and mechanisms in these films give rise to such anomalous mechanical behaviour\, making this fertile ground for atomisti c studies using realistic machine-learned interatomic potentials (MLIPs) . In this talk\, I will first give an overview of the history of atomist ic modelling\, introducing MLIPs and discussing their strengths and weak nesses. I will then present the results from two studies [2\, 3] which c ombine MLIPs with molecular dynamics and arclength continuation to eluci date the underlying mechanisms governing plasticity in a-C. I will furth er show that\, with careful structure preparation\, it is possible to re produce the experimental stress-strain response\, opening the door to di rect collaboration between theorists and experimentalists on this import ant class of materials. [1] Yoon\, J.\, Jang\, Y.\, Kim\, K.\, Kim\, J.\ , Son\, S.\, & Lee\, Z. (2022). In situ tensile and fracture behavior of monolithic ultra-thin amorphous carbon in TEM. Carbon\, 196\, 236–242. https://doi.org/10.1016/j.carbon.2022.04.062 [2] Birks\, F.\, Ghanem\, I .\, Pastewka\, L.\, Kermode\, J.\, & Buze\, M. (2026). Resolving structu ral avalanches in amorphous carbon with arclength continuation. Physical Review Letters. https://doi.org/10.1103/6n5m-rxc1 [3] Birks\, F.\, & Ke rmode\, J. (2026). [Manuscript in preparation]. Bio: Fraser is a PhD res earcher in Computational Materials Science at the ÉñÂí¸£ÀûӰƬ and a member of the Warwick Centre for Predictive Modelling\, where his work focuses on machine-learned interatomic potentials (MLIPs)\, atomist ic simulations\, and the mechanics of materials failure. Before joining Warwick\, Fraser studied Natural Sciences at the University of Cambridge \, graduating with First Class honours and receiving multiple academic p rizes\, including the Wheatley Prize and the Part IB Physics Practical P rize. His research combines computational physics\, applied mathematics\ , and machine learning to better understand how materials behave at the atomic scale. Fraser has contributed to open-source scientific software development\, published research in leading journals including Physical Review Letters\, and presented award-winning work on machine learning fo r atomistic simulations at international conferences and national compet itions such as STEM for BRITAIN\, where he received a silver medal in Ph ysics. Alongside his research\, Fraser is passionate about science commu nication and outreach\, regularly speaking at public events\, student co nferences\, and engagement activities exploring the role of AI and machi ne learning in modern physics and materials science. LOCATION: CATEGORIES:WCPM LAST-MODIFIED:20260604T083509Z ORGANIZER;CN=Jin Kang: END:VEVENT BEGIN:VEVENT DTSTAMP:20260608T093958Z DTSTART;VALUE=DATE-TIME:20260629T130000 DTEND;VALUE=DATE-TIME:20260629T140000 SUMMARY:WCPM\, Loïc Lannelongue\, Cambridge TZID:Europe/London UID:20260629-8ac672c49da8d90f019daa254b71025d@warwick.ac.uk CREATED:20260422T085417Z DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: The (environmental) sustainability challenge of modern computing & AI Abstr act: From genetic studies and astrophysics simulations to AI\, scientifi c computing has enabled amazing discoveries—and there's no doubt it will continue to do so. At the same time\, the resource usage (energy\, wate r) and environmental impacts of digital (research) infrastructures are b ecoming impossible to ignore given the urgency of the climate crisis. So what can we all do about it? And as scientists\, should we even be thin king about this? We'll break down how computing activities impact the en vironment\, debate our collective responsibility to tackle it\, and disc uss the latest efforts of the Cambridge Sustainable Computing Lab to emp ower researchers to understand and mitigate their environmental impacts. Through the lens of the GREENER principles for environmentally sustaina ble science\, we'll explore the challenges the research community needs to overcome to create real change in this space. It will also be a chanc e to highlight how the Green DiSC certification framework can support sc ientists and institutions in making their research more sustainable. Bio : Dr Loïc Lannelongue is an Assistant Research Professor in Computer Sci ence at the University of Cambridge\, where he also serves as Bye-Fellow and Director of Studies in Computer Science (Part II) at Jesus College Cambridge. His work sits at the intersection of computing\, sustainabili ty\, and responsible innovation. Dr Lannelongue specialises in environme ntally sustainable computing\, with a particular focus on understanding and reducing the environmental impact of modern computational practices\ , including artificial intelligence. His research takes a multi-faceted approach\, combining technical development\, behavioural insights\, and policy engagement to drive more sustainable scientific workflows. His ac ademic interests include developing tools to monitor and reduce the carb on footprint of scientific computing\, contributing to sustainability fr ameworks and policy\, and exploring the ethical implications of modern s cience and AI. In parallel\, he works in radiogenomics\, applying machin e learning to integrate genomics and medical imaging data to improve und erstanding of cardiovascular disease. Through his research and teaching\ , Dr Lannelongue is committed to advancing a more sustainable and respon sible future for computational science. Webpage: https://www.jesus.cam.a c.uk/people/loic-lannelongue LOCATION: CATEGORIES:WCPM LAST-MODIFIED:20260422T085417Z ORGANIZER;CN=Jin Kang: END:VEVENT BEGIN:VEVENT DTSTAMP:20260608T093958Z DTSTART;VALUE=DATE-TIME:20260608T130000 DTEND;VALUE=DATE-TIME:20260608T140000 SUMMARY:WCPM\, Kevin Huang\, Warwick TZID:Europe/London UID:20260608-8ac672c59d8bfea7019daa1cd0024e12@warwick.ac.uk CREATED:20260521T094626Z DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: Dia gonal Symmetrization of Neural Network Solvers for the Many-Electron Sch rödinger Equation Abstract: Incorporating group symmetries into neural n etworks has been a cornerstone of success in many AI-for-science applica tions. Diagonal groups of isometries\, which describe the invariance und er a simultaneous movement of multiple objects\, arise naturally in many -body quantum problems. Despite their importance\, diagonal groups have received relatively little attention\, as they lack a natural choice of invariant maps except in special cases. We study different ways of incor porating diagonal invariance in neural network ansätze trained via varia tional Monte Carlo methods\, and consider specifically data augmentation \, group averaging and canonicalization. We show that\, contrary to stan dard ML setups\, in-training symmetrization destabilizes training and ca n lead to worse performance. Our theoretical and numerical results indic ate that this unexpected behavior may arise from a unique computational- statistical tradeoff not found in standard ML analyses of symmetrization . Meanwhile\, we demonstrate that post hoc averaging is less sensitive t o such tradeoffs and emerges as a simple\, flexible and effective method for improving neural network solvers. Bio: Kevin is a postdoctoral rese arch fellow funded by the Engineering and Physical Sciences Research Cou ncil (EPSRC) through the ProbAI Hub. They are currently based at the Uni versity of Warwick\, working with Gareth Roberts\, and collaborate with Boris Hanin at Princeton University. They completed a PhD in machine lea rning at the Gatsby Computational Neuroscience Unit\, University College London\, under the supervision of Peter Orbanz and Morgane Austern. Dur ing this time\, they were also a visiting researcher with the LIPS group at Princeton Computer Science\, hosted by Ryan P. Adams. Prior to this\ , they completed both their undergraduate and master’s degrees in mathem atics at the University of Cambridge. Their research lies at the interse ction of machine learning theory\, probability\, and statistics. They st udy the emergence of universal structures in large-scale stochastic syst ems\, drawing on tools from random matrix theory\, high-dimensional stat istics\, symmetry-based inference\, and stochastic optimisation. Alongsi de this theoretical work\, they increasingly engage with applied challen ges\, particularly around scaling laws in neural networks\, AI for scien tific discovery\, and the robustness and safety of machine learning mode ls. For the 2025–2026 academic year\, he is co-organising the ProbAI onl ine seminar series and will lead the ProbAI Theory of Scaling Laws Works hop at Warwick in summer 2026. LOCATION: CATEGORIES:WCPM LAST-MODIFIED:20260521T094626Z ORGANIZER;CN=Jin Kang: END:VEVENT BEGIN:VEVENT DTSTAMP:20260608T093958Z DTSTART;VALUE=DATE-TIME:20260601T130000 DTEND;VALUE=DATE-TIME:20260601T140000 SUMMARY:WCPM\, Thomasina Ball\, Warwick TZID:Europe/London UID:20260601-8ac672c69da8d8e7019daa186609062e@warwick.ac.uk CREATED:20260505T112726Z DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: Mod elling mountain building: Wrinkles and creases in thin layers of viscopl astic fluid Abstract: Wrinkles or creases in the surface of a material a re indicative of compression. For example\, on Earth\, mountain ranges f ormed due to the plate tectonics exhibit regular spaced folds on the sur face. In this talk I will discuss some of the theoretical and experiment al approaches we have taken to describe the process of mountain building by modelling it as a viscoplastic fluid (a material with solid and flui d-like properties). Bio: Research Interests: Thomasina's research intere sts lie in mathematical modelling of fluid dynamical phenomena from obse rvations of laboratory experiments and the natural world around us. In p articular she is interested in the areas of: non-Newtonian rheologies\, yield stress fluids\, gravity-driven flow\, geophysical flows\, instabil ities that arise from rheology contrasts\, fluid-structure interactions. Most relevant recent publications: Ball\, T. V. & Balmforth\, N. J. (20 25) Non-axisymmetric patterns in floating viscoplastic films. J. Fluid M ech. 1007. Ribinskas\, E.\, Ball\, T. V.\, Penney\, C. E.\, & Neufeld\, J. A. (2024) Scraping of a viscoplastic fluid to model mountain building . J. Fluid Mech. See her Publications page for a full list with preprint s: /fac/sci/maths/people/staff/tball/ LOCATION:L5\, Science Concourse CATEGORIES:WCPM LAST-MODIFIED:20260505T112726Z ORGANIZER;CN=Jin Kang: END:VEVENT BEGIN:VEVENT DTSTAMP:20260608T093958Z DTSTART;VALUE=DATE-TIME:20260622T130000 DTEND;VALUE=DATE-TIME:20260622T140000 SUMMARY:WCPM\, Ludovic Berthier\, ESPCI TZID:Europe/London UID:20260622-8ac672c79d8bfbdd019daa22fb6c2c96@warwick.ac.uk CREATED:20260601T090345Z DESCRIPTION:Networking Lunch: Outside L5\, from 12:30pm - 1pm. Title: Fas t equilibration of glassy systems: Where do we stand? Abstract: Monte Ca rlo simulations are widely employed to measure the physical properties o f glass-forming liquids in thermal equilibrium\, thus offering an effici ent alternative to molecular dynamics studies of the glass transition. I n both approaches however\, ensuring ergodicity and proper equilibrium s ampling is a difficult challenge. I will provide a brief overview of Mon te Carlo studies of glass-formers to illustrate the difficulty of the sa mpling task. I will then define and implement a series of enhanced Monte Carlo algorithms which display a much faster approach to the desired er godic sampling of the configuration space for this family of complex sys tems. I will also discuss how generative models relying on machine learn ing models are starting to be developed to solve the same problem. Bio: Ludovic Berthier is a Directeur de Recherche at the CNRS\, based at the Laboratoire Gulliver at ESPCI Paris. He is an internationally recognised leader in statistical physics\, specialising in the theory and simulati on of complex\, disordered systems. His research spans a wide range of t opics at the intersection of physics and materials science\, including n on-equilibrium statistical mechanics\, soft matter and complex fluids\, and the physics of supercooled liquids and glasses. He has made particul arly influential contributions to understanding the glass transition\, a morphous solids\, and jamming phenomena\, as well as emerging areas such as active and biological matter. Ludovic’s work combines theoretical in sight with advanced computational methods to uncover universal behaviour s in high-dimensional and disordered systems. He has authored numerous h igh-impact publications in leading journals such as Nature Materials\, P hysical Review Letters\, Physical Review X\, and PNAS\, and has contribu ted to major review articles shaping the field\, including on yielding i n amorphous solids and machine learning approaches to glassy systems. Th rough his research\, he continues to push the boundaries of how we under stand and design complex materials\, both in and out of equilibrium. Fin d out more here: https://ludovicberthier.github.io/ LOCATION: CATEGORIES:WCPM LAST-MODIFIED:20260601T090345Z ORGANIZER;CN=Jin Kang: END:VEVENT END:VCALENDAR