BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Memento EPFL// BEGIN:VEVENT SUMMARY:Advancing simulation\, analysis and prediction of complex chemical systems using modern chemical graph theory and computational topology DTSTART;VALUE=DATE:20250716 DTSTAMP:20260405T215027Z UID:346643075760943e2ac13d07e0324891b0f18014744bfd6f48a340ae CATEGORIES:Conferences - Seminars DESCRIPTION:You can apply to participate and find all the relevant informa tion (speakers\, abstracts\, program\,...) on the event website: https:// www.cecam.org/workshop-details/advancing-simulation-analysis-and-predictio n-of-complex-chemical-systems-using-modern-chemical-graph-theory-and-compu tational-topology-1389.\n\nRegistration is required to attend the full eve nt\, take part in the social activities and present a poster at the poster session (if any).  However\, the EPFL community is welcome to attend  specific lectures without registration if the topic is of interest to t heir research. Do not hesitate to contact the CECAM Event Manager if you h ave any question.\n\nDescription\nThe simulation of complex chemical\, mat erials and biophysical systems is increasingly reliant upon the transfer o f information across length and timescales to both accelerate simulation t ime as well as interpret collective phenomena that derive from many body i nteractions beyond the scale of electrons. Ultimately\, it is desirable t o extend many-body theories beyond traditional domains of electronic struc ture theory and into complex condensed matter systems\, where propagating system states in time must be able to self-consistently account for many-b ody effects (described by different granularities of information). This p resents a unique grand challenge for the computational chemistry community and will require interdisciplinary collaboration with the mathematics and computer science communities – where multidimensional data analysis ins pired by machine learning and AI is rapidly advancing the mathematical lan guages associated with the shape of data\, specifically the mathematical l anguages of graph theory and computational topology.\n \nThe focus of thi s CECAM workshop will be to bring together computational chemists\, materi als scientists and biophysicists with leaders in the mathematics of graphs and computational topology. The workshop will educate these communities\, foster collaboration and inspire development of both applied and fundamen tal computational methods in chemistry. We will focus upon each scale of i nformation relevant to computational chemistry (from electrons to the meso scale) and identify opportunities where graph theory and topology can help in method development and information transfer to accelerate interdiscipl inary innovation.\n \nFor example\, at the smallest scale of electrons\, continued development is needed for reduced-complexity electronic structur e methods (e.g.\, through effective Hamiltonians). There\, fundamental que stions remain about how to achieve the best parametrization\, what optim ization methods to employ\, how electronic data is represented\, and perh aps most importantly – how to maintain physical transparency through ste p-by-step coarse-graining that may use nonphysics neural network structure s to represent the data from electronic structure calculations.  As che mical complexity grows via molecular degrees of freedom\, chemical composi tion\, or diversity of intermolecular interactions\, the breadth of config uration ensembles can increase significantly - reflecting an increasingly rugged underlying potential energy landscape. Thus\, there is need to char acterize and predict spatial heterogeneities\, collective dynamics and the relation to energy landscape topology. This is important not only in samp ling\, but also for predictive models that seek to understand the relation ships between physicochemical properties and the configurational phase spa ce. Fundamentally\, such chemical insight can be greatly accelerated throu gh mathematical notions of distance (e.g.\, distances to compare the preci se graph combinatorial structure). This may include summaries of the spec tral structures of graph representations of the molecular system in combin ation with the energy landscape - such as the spectra of the graph Laplac e operator or the diffusion operator associated with input graphs. Recent advancements in topological data analysis also provide new ways to compare graph representation of high-dimensional data via persistent homology\, t hat have the potential to be adapted for chemical systems.\n \nThrough an organizational structure that combines use-case scenarios\, grand-challen ge talks and methodological and software tutorials (alongside poster prese ntations and roundtable discussions)\, this CECAM workshop will set the st age for increased collaboration between the applied math and computational sciences.\n\nReferences\n[1] A. Clark\, H. Adams\, R. Hernandez\, A. Kryl ov\, A. Niklasson\, S. Sarupria\, Y. Wang\, S. Wild\, Q. Yang\, ACS Cent. Sci.\, 7\, 1271-1287 (2021)\n[2] A. Clark\, P. Dral\, I. Tamblyn\, O. Isa yev\, Phys. Chem. Chem. Phys.\, 25\, 22563-22564 (2023)\n[3] Energy Lands capes: Applications to Clusters\, Biomolecules and Glasses\, Cambridge Mol ecular Science\, Cambridge UK (2004).\n[4] Y. Aboulfath\, S. Bougueroua\, A. Cimas\, D. Barth\, M. Gaigeot\, J. Chem. Theory Comput.\, 20\, 1019-10 35 (2024)\n[5] M. Witman\, S. Ling\, P. Boyd\, S. Barthel\, M. Haranczyk\, B. Slater\, B. Smit\, ACS Cent. Sci.\, 4\, 235-245 (2018)\n[6] T. Easley \, K. Freese\, E. Munch\, J. Bijsterbosch\, Comparing representations of h igh-dimensional data with persistent homology: a case study in neuroimagin g\, arXiv:2306.13802 LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103 STATUS:CONFIRMED END:VEVENT END:VCALENDAR