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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251105T153000
DTEND;TZID=Europe/London:20251105T170000
DTSTAMP:20260430T121118
CREATED:20250923T174410Z
LAST-MODIFIED:20251021T123952Z
UID:6988-1762356600-1762362000@thomasyoungcentre.org
SUMMARY:TYC Soiree: simulation of photo-excited and charge transport processes in organic semiconductors
DESCRIPTION:TYC Soiree: simulation of photo-excited and charge transport processes in organic semiconductors Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nAtomistic insights into the photodynamics of organic crystals and nanoclusters – Federico Hernandez\, QMUL\n\n\n\nExciton Delocalization and Dynamics: From Light Absorption to Charge Separation in Molecular Aggregates – Samuele Giannini\, University of Pisa\n\n\n\nLight absorption\, charge separation\, and electronic transport are vital for optimizing optoelectronic devices and designing new materials\, yet a fundamental understanding remains challenging because these processes span multiple time\, length\, and morphological scales. Quantum phenomena—arising from coupled electronic and vibrational (vibronic) interactions—govern both the optical response and electronic transport in supramolecular aggregates and molecular semiconductors.1\,2 \n\n\n\nI will show how first-principles-based Hamiltonians\, parametrized for realistic\, energetically disordered material morphologies and incorporating localized and charge transfer states\, can explain aggregation-induced changes in steady-state optical spectra.3\,4 By coupling these Hamiltonians with both full quantum dynamics and mixed quantum–classical dynamics\, we characterize the nature and evolution of electronic excitations across a broad range of timescales. Ultrafast dynamics in dense vibronic manifolds are resolved using efficient Multiconfigurational Time Dependent Hartree wavepacket propagation\, while a surface-hopping approach in the excitonic-state basis enables simulations at longer times. \n\n\n\nOur results clarify the role of exciton delocalization and coherence5 in enhancing the efficiency of important electronic processes such as charge separation in molecular aggregates6 and provide structure–property relationships that inform the design of more efficient optoelectronic devices. \n\n\n\nReferences: \n\n\n\n1. Giannini\, S. et al. Exciton transport in molecular organic semiconductors boosted by transient quantum delocalization. Nat. Commun. 13\, 2755 (2022).2. Giannini\, S. et al. Transiently delocalized states enhance hole mobility in organic molecular semiconductors. Nat. Mater. 22\, 1361–1369 (2023).3. Giannini\, S. et al. On the Role of Charge Transfer Excitations in Non-Fullerene Acceptors for Organic Photovoltaics. Mater. Today 80\, 308–326 (2024).4. Giannini\, S.\, Cerdá\, J.\, Prampolini\, G.\, Santoro\, F. & Beljonne\, D. Dissecting the nature and dynamics of electronic excitations in a solid-state aggregate of a representative non-fullerene acceptor. J. Mater. Chem. C 12\, 10009–10028 (2024).5. Giannini\, S.\, Segalina\, A.\, Padula\, D.\, Cantina\, M. & Pastore\, M. Disentangling Delocalization and Coherence in Photoexcited Noisy Supramolecular Aggregates. (submitted 2025)6. Ivanovic\, F.\, Peng\, W.-T.\, Giannini\, S.\, Blumberger\, J. Transiently Delocalised Hybrid Quantum States are the Gateways for Efficient Exciton Dissociation at Organic Donor-Acceptor Interfaces. (2025) https://doi.org/10.21203/rs.3.rs-7059572/v1.
URL:https://thomasyoungcentre.org/event/tyc-soiree-simulation-of-photo-excited-and-charge-transport-processes-in-organic-semiconductors/
LOCATION:Leolin Price Lecture Theatre in UCL GOSICH – Wellcome Trust Bldg\, 30 Guildford Street\, London\, WC1N 1DP\, United Kingdom
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251113T160000
DTEND;TZID=Europe/London:20251113T170000
DTSTAMP:20260430T121118
CREATED:20251014T104037Z
LAST-MODIFIED:20251014T104039Z
UID:7028-1763049600-1763053200@thomasyoungcentre.org
SUMMARY:TYC Seminar: Machine Learning for Periodic and Framework Materials
DESCRIPTION:TYC Seminar: Machine Learning for Periodic and Framework Materials Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nDr Ganna Gryn’ova\, University of Birmingham\n\n\n\nSignificant recent advances in chemical machine learning allow predictions of structures and physico-chemical properties of molecular systems with high accuracy and at a fraction of the computational cost of conventional quantum-chemical modelling. However\, the associated tools\, such as foundational models (e.g.\, MACE) or quantum-inspired representations (e.g.\, SPAHM and MAOC1) are not easily and directly transferrable to periodic materials due to the need to fine-tune the models on target materials\, sparsity of high-quality experimental training data\, and the higher costs of generating the presentations. In this talk\, we will discuss our recent efforts to address these limitations. Focusing on metal-organic and covalent organic frameworks\, we will present a new quantum-inspired representation for machine learning tasks and a new fragmentation algorithm2 enabling rational design of these systems. We will also demonstrate how persistent homology can be employed to coarse-grain the representation reducing the computational effort without sacrificing the accuracy of the predictions. \n\n\n\nReferences \n\n\n\nM. Ernst\, R. Fedorov\, A. Calzolari\, F. F. Grieser\, S. Ber\, G. Gryn’ova\, preprint DOI: 10.26434/chemrxiv-2025-zbc8x. \n\n\n\nS. Llenga\, G. Gryn’ova\, J. Chem. Phys. 2023\, 158\, 214116.
URL:https://thomasyoungcentre.org/event/tyc-seminar-machine-learning-for-periodic-and-framework-materials/
LOCATION:Royal School of Mines\, Room G05\, Royal School of Mines\, London\, South Kensington\, SW7 2AZ\, United Kingdom
CATEGORIES:Main event
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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251120T150000
DTEND;TZID=Europe/London:20251120T170000
DTSTAMP:20260430T121118
CREATED:20250529T124145Z
LAST-MODIFIED:20251113T161933Z
UID:6703-1763650800-1763658000@thomasyoungcentre.org
SUMMARY:TYC Highlight Seminar: Machine-Learned Force Fields for Molecular Simulations Beyond AlphaFold and Empirical Potentials
DESCRIPTION:TYC Highlight Seminar: Machine-Learned Force Fields for Molecular Simulations Beyond AlphaFold and Empirical Potentials Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nAlexander Tkatchenko\, University of Luxembourg\n\n\n\nThe convergence between accurate quantum-mechanical (QM) models (and codes) with efficient machine learning (ML) methods seem to promise a paradigm shift in all-atom simulations. Many challenging applications are now being tackled by increasingly powerful QM/ML methodologies (https://doi.org/10.1021/acs.chemrev.0c01111; https://doi.org/10.1021/acs.chemrev.1c00107). These include modeling covalent materials\, molecules\, molecular crystals\, surfaces\, and even whole proteins under physiological conditions (https://www.science.org/doi/abs/10.1126/sciadv.adn4397; https://doi.org/10.1021/jacs.5c09558).  \n\n\n\nIn this talk\, I will attempt to provide a reality check on these recent advances and on the developments required to enable fully predictive dynamics of complex functional (bio)molecular and material systems. Multiple challenges are highlighted\, in particular transferability in chemical space and interatomic interactions that should enable this field to grow for the foreseeable future.
URL:https://thomasyoungcentre.org/event/tyc-highlight-seminar-machine-learned-force-fields-for-molecular-simulations-beyond-alphafold-and-empirical-potentials/
LOCATION:Denys Holland Lecture Theatre\, Bentham House\, UCL\, 4–8 Endsleigh Gardens\, London\, WC1H 0EG\, United Kingdom
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251127T150000
DTEND;TZID=Europe/London:20251127T170000
DTSTAMP:20260430T121118
CREATED:20251008T131059Z
LAST-MODIFIED:20251125T130914Z
UID:7018-1764255600-1764262800@thomasyoungcentre.org
SUMMARY:TYC Soiree: Modelling and simulation of biomolecules: pathways\, kinetics and catalysis
DESCRIPTION:TYC Soiree: Modelling and simulation of biomolecules: pathways\, kinetics and catalysis Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nFor anyone attending online: \n\n\n\n\nhttps://ucl.zoom.us/j/99843406795\n\n\n\n\nMeeting ID: 998 4340 6795  \n\n\n\n\n\n\n\n15:00 – 15:40Gabriele Corso\, Massachusetts Institute of Technology15:40 – 16:20Marc Van der Kamp\, University of Bristol16:20 – 17:00Stefano Motta\, University of Milano Bicocca17:00 onwardsDrinks reception at UCL Physics E7\n\n\n\n \n\n\n\nBoltz: Towards Accurate Biomolecular Modeling and Design – Gabriele Corso\, Massachusetts Institute of Technology\n\n\n\nAccurately modeling biomolecular interactions remains a central challenge in modern biology. Breakthroughs such as AlphaFold3 and Boltz-1 have greatly advanced structure prediction of biomolecular complexes. Building on this progress\, we introduced Boltz-2\, the first AI model to approach the accuracy of free-energy perturbation (FEP) methods for estimating small molecule–protein binding affinities. Most recently\, with BoltzGen\, we demonstrated that fine-tuning large-scale structure prediction models for protein design enables a powerful end-to-end pipeline. We validated this pipeline experimentally with multiple wet-lab collaborators\, achieving successful designs across a wide range of novel targets. \n\n\n\nGabriele Corso recently received his PhD from MIT CSAIL where his research focused on developing novel ML frameworks to tackle challenging problems in drug discovery and he led the development of popular models in the space including DiffDock\, Boltz-1 and Boltz-2. \n\n\n\nMapping Biomolecular Conformational Pathways with Self-Organizing Maps – Stefano Motta\, University of Milano Bicocca\n\n\n\nUnderstanding complex biomolecular processes from molecular dynamics (MD) simulations requires interpreting large\, high-dimensional datasets. I will discuss how Self-Organizing Maps (SOMs)\, a type of unsupervised machine learning models\, can generate intuitive\, low-dimensional representations of the conformational space sampled during these simulations (1). I will then demonstrate how this approach\, implemented in our new R package SOMMD(2)\, can be used to reconstruct molecular pathways in processes such as protein unfolding and ligand binding\, and to build transition network models that characterize their key events(1\,3). \n\n\n\n1.      Motta\, S.\, Callea\, L.\, Bonati\, L.\, & Pandini\, A. (2022). PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations. Journal of Chemical Theory and Computation\, 18(3)\, 1957–1968. \n\n\n\n2.      Motta S.\, Callea L.\, Mulla S. I.\, Davoudkhani H.\, Bonati L.\, Pandini A. (2025). SOMMD: an R package for the analysis of molecular dynamics simulations using self-organizing map. Bioinformatics\, 41(6)\, btaf308. \n\n\n\n3.      Callea L.\, Caprai C.\, Bonati L.\, Giorgino T.\, Motta S. (2024). Self-organizing maps of unbiased ligand–target binding pathways and kinetics. The Journal of Chemical Physics\, 161\, 135102. \n\n\n\nStefano Motta obtained his PhD in Chemical Sciences from the University of Milano-Bicocca in 2018\, where he has been an Assistant Professor since 2022. His research focuses on the development and application of computational methods to investigate the structure and dynamics of biomolecular systems. His current research interests include the use of molecular dynamics to study protein-ligand recognition\, the mechanism of action of bHLH-PAS proteins\, the characterization of nanosystems for biomedical applications\, and the development of machine learning approaches for the analysis of complex simulations. \n\n\n\nMultiscale simulations for understanding and engineering enzymes: from QM/MM to ML/MM – Marc Van der Kamp\, University of Bristol\n\n\n\nEnzymes have excellent potential as selective and efficient biocatalysts for industry. Obtaining enzyme biocatalysts with both the desired selectivity and activity\, however\, remains a challenge. Atomistic simulations can provide valuable information for rational engineering. Ideally\, simulation protocols require limited computational resource (and thus energy) but maintain sufficient accuracy. Here\, the development of tools and methods for ‘in silico enzyme screening’ with reaction simulations are discussed\, highlighting applications to different enzymes. We have shown that short QM/MM reaction simulations with semi-empirical QM methods can be used to correctly indicate activity for certain enzymes\, such as serine beta-lactamases.1 When combined with automated protocols to set up simulations\, such as Enlighten2\,2 this can result in efficient evaluation of activity and selectivity. We demonstrate how this can be used to obtain key insights into natural beta-barrel Diels-Alderases\,3 which are promising stable and stereoselective biocatalysts.  \n\n\n\nFor highly efficient screening of enzyme activity\, such that calculations can be used during enzyme (re)design\, further increases in efficiency and accuracy are important. Replacing QM by machine-learning (ML) potentials can\, in principle\, offer QM accuracy at a fraction of the computational cost. However\, due to the absence of electrons in ML potentials\, properly describing the electrostatic interaction between ML and MM regions\, crucial for capturing enzyme catalysis\, is a challenge. We have developed the “electrostatic ML embedding” (EMLE) scheme that solves this issue\, allowing DFT/MM accuracy.4\,5 Here\, we show that this method can be applied for enzyme reaction simulations to capture key catalytic effects.  \n\n\n\nReferences:  \n\n\n\n\nV. H. A. Hirvonen\, K. Hammond\, E. I. Chudyk\, M. A. L. Limb\, J. Spencer\, A. J. Mulholland and M. W. van der Kamp. J. Chem. Inf. Model. 2019\, 59\, 3365-3369.\n\n\n\nK. Zinovjev and M. W. van der Kamp. Bioinformatics\, 2020\, 36\, 5104–5106.\n\n\n\nL. Maschio\, et al. Chem. Sci. 2024\, 15\, 11572-11583; Mbatha et al.\, Chem. Sci. 2024\, 15\, 14009-14015.\n\n\n\nK. Zinovjev\, L. Hedges\, R. M. Andreu\, C. Woods\, I. Tuñón and M. W. van der Kamp. J. Chem. Theory Comput. 2024\, 20\, 4514–4522.\n\n\n\nV. Gradisteanu\, E. W. Chan\, L. Hedges\, M. Malagarriga\, R. David\, M. de la Puente\, D. Laage\, I. Tuñón\, M. W. van der Kamp\, K. Zinovjev. ChemRxiv 2025\, DOI: 10.26434/chemrxiv-2025-nw9lt. \n\n\n\n\nMarc is Associate Prof. in Computational Biochemistry in the School of Biochemistry in Bristol\, and is an expert in biomolecular simulation of enzymes and their reactions. After obtaining a PhD in this field in Bristol (2008)\, he pursued postdoctoral research at the University of Washington (with Prof. Valerie Daggett) and in Bristol (with Prof. Adrian Mullholland). Then\, as a BBSRC David Phillips Fellow (2015-2021)\, he established a group with PDRAs and PhDs and advanced the use of detailed biomolecular simulation for understanding enzyme biocatalysts and predicting properties of their variants. The main research interests in the group include: enzymes involved in antibiotic resistance\, computational simulation methods to aid enzyme engineering and design of biologic drugs\, and further understanding the principles of enzyme catalysis and specificity.
URL:https://thomasyoungcentre.org/event/https-tyc-soiree-modelling-and-simulation-of-biomolecules-pathways-kinetics-and-catalysis/
LOCATION:Denys Holland Lecture Theatre\, Bentham House\, UCL\, 4–8 Endsleigh Gardens\, London\, WC1H 0EG\, United Kingdom
CATEGORIES:Main event
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