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DTSTAMP:20260410T213859
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SUMMARY:TYC Welcome Day 2024
DESCRIPTION:TYC Welcome Day 2024 Share on X\n\n\n\n\nWe encourage you to attend our in-person TYC Welcome Event which is the perfect opportunity to begin networking with your peers\, and to hear about the fantastic benefits of being affiliated to this active and exciting institute.  Our Interest Group Leads will talk to you about the hot topics they are working on\, and a panel of TYC students and postdocs will be on hand to answer your questions\, providing an overview of TYC activities and opportunities. \n\n\n\nLunch will be provided. \n\n\n\nTell us your PhD topic\, plus one (or more) burning question/s you have for the current TYC PhD students and postdocs\, to enable the panel to cover topics which are relevant to you. Ask them anything – from student life in London and at the TYC\, to what it’s like to undertake a PhD.  Questions will be answered anonymously. \n\n\n\nWe’ll need your confirmation by email to register you\, and send out details.  Don’t forget to include your question/s! Email Karen at tyc-administrator@ucl.ac.uk \n\n\n\n11:05 Introduction to the TYC – Jochen Blumberger \n\n\n\nInterest Group spokespeople present TYC Interest Groups\, and their ‘hot topics’11:15 – Edina Rosta\, UCL Physics / Alessandro Pandini\, Brunel – Soft and Biological Matter (biochemistry\, biophysics\, biomaterials\, statistical mechanics)11:25 – Devis Di Tommaso\, QMUL Chemistry – Structural materials (dislocations\, rheology\, chemimechanics\, tribology)11:35 – Martijn Zwijnenburg\, UCL Chemistry – Functional Materials & Devices (Light-Matter interactions\, spectroscopy\, excited states\, photonics\, plasmonics\, solar energy conversion\, electronic\, thermal and ionic transport)11:45 – Clotilde Cucinotta\, Imperial Chemistry – Surfaces & Interfaces (catalysis\, electrochemistry\, nanostructures)11:55 – Jan Tomczak\, King’s Physics – Methods and Formalisms for simulating materials12:05 – Venkat Kapil\, UCL Physics – Artificial Intelligence and Machine Learning \n\n\n\n12:15 – Student Q&A panel \n\n\n\n13:00 – Lunch social \n\n\n\n14:00 – End
URL:https://thomasyoungcentre.org/event/tyc-welcome-day-2024/
LOCATION:Nyholm Room\, Christopher Ingold Building\, Gordon Street\, London
CATEGORIES:Main event
END:VEVENT
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DTSTART;TZID=Europe/London:20241014T133000
DTEND;TZID=Europe/London:20241015T140000
DTSTAMP:20260410T213859
CREATED:20240607T144023Z
LAST-MODIFIED:20241002T163714Z
UID:5367-1728912600-1729000800@thomasyoungcentre.org
SUMMARY:Advances in modelling defects and interfaces workshop
DESCRIPTION:Institute of Physics (IOP)\, London \n\n\n\n\n\n\n\n\n\n\nAdvances in modelling defects and interfaces workshop Share on X (formerly Twitter)\n\n\n\n\nThe Thomas Young Centre takes great pleasure to announce the workshop “Advances in modelling defects in solids and interfaces”\, being organised to honour the achievements of Professor Alexander Shluger in modelling defects and interfaces in solids and nanosystems and contributions to computational materials science. \n\n\n\nAlex has been part of the UK Computational Materials Science community for more than 30 years\, first working at the Royal Institution\, and since 1996 at University College London\, where he has been a Head of the Condensed Matter and Materials Physics group and co-director of the Thomas Young Centre. \n\n\n\nHe has made important contributions to the theoretical modelling of defects in the bulk and at surfaces and interfaces of insulators.  He also contributed to developing models explaining mechanisms of imaging and manipulation of surface atoms and molecules using atomic force microscopy. His achievements were recognised by the Institute of Physics (IoP) by the award of the David Tabor Medal and Prize in 2020. \n\n\n\nAlex continues to maintain a very active research group at UCL: https://www.ucl.ac.uk/condensed-matter-material-physics/alexander-shluger-group \n\n\n\nMonday 14 October \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTuesday 15 October \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n10 invited speakers will deliver research talks at the forefront of computational materials science\, who are long-term collaborators or former students and postdocs of Alex: \n\n\n\nEnergy of electron traps in insulating oxides – Valeri Afanas’ev\, KU Leuven\, BelgiumFor more than 60 years insulating oxides were the key enables of integrated semiconductor electronics evolving from the “classical” SiO2 thermally grown on Si to the nowadays successors like high-k (Al2O3\, HfO2\, ZrO2\,…) and low-k (porous SiOCN matrices) layers. Not surprisingly\, reliability of these insulators became the core issue in securing sufficient lifetime of the functional devices. Charge trapping\, development of leakage current and\, as the ultimate failure\, dielectric breakdown are seen as the consequence of injected electronic charges prompting a deeper understanding of electron-network interactions. Though in the case of SiO2 the decennia of research by electron spin resonance (ESR) revealed significant impact of dangling bond (DB) defects (Pb-\, E’-type centers)\, it appears not to be the case in high-k metal oxides urging to re-consider the DB paradigm as whole. In these systems\, the ESR analysis was mostly successful in revealing the impurity-related trapping sites but not the intrinsic ones. In these circumstances\, the spectroscopic information about critically important imperfections of the oxide matrix should be delivered by alternative methods. The most straightforward approach addresses the core effect – charges generated due to electron and hole trapping. Since microscopic parameters of the trapping process (capture cross section\, trapping rate\, etc.) do not deliver information about atomic arrangements responsible for the trapping\, one might consider analysis of the “final result”\, i.e.\, the trapped electron (hole) state\, to “recognize the enemy”. It will be shown that\, unlike in the case of SiO2\, the trapped electron sites in high-k metal oxides can be assessed by optical excitation (de-population\, i.e.\, the trapped charge removal) which opens the way to their spectroscopic identification through trapped electrons energy distribution. Several typical energies of the trapped electrons can be identified within the bandgap of insulating oxides. With this information as the solid reference\, theoretical modelling of the defects emerges an unmissable tool of defect identification. As the oxygen deficiency models appear to fail in explaining the electron trapping evolution as affected by technological processing\, one is prompted to search for alternative culprits for electron trapping. The simulations reveal that the experimentally observed deep (2-3 eV) electron trapping can be explained by self-localization of electrons in polaronic states related to disorder of the amorphous oxide network. Furthermore\, comparison of optical and thermal energies of electron escape from the trap allowed us to estimate the relaxation energy which also appears to be consistent with the polaronic hypothesis. These results show that\, even in the absence of atomic information delivered by ESR\, the trapping sites can be identified on the basis of their energy spectrum using modelling of the trapping sites. \n\n\n\nNew insights into defect and electronic properties of oxides and nitrides – Richard Catlow; Department of Chemistry\, University College London \n\n\n\nWe will discuss recent work on the defect and electronic structure of technological important oxide and nitride materials. We will highlight the role of QM/MM methods in modelling these materials and will show how these methods can be integrated with other computational approaches. Recent work on the following materials will be reviewed: \n\n\n\n\nCeO2 where we will show how by applying QM/MM techniques in conjunction with Mott-Littleton and periodic methods\, we have developed a consistent set of models for the defect and electronic structure of the material.\n\n\n\nAlN and GaN\, where we characterise the basic defect structure and in the latter case discuss dopant-defect complexes leading to p-type conductivity\n\n\n\nZnO where we discuss both the intrinsic defect structure and that of Li and Cu doped systems.\n\n\n\n\nWhole focusing on these materials\, we will aim to give a broader perspective on modelling defects in insulators and semiconductors. \n\n\n\nRemoving the defect in Scanning Probe Microscopy – Adam Foster\, Aalto University\, FinlandScanning Probe Microscopy (SPM) has been the engine of characterization in nanoscale systems in general\, and the evolution of functionalized tips as a reliable tool for high-resolution imaging without material restrictions has been a breakthrough in studies of molecular systems [1]. In parallel\, machine learning (ML) methods are increasingly being applied to data challenges in SPM. In particular\, the success of deep learning in image recognition tasks has led to their application to the analysis of SPM images\, especially in the context of surface feature characterisation and techniques for autonomously-driven SPM [2]. \n\n\n\nIn this work\, we explore the general potential for using ML approaches to aid in the analysis of Atomic Force Microscopy (AFM) and Scanning Tunnelling Microscopy (STM) images\, along with the possibilities of introducing ML automation into experimental workflows. As an example\, we build upon a deep learning infrastructure that matches a set of AFM/STM images with a unique descriptor characterizing the molecular configuration [3]\, and then develop a workflow that takes experimental images of complex molecular systems and revises initial ML structure predictions with neural network potential simulations [4]. In this context\, we discuss the challenges of handling experimental data and possible data augmentation strategies. Beyond this\, we show how ML approaches can be used actively during SPM experiments for construction of nanostructures through atomic manipulation [5]\, while also highlighting approaches towards automated construction of more complex molecular systems atom-by-atom and bond-by-bond. \n\n\n\nReferences:[1] N. Pavliček and Leo Gross\, Nature Reviews Chemistry 1\, 1–11 (2017)[2] O.M. Gordon and P.J. Moriarty\, Mach. Learn.: Sci. Technol. 1 (2020) 023001; Sergei V. Kalinin et al\, MRS Bulletin (2022) s43577-022-00413-3[3] B. Alldritt\, P. Hapala\, N. Oinonen\, F. Urtev\, O. Krejci\, F. F. Canova\, J. Kannala\, F. Schulz\, P. Liljeroth\, and A. S. Foster\, Sci. Adv. 6 (2020) eaay6913; Lauri Kurki\, Niko Oinonen and Adam S. Foster\, ACS Nano (2024) acsnano.3c12654[4] F. Priante\, N. Oinonen\, Y. Tian\, D. Guan\, C. Xu\, S. Cai\, P. Liljeroth\, Y. Jiang\, and A. S. Foster\, ACS Nano (2024) acsnano.3c10958[5] I-Ju Chen\, Markus Aapro\, Abraham Kipnis\, Alexander Ilin\, Peter Liljeroth and Adam S. Foster\, Nat. Commun. 13 (2022) 7499 \n\n\n\nDefects: How many is too many? – Jacob Gavartin\, Schroedinger Inc.Solid state defects are typically defined as a 1\,2\, or 3-dimensional violation of an otherwise “perfect” crystal atomic order. It is a powerful concept which has been tremendously successful in the description of numerous properties of real materials. The progress in theory of defects has been significant\, not least due to the pioneering contributions of UCL academicians such as professors Marshall Stoneham\, Richard Catlow and Alex Shluger to name a few. The success in this field has led to a change of current theoretical aspirations from descriptive models to quantitative predictions of defect related properties. In this contribution I shall discuss a few examples where a canonical notion of a defect as an isolated species breaks down due to the defects’ interactions\, so that defect aggregated properties differ significantly from those of isolated defects. The discussed phenomena include: \n\n\n\n\nCharged defects and charge compensation mechanisms\n\n\n\nDefects concentration prediction\n\n\n\nDefects agglomeration and segregation effects and phase transitions\n\n\n\nDefects kinetics versus thermodynamics\n\n\n\n\nBased on these examples I shall address successes and challenges of the reliable predictions by atomistic modelling.  \n\n\n\nSpatially resolved trap states and random telegraph noise in semiconductors – Peter Grutter\, McGill University\, CanadaSemiconductor interfaces often have isolated trap states which modify electronic properties. Here\, we study the electric susceptibility of the Si/SiO2 interface with nm spatial resolution using frequency-modulated atomic force microscopy. We show that surface charge organization timescales\, which range from 1−150 ns\, increase significantly around interfacial states [1]. We conclude that dielectric loss under time-varying gate biases at MHz and sub-MHz frequencies in metal-insulator-semiconductor capacitor device architectures is highly spatially heterogeneous over nm length scales [1]. \n\n\n\nIn frequency-modulated atomic force microscopy the measured frequency shift is quadratic in applied bias for metallic samples and probes. However\, for semiconducting samples\, band bending effects must be considered\, resulting in non-parabolic bias curves. We have developed a framework to quantitatively describe a metal-insulator semiconductor (MIS) device formed out of a metallic AFM tip\, vacuum gap\, and semiconducting sample. We show how this framework allows us to measure dopant concentration\, bandgap and band bending timescales of different types of defects on semiconductors with nm scale resolution on Si\, 2D MoSe2 and pentacene monolayers [2]. \n\n\n\nWe also measure temporal two-state fluctuations of individual defects at the Si/SiO2 interface with nanometer spatial resolution using frequency-modulated atomic force microscopy with single electron sensitivity. We demonstrate that two-state fluctuations are localized at interfacial traps\, with bias-dependent rates and amplitudes. When measured as an ensemble\, the observed defects have a 1/f power spectral trend at low frequencies [3]. \n\n\n\nLow-frequency noise due to two level fluctuations inhibits the reliability and performance of nanoscale semiconductor devices\, and challenges the scaling of emerging spin based quantum sensors and computers. The presented method and insights provide a more detailed understanding of the origins of 1/f noise in silicon-based classical and quantum devices\, and could be used to develop processing techniques to reduce two-state fluctuations associated with defects. \n\n\n\nUnderstanding and engineering defects in silicon oxide for non-volatile memories – Tony Kenyon\, University College London\, UKSilicon oxide\, for many years regarded as a stable dielectric and widely deployed in CMOS electronics\, can\, by careful defect engineering\, be transformed into an excellent material for resistance switching. In this talk I will discuss its application in resistive RAM and memristive devices\, highlighting how important it is to combine atomistic modelling with experimental work to understand and engineer the contribution of bulk and interface defects. \n\n\n\nDoping effects in nanoperovskites for hydrogen production – Eugene Kotomin\, Max Planck Institutes\, GermanyHydrogen production directly from water is the efficient source for green\, environmentally friendly energy. Sunlight-driven water splitting is one of the most promising pollution-free strategies for production of hydrogen. Photocatalytic water splitting consists of water decomposition into hydrogen and oxygen by a reaction with photo-generated charge carriers. However\, many challenges must be overcome before photocatalytic water splitting can be practically implemented at a large scale. We discuss the results of large scale first-principles calculations on structural and electronic properties of SrTiO3 (STO) perovskite photocatalyst (band gap 3.25 eV) and how to modify its electronic band structure by means of defects and impurities. DFT calculations were performed with CRYSTAL17 computer code within the linear combination of atomic orbitals (LCAO) approximation and using B1WC advanced hybrid exchange-correlation functional. We considered the bulk STO crystal and its (001) 2D slabs\, as well as faceted nanoparticles. A supercell was used to simulate point defects (neutral and charged oxygen vacancies\, N and Al substitutional atoms [1-4]). Introduction of these defects indeed makes STO photocatalyst more efficient for sunlight-driven water splitting. \n\n\n\nFacile Reconstruction of Extended Defects in Antimony Selenide Demonstrated by First-Principles Calculations and Electron Microscopy – Keith McKenna\, University of York\, UKMost crystalline materials\, whether naturally occurring or manufactured for technology\, are polycrystalline\, making grain boundaries one of the most ubiquitous types of structural defect. Grain boundaries in semiconductors and insulators often cause significant modification of electronic\, optical and transport properties\, therefore affecting the performance of polycrystalline materials for diverse technologies (e.g.\, in photochemistry\, photovoltaics\, energy storage and electronics). Computational modelling of grain boundaries can bring valuable insights into fundamental properties and aid in the discovery and optimisation of materials for applications. \n\n\n\nIn the first part of this talk\, I will provide a brief introduction to computational approaches for modelling grain boundaries\, providing examples from our previous work spanning a range of materials. Through these examples the power of combining first-principles calculations and electron microscopy for understanding grain boundaries will be highlighted [1]. \n\n\n\nIn the second part of the talk\, I will present our investigations into the structure and properties of extended defects in antimony selenide (Sb2Se3) and related semiconductors that are promising for application as solar absorbers in thin-film photovoltaic and photoelectrochemical cells [2\,3]. Different from the conventional picture that emerges from studies of many other compound semiconductors\, we show that dangling bonds introduced at such extended defects such as surfaces and grain boundaries readily reconstruct to eliminate deleterious deep gap states associated with enhanced electron-hole recombination [4\,5]. Our calculations predict the reconstruction of extended defects leads to significant long-range strain fields which have subsequently been observed using scanning transmission electron microscopy [6]. Preliminary results for structurally-similar chalcohalide materials indicate this behaviour may be common to a wider range of promising semiconductors conferring some degree of intrinsic grain boundary tolerance. \n\n\n\nReferences[1] J. Quirk et al\, Appl. Phys. Rev. 11\, 011308 (2024).[2] Y. Zhao et al\, Adv. Energy Mater. 12\, 2103015 (2022).[3] Z. Duan et al\, Adv. Energy Mater. 34\, 2202969 (2022).[4] R.E. Williams et al\, ACS Appl. Mater. & Inter. 12\, 21730 (2020).[5] K.P. McKenna\, Adv. Electron. Mater. 7\, 2000908 (2021).[6] R.A. Lomas-Zapata et al\, Phys. Rev. X Energy 3\, 013006 (2024). \n\n\n\nTowards atomic precision in superconducting qubits: controlling interfacial defects and disorder in Ta films – Peter Sushko\, Pacific Northwest National Laboratory\, USAIn recent years\, there has been significant progress in the development of platforms for quantum computing. A breakthrough in quantum computing hardware has been the discovery of superconducting quantum circuits\, which offer scalability and low error rates. However\, the practical implementation of superconducting qubits in a quantum processor is hindered by their limited coherence lifetime. \n\n\n\nCoherence times of transmon devices can be affected by oxidation of the components made of superconducting metals\, such as Nb and Ta. Spontaneous oxidation results in the formation of suboxide phases and surface amorphization that contribute to dielectric losses that are primarily attributed to two-level systems within such native oxide layers. Mitigating undesirable effects of surface oxidation requires understanding the mechanisms of interfacial interactions at the atomic scale. \n\n\n\nWe will review recent experimental studies that provide new insights into the atomic structure and composition of the native oxide layer and focus on ab initio simulations of the mechanisms of Ta and Nb interaction with oxygen. In particular\, we consider factors controlling the early stages of Ta film growth\, and energetics and pathways of the Ta film oxidation\, including propagation of the oxidation front into the Ta subsurface and corresponding electronic structure changes\, and explore strategies for suppressing Ta oxidation using reactive metal coatings. We will also consider the mechanism of defect accumulation at the metal/metal oxide interfaces and discuss models of candidate two-level systems. \n\n\n\nC. Zhou\, J. Mun\, J. Yao\, A. K. Anbalagan\, M. D. Hossain\, R. A. McLellan\, R. Li\, K. Kisslinger\, X. Tong\, G. Li\, A. R. Head\, C. Weiland\, A. L. Walter\, Q. Li\, Y. Zhu\, P. V. Sushko\, M. Liu\, Ultrathin magnesium-based coating as an efficient oxygen barrier for superconducting circuit materials\, Advanced Materials 36\, 2310280 (2024). DOI: 10.1002/adma.202310280 \n\n\n\nJ. Mun\, P. V. Sushko\, E. Brass\, C. Zhou\, K. Kisslinger\, X. Qu\, M. Liu\, Y. Zhu\, Probing oxidation-driven amorphized surfaces in a Ta(110) film for superconducting qubit\, ACS Nano 18\, 1126-1136 (2024). DOI: 10.1021/acsnano.3c10740 \n\n\n\nInterfacial re-hybridisation: blessing or damnation? – Gilberto Teobaldi\, STFC – UKRIInterfacial electronic re-hybridisation (ER)\, and the ensuing emergence of properties different from the isolated interface-constituents\, has long attracted scientific and technological interest. Understanding ER holds the key to control interfacial properties\, and promote rational advances in those technologies whose functioning (or failure) rests on contacting different materials. Control of ER may also enable the definition of new solutions by interfacing expectedly unappealing\, yet readily available\, materials. The residual challenges in atomic and time resolved experimental characterisation of interfaces make Density Functional Theory (DFT) a valuable source of atomistic insights\, albeit with intrinsic accuracy-viability compromises. DFT can also be used to inexpensively explore materials and strategies to tailor interfacial ER and emergent properties for a given application. Along these lines\, here I will present an overview of recent results\, insights and\, where available experimental validation\, on the potential of interfacial ER for applications as diverse as enhancement of magnetism in transition-metals\, stabilisation of alkali-metal anodes for high energy-density batteries\, and control of redox kinetics at photo/electro-chemical interfaces. \n\n\n\n\n\n\n\nWe expect an audience of scientific peers in computational materials research\, from PhD students to Senior Professors. \n\n\n\nPlease direct any queries you have to Karen Stoneham (tyc-administrator@ucl.ac.uk).
URL:https://thomasyoungcentre.org/event/advances-in-modelling-defects-and-interfaces-workshop/
LOCATION:Institute of Physics\, 37 Caledonian Road\, London\, N1 9BU\, United Kingdom
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20241029T133000
DTEND;TZID=Europe/London:20241031T133000
DTSTAMP:20260410T213859
CREATED:20240228T143725Z
LAST-MODIFIED:20241029T150239Z
UID:4929-1730208600-1730381400@thomasyoungcentre.org
SUMMARY:MMM Hub Conference & User Meeting 2024
DESCRIPTION:MMM Hub Conference & User Meeting 2024 Share on X\n\n\n\n\nVenue: Battle of Britain Bunker\, Wren Ave\, Uxbridge UB10 0GG \n\n\n\nThe Materials and Molecular Modelling (MMM) Hub is holding a conference and user meeting between 29-31 October 2024\, to bring together the national community of modellers in materials and theoretical chemistry to present the latest research in the field\, and provide the opportunity to network and discuss with like-minded researchers.  The meeting is taking place at the Battle of Britain Bunker\, Wren Ave\, Uxbridge UB10 0GG\, close to Brunel University London. \n\n\n\n\n\nThe conference will highlight the high-calibre scientific throughput produced across the MMM Hub’s partner community and beyond\, highlighting particularly the contribution of modern HPC resources (including MMM Hub’s ‘Young’)\, in enabling these advances.   A selection of breakthrough materials and molecular modelling research taking place across the country will be presented\, addressing challenges to society and industry through simulation at the atomic scale\, alongside discussion in emerging computing trends and how this impacts materials scientists. \n\n\n\nTopics will include\, but not be limited to\, molecular modelling\, biological and technological soft matter\, functional materials and devices\, structural materials\, surfaces and interfaces and methods and method development.  The meeting will provide an excellent opportunity for researchers at all levels to learn about the forefront of this important field in numerical simulation\, and to showcase their most recent results. \n\n\n\nThe meeting will see a number of invited and contributed talks\, plus a selection of 2-minute flash talks from across the community.  We also invite participants\, particularly graduate student users of the Hub\, to contribute A1-size\, portrait orientation posters of their research. The posters will be on display to participants throughout the day\, and at a drinks reception and Poster Presentation. \n\n\n\n\n\n\n\nTuesday 29th October 2024 \n\n\n\n\n\n\n\n\n\n\n\nWednesday 30th October \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThursday 31st October \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMMM Hub Conference & User Meeting Programme 2024Download\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nInvited speakers\n\n\n\nNavigating Materials Space with Machine Learning – Keith Butler\, University College LondonThe discovery and design of new materials is critical for advancing carbon-emission reducing technologies such as renewable energy and electric vehicles. Experimental discovery of new materials is typically slow and costly\, quantum mechanics (QM) calculations have brought computational materials design within reach. However\, QM calculations are often limited to relatively small sets of materials\, as their computational costs are too great for large-scale screening\, this is the case for calculating properties required for new energy materials. New methods in machine learning (ML) and deep learning (DL) have emerged as a powerful complementary tool to QM calculations – learning rules from data calculated from QM and applying cheap\, efficient models to explore large chemical spaces. However\, several challenges still exist for example\, learning from small and limited datasets\, obtaining measures of confidence in models and understanding the results of DL models. All these challenges must be addressed to fully realise the power of DL for design of new sustainable materials. In this talk I will give examples of recent work in our group to address these issues\, including using unsupervised learning to accelerate the characterisation of battery materials without requiring labelled data[1]\,   building models with reliable uncertainty quantification [2]\, capable of learning on significantly smaller datasets than regular DL models and using DL to match experimental and simulated data [3]. Finally\, I will also discuss how the latest exciting developments in large language models could help to solve the challenges of crystal structure prediction [4]. \n\n\n\n1 Versatile domain mapping of scanning electron nanobeam diffraction datasets utilising variational autoencoders npj Computational Materials 9 (1)\, 14\, 20232 Entropy-based active learning of graph neural network surrogate models for materials properties The Journal of Chemical Physics 155 (17)\, 174116\, 20213 Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data Digital Discovery 2.578\, 20234 Crystal Structure Generation with Autoregressive Large Language Modeling arXiv preprint arXiv:2307.04340\, 2023 \n\n\n\nA force field for the periodic table – Gábor Csányi\, University of CambridgeA new computational task has been defined and solved over the past 15 years for extended material systems: the analytic fitting of the Born-Oppenheimer potential energy surface as a function of nuclear coordinates. The resulting potentials  (“force fields”) are reactive\, many-body\, with evaluation costs that are currently on the order of 0.1-10 ms/atom/cpu core (or about 1-10ms on a powerful GPU)\, and reach accuracies of a few meV/atom when trained specifically for a given system using iterative or active learning methods. The latest and most successful architectures leverage many-body symmetric descriptions of local geometry and equivariant message passing networks.  Perhaps the most surprising recent result is the stability of models trained on very diverse training sets across the whole periodic table. Our recently discovery is that the MACE-MP-0 model that was trained on just ~150\,000 real and hypothetical small inorganic crystals (90% of training set < 70 atoms)\, is capable of stable molecular dynamics at ambient conditions on any system tested so far – this includes crystals\, liquids\, surfaces\, clusters\, molecules\, and combinations of all of these. The astounding generalisation performance of such foundation models open the possibility to creating a universally applicable interatomic potential with useful accuracy (especially when fine-tuned with a little bit of domain-specific data)\, and democratise quantum-accurate large scale molecular simulations by lowering the barrier to entry into the field. \n\n\n\nIdentifying CO2 Conversion Catalysts: High-Throughput DFT Calculations\, Machine Learning\, and Beyond – Devis Di Tommaso\, Queen Mary University of LondonThe rising carbon dioxide (CO2) level and overall concentrations in the atmosphere due to fossil fuel combustion\, a major cause of global warming\, pose a serious threat to humankind. One of the most promising solutions to mitigating this risk is the chemical conversion of gaseous CO2 into value-added chemicals and materials. Catalysts can facilitate favourable pathways to reduce the overall energy requirements of the electrochemical CO2 reduction reaction (eCO2RR). The eCO2RR has emerged as a potential strategy for converting CO2 because if coupled with electricity from renewable sources (wind\, solar\, or hydropower plants)\, the eCO2RR could achieve a carbon-neutral energy cycle [1]. Unfortunately\, due to the inertness of CO2\, the main challenge is to find a specific catalyst capable of accelerating the sluggish kinetics of the eCO2RR. In this talk\, I will give an overview of computational strategies we have developed based on the u quantum chemical methods [2-4]\, high-throughput calculations [5]\, and machine learning [6\, 7] methods to accelerate the discovery of earth-abundant and active metal-based catalysts.. References: [1] Z. Wang\, Y. Zhou\, P. Qiu\, C. Xia\, W. Fang\, J. Jin\, L. Huang\, Y. Q. Su\, R. Crespo-Otero\, X. Tian\, B. You\, W. Guo\, D. Di Tommaso\, Y. Pang\, S. Ding\, and B. Y. Xia\, Advanced catalyst design and reactor configuration upgrade in electrochemical carbon dioxide conversion\, Advanced Materials\, 2023\, 35\, 2303052; [2] W. Lin\, A. G. Nabi\, M. Palma\, and D. Di Tommaso\, Copper nanowires for electrochemical CO2 reduction reaction\, ACS Applied Nano Materials\, 2024\, doi: 10.1021/acsanm.3c06116; [3] Q. Zhao\, K. Lei\, B. Yu Xia\, R. Crespo-Otero\, and D. Di Tommaso\, Molecular engineering binuclear copper catalysts for selective CO2 reduction to C2 products\, Journal of Energy Chemistry\, 2024\, 90\, 166-173; [4] Q. Zhao\, R. Crespo-Otero\, and D. Di Tommaso\, The role of copper in enhancing the performance of heteronuclear diatomic catalysts for the electrochemical CO2 conversion to C1 chemicals\, Journal of Energy Chemistry\, 2023\, 85\, 490–500; [5] Ab initio random structure searching and catalytic properties of copper-based nanocluster with Earth-abundant metals for the electrocatalytic CO2-to-CO conversion\, A. G. Nabi\, A. ur Rehman\, A. Hussain\, and D. Di Tommaso\, Molecular Catalysis\, 2022\, 527\, 112406; [6] A. Muthuperiyanayagam and D. Di Tommaso\, Electrocatalytic CO2 reduction on amorphous Cu surfaces: Unveiling structure-activity relationships\, ChemRxiv\, 2024\, doi: 10.26434/chemrxiv-2024-bxqmn; [7] M. Anselmi\, G. Slabaugh\, R. Crespo-Otero\, and D. Di Tommaso\, Molecular graph transformer: stepping beyond ALIGNN into long-range interactions\, Digital Discovery\, 2024\, DOI: 10.1039/D4DD00014E \n\n\n\nThe (other) big bang theory: towards a structure/property model to rationalise the impact sensitivities of energetic materials – Carole A. Morrison\, University of EdinburghImpact sensitivity is a measure of how much mechanical energy is required to initiate explosives and propellants. This important safety metric is typically measured by a simple experiment\, where a known weight is dropped from an increasing height\, until the minimum threshold energy is observed. However\, the data obtained (essentially a binary call of ‘go\, no-go’) is prone to user interpretation and variations in sample purity\, crystallinity\, temperature\, humidity etc. This uncertainty in the experimental measurement has driven the need for physical models that can successfully link the chemical structure to the material property. \n\n\n\nOver the last five or so years we have developed a condensed phase model that can link the crystal structure\, via its phonon density of states (computed using plane-wave DFT)\, to its impact sensitivity. Based on the principles of vibrational up-pumping we have now successfully applied this model to ca. 40 energetic materials. However\, while it is important for a model to predict the desired property from the given structure\, the reverse process is actually more powerful\, i.e. for a desired impact sensitivity what sort of molecules should I make? For this more data is needed\, and our existing workflow becomes unmanageable. In short\, it’s time to think about machine learning. \n\n\n\nThis talk outlines our approach to building a machine learning model for impact sensitivity using features we learned from our vibrational up-pumping model\, alongside others that can act to guide synthetic chemists in molecular design. We also consider how to approach the issue of uncertainty in the experimental dataset. \n\n\n\nExploring hybrid organic/2D van der Waals heterostructures with first-principles quantum mechanical simulations – Juliana Morbec\, Keele UniversityCombining two-dimensional (2D) materials with organic materials can be highly attractive for applications that require flexibility and where size and weight are important parameters\, such as in wearable\, portable\, and mobile devices. Organic materials often exhibit excellent optical absorption efficiency and photo- and temperature-induced conformational changes\, while 2D materials tend to demonstrate relatively high carrier mobility\, superior mechanical flexibility\, and tunable electronic and optical properties. Combining both systems can stabilize the organic materials and create heterostructures with both high carrier mobility and high optical absorption efficiency\, which is promising for solar energy conversion. In this work\, we investigate heterostructures composed of organic molecules (e.g.\, pentacene and azulene) and transition metal dichalcogenides (TMDs) for application in photovoltaic devices\, using density-functional-theory calculations. We examine the interaction between the molecules and monolayer TMDs\, as well as the band alignment of the heterostructures\, considering the effects of molecular coverage\, rotation\, and dielectric screening. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThis year’s MMM Hub Conference is supported by AWE\, Hewlett Packard Enterprise (HPE)\, The American Society for Mechanical Engineers (ASME)\, RSC Advances\, and RSC Molecular Systems Design & Engineering (MSDE) \n\n\n\nRSC MSDE are offering up to 2 MSDE sponsored poster/presentation prizes each consisting of a certificate\, £200 RSC Books voucher and a free RSC student membership for a year. \n\n\n\nRSC Advances is offering a £100 prize to a winning poster presenter. \n\n\n\n\n\n\n\n\n\n\n\nGetting to the Battle of Britain Bunker\n\n\n\n\nBY TUBE:Nearest station Uxbridge (Metropolitan and Piccadilly)\, then one mile walk through Dowding Parkvia the High Street and St Andrews Road\, signposted by blue and brown tourist signs. There is a cab rank at Uxbridge.BY BUS:  To St. Andrew’s Church on the A10 or U3 from Heathrow or the 427\, U1\, U4 or U7 then walk through Dowding Park. Bus U2 stops at the junction of Hercies Road and Honey Hill.  Check routes at www.tfl.gov.uk/plan-a-journey\n\n\n\nBY CAR: Use satnav postcode UB10 0GG or search maps for Battle of Britain Bunker\n\n\n\n\n\n\n\n\nACCESS\n\n\n\nThere are accessible parking spaces available near the main entrance\, a lift inside the building and a ramp up to the building. \n\n\n\nSuggested hotel: Premier Inn\, Colham House\, Bakers Road\, Uxbridge\, UB8 1QJ \n\n\n\n\n\n\n\nMMM-Hub-conference-2024-privacy-notice \n\n\n\n\n\n\n\nCode of conduct: \n\n\n\nWe value the participation of every member of the materials and molecular modelling community and want to ensure that everyone has an enjoyable and fulfilling experience\, both professionally and personally. Accordingly\, all participants of the MMM Hub Conference and User meeting are expected to always show respect and courtesy to others.  The MMM Hub and its partners strive to maintain inclusivity in all of our activities.  All participants (staff and students) are entitled to a harassment-free experience\, regardless of gender identity and expression\, sexual orientation\, disability\, physical appearance\, body size\, race\, age\, and/or religion. Harassment in any form is not acceptable for any of us.  We respectfully ask all attendees of the MMM Hub Conference and User meeting to kindly conform to the following Code of Conduct: \n\n\n\n\nTreat all individuals with courtesy and respect.\n\n\n\nBe kind to others and do not insult or put down other members.\n\n\n\nBehave professionally. Remember that harassment and sexist\, racist\, or exclusionary jokes are not appropriate.\n\n\n\nHarassment includes\, but is not limited to\, offensive verbal comments related to gender\, sexual orientation\, disability\, physical appearance\, body size\, race\, religion\, sexual images in public spaces\, deliberate intimidation\, stalking\, following\, harassing photography or recording\, sustained disruption of discussions\, and unwelcome sexual attention.\n\n\n\nParticipants asked to stop any harassing behaviour are expected to comply immediately.\n\n\n\nContribute to communications with a constructive\, positive approach.\n\n\n\nBe mindful of talking over others during presentations and discussion and be willing to hear out the ideas of others.\n\n\n\nAll communication should be appropriate for a professional audience\, and be considerate of people from different cultural backgrounds. Sexual language and imagery are not appropriate at any time.\n\n\n\nChallenge behaviour\, action and words that do not support the promotion of equality and diversity.\n\n\n\nArrive at the conference events punctually where possible.\n\n\n\nShow consideration for the welfare of your friends and peers and\, if appropriate\, provide advice on seeking help.\n\n\n\nSeek help for yourself when you need it.\n\n\n\n\nMMM Hub Conference 2024 Organising Committee George Booth\, King’s College LondonAlejandro Santana Bonilla\, King’s College LondonRicardo Grau-Crespo\, University of ReadingEd Smith\, Brunel University LondonKaren Stoneham\, University College LondonDavid Wilkins\, Queen’s University BelfastJun Xia\, Brunel University London
URL:https://thomasyoungcentre.org/event/mmm-hub-conference-user-meeting-2024/
CATEGORIES:Main event
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