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SUMMARY:3rd TYC Early Career Award Symposium 2026
DESCRIPTION:3rd TYC Early Career Award 2026 Share on X\n\n\n\n\nThe TYC Early Career Prize\, will be awarded to an early career researcher in recognition of their original published research in the theory and/or simulation of materials or (bio)molecules. \n\n\n\nAre you an early career postdoctoral researcher eager to showcase your work and accelerate your professional growth? The TYC Early Career Prize\, established in 2022\, is a fantastic opportunity to gain recognition for your original published research in the theory and/or simulation of materials or (bio)molecules. Winning – or even being shortlisted – offers tremendous benefits: a prestigious accolade that strengthens your CV\, enhances your visibility in the scientific community\, and opens doors to future funding opportunities.  \n\n\n\nShortlisted candidates will be invited to present their research at a dedicated in-person Symposium at UCL on 25 June 2026\, providing invaluable networking and exposure to leading academics in the field. Plus\, the award comes with a £500 prize\, underscoring the value placed on your contributions. Don’t miss this chance to elevate your career and position yourself as a rising leader in computational and theoretical science! \n\n\n\nDetails of how to apply  in 2028 can be found here \n\n\n\n\n\n\n\nThe awardee will be selected by a panel of academics in the broad field of theory and simulation of materials and molecules.  \n\n\n\n\nRegister to attend here\n\n\n\n\n\n\n\n\nAttendance is free but we kindly ask you to register before Sunday 7th June 2026. \n\n\n\n\n\n\n\nShortlisted candidates: \n\n\n\nMing Chen\, Imperial College London – From Simplified to Realistic Models: Advanced Constant-Potential Simulations of Interfacial Structure and Charging Dynamics\n\n\n\nMolecular dynamics simulations have become indispensable for understanding electrical double layers (EDLs)\, providing atomistic insight into interfacial structure and ion dynamics. However\, many existing models\, including constant charge method and classical constant potential method\, still rely on idealized descriptions of electrochemical interfaces\, neglecting quantum effects at the electrode surface and thereby limiting their ability to capture realistic charging behaviour.  \n\n\n\nHere\, we develop a constant-potential framework that explicitly accounts for electron spillover from the outermost nuclei of the electrode. This advance delivers a more realistic description of interfacial charge distributions\, local electric fields\, and capacitance\, and quantitatively reproduces the bell-shaped capacitance curve and charging timescale of aqueous electrolytes observed experimentally. Building on this methodological development\, we further apply advanced constant-potential simulations to ionic liquids confined in MXene slit pores and identify that charging is governed not simply by pore size\, but by voltage-dependent ion transport under confinement. In particular\, the in-pore lateral conductivity exhibits a non-monotonic dependence on polarization\, which uncovers two distinct transport regimes: an order-limited regime at low polarization and a collision-limited regime at higher polarization.  \n\n\n\nThese results establish advanced constant-potential molecular dynamics as a powerful framework for resolving interfacial charging under nanoconfinement\, offering both microscopic insight into nanoporous electrodes and design principles for next-generation high-power energy-storage devices. \n\n\n\n\n\n\n\nPhilipp Schienbein\, University College London – Machine-learning enhanced atomistic modelling of electrochemical interfaces and vibrational spectroscopy\n\n\n\nElectrochemical energy conversion processes at semiconductor–water interfaces\, such as photo- and electrocatalytic water splitting\, are governed by atomistic phenomena\, including solvation dynamics\, protonation equilibria\, ion adsorption\, and charge-dependent surface chemistry. A predictive description requires simulations that accurately capture the electronic structure while explicitly resolving the fluctuating solvent environment. \n\n\n\nIn this work\, we compute thermodynamic and kinetic properties of semiconductor–water interfaces at ab initio accuracy by combining enhanced sampling with machine-learning accelerated molecular dynamics. This approach enables the quantification of protonation free energies (pKa values) [1] and ion adsorption processes at metal oxide surfaces\, both in the absence and presence of charge carriers. By extending accessible time and length scales beyond conventional ab initio molecular dynamics\, we obtain direct atomistic insight into interfacial processes governing stability and reactivity [2]. \n\n\n\nComplementing these insights\, we develop a generally applicable machine-learning framework\, Mimyria [3\,4]\, that provides an integrated atomistic simulation-to-spectroscopy workflow. It enables the quantitative prediction of vibrational spectra (IR\, Raman) in explicit solvation at finite temperature\, explicitly accounting for anharmonic effects\, and allows direct comparison with experiment\, providing a stringent route to assess simulation accuracy while yielding atomistic insight into spectroscopic features that are experimentally inaccessible. Thereby\, the framework yields spectroscopic fingerprints under realistic conditions. It further enables the inclusion of external electric fields [5] and their effect on vibrational response. Building on this capability\, we simulate time-dependent perturbations\, where modelled light pulses drive vibrational excitations and induce reactive processes in non-equilibrium molecular dynamics [6]. This enables predictive access to time-resolved and non-linear vibrational spectroscopy\, including surface-specific techniques such as SFG\, and to light-driven chemical processes. \n\n\n\n[1] P. Schienbein and J. Blumberger. “Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water”. ChemPhysChem 26 (2024)\, e202400490.[2] P. Schienbein and J. Blumberger. “Nanosecond solvation dynamics of the hematite/liquid water interface at hybrid DFT accuracy using committee neural network potentials”. Phys. Chem. Chem. Phys. 24 (2022)\, 15365.[3] P. Schienbein. “Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor”. J. Chem. Theory Comput. 19 (2023)\, 705.[4] P. Schienbein. Mimyria: “Machine learned vibrational spectroscopy for aqueous systems made simple”. J. Chem. Theory Comput. accepted (2026).[5] K. Joll\, P. Schienbein\, K. M. Rosso\, and J. Blumberger. “Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials”. Nat. Commun. 15 (2024)\, 8192.[6] K. Joll and P. Schienbein. “THz Pump Pulse-Driven Temporal Response of Liquid Water Probed by Machine-Learning-Accelerated Non-Equilibrium Molecular Dynamics”. J. Phys. Chem. Lett. 16 (2025)\, 9183. \n\n\n\n\n\n\n\nJoao Simao\, Imperial College London
URL:https://thomasyoungcentre.org/event/3rd-tyc-early-career-award-2025/
LOCATION:Graduate Centre Foyer & Lecture Theatre\, Queen Mary University of London\, Mile End Road\, London\, E1 4NS\, United Kingdom
CATEGORIES:Main event
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