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DTSTART;TZID=UTC:20260325T101500
DTEND;TZID=UTC:20260325T110000
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SUMMARY:Seminar by Volker Deringer
DESCRIPTION:Prof. Volker Deringer from the University of Oxford will deliver an invited seminar on Wednesday 25 March 2025 at 10:15-11:00 in hall A304 (Ke2) of the Aalto University School of Chemical Engineering main building\, Kemistintie 1\, Espoo. \nMachine-learned interatomic potentials for materials chemistry \nVolker Deringer \nDepartment of Chemistry\, University of Oxford \nMachine-learned interatomic potentials (MLIPs) are now widely used in atomistic simulations\, giving access to length and time scales that are otherwise inaccessible to first-principles methods. Their reliability in practice\, however\, depends crucially on the quality and coverage of the training data. In this talk\, I will discuss data-efficient approaches to constructing MLIPs\, including ML-accelerated first-principles molecular dynamics for generating reference configurations\, and automated de novo exploration of relevant configurational spaces using the autoplex software. I will also highlight model-distillation strategies (specifically\, a teacher–student approach) using accurate but computationally costly models to train fast\, task-specific MLIPs for downstream applications. I will illustrate these ideas using examples of structurally complex materials: amorphous silicon\, graphene oxide\, and phase-change materials that are used in data storage and neuromorphic computing.
URL:https://ocamm.fi/event/seminar-by-volker-deringer/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2023/09/volker-deringer.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250527T100000
DTEND;TZID=UTC:20250527T133000
DTSTAMP:20260510T232033
CREATED:20250519T105039Z
LAST-MODIFIED:20250519T105039Z
UID:384-1748340000-1748352600@ocamm.fi
SUMMARY:AI for Science: from molecules to materials
DESCRIPTION:The Aalto University House of AI organizes the first AI for Science event in the Otaniemi campus\, with the collaboration of OCAMM. The events serves to highlight the research carried out at Aalto at the interface between AI and the chemical sciences. For more information\, visit the official website of the event: https://www.aalto.fi/en/events/ai-4-science-from-molecules-to-materials
URL:https://ocamm.fi/event/ai-for-science-from-molecules-to-materials/
LOCATION:Undergraduate Center\, Otakaari 1\, Espoo\, Uusimaa\, 02150\, Finland
CATEGORIES:Networking,Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250507T131500
DTEND;TZID=UTC:20250507T140000
DTSTAMP:20260510T232033
CREATED:20250220T073202Z
LAST-MODIFIED:20250430T115351Z
UID:298-1746623700-1746626400@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Albert Bartók (Warwick)
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 7 May 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Albert Bartók\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nMaterials modelling across the scales \nAlbert P. Bartók \nSchool of Engineering and Department of Physics\, University of Warwick\, UK \nThe past two decades have seen a transformative change in atomistic modelling with the development of machine-learned interatomic potentials\, which allow quantum-accurate simulations at an affordable computational cost. While the formalism of these models has converged\, there still remain open questions about the optimal way to generate training databases as well as about the reliability of potentials. In this talk\, I will present our efforts to automatically generate atomic databases using a combination of active learning and advanced sampling methods and how the resulting potential results in exceptionally accurate potential energy surface for Mg at a pressure range of 0-600 GPa. I will also show how fast and accurate potentials can help us discover novel phenomena\, illustrated by our observation on how helium affects dislocation mobility in tungsten. Finally\, I will report how transfer learning may be used to fine-tune foundation models using a little amount of data\, resulting in accurate\, but application-specific potentials. \nAbout the speaker\nAlbert Bartók-Pártay is an Associate Professor at the University of Warwick. He earned his Ph.D. degree in physics from the University of Cambridge in 2010\, his research having been on developing interatomic potentials based on ab initio data using machine learning. He was a Junior Research Fellow at Magdalene College\, Cambridge\, and later a Leverhulme Early Career Fellow. Before taking up his current position\, he was a Research Scientist at the Science and Technology Facilities Council. His research focuses on developing theoretical and computational tools to understand atomistic processes.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-albert-bartok/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ocamm.fi/wp-content/uploads/2025/02/ABartokPartay.png
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250416T131500
DTEND;TZID=UTC:20250416T140000
DTSTAMP:20260510T232033
CREATED:20250220T073014Z
LAST-MODIFIED:20250414T112150Z
UID:296-1744809300-1744812000@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Leonardo Espinosa (VTT)
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 16 April 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Leonardo Espinosa\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nMachine Learning Across Industries: From Academia to Real Applications \nLeonardo A. Espinosa-Leal \nPrincipal Scientist\, ProperTune AI Team\, VTT\, Espoo\, Finland \nIt is not a novelty to hear that machine learning is everywhere. Since you wake up until you go to sleep\, your life and decisions are driven (and in some cases controlled) through the interaction with machine learning algorithms. Despite their ubiquitousness and the constant buzz about AI and the need for digitalization across industries\, it is very interesting that most ML projects in the sector (60% to 80%\, depending on the area) never reach the deployment phase. In this talk\, we will discuss\, using real (successful and unsuccessful) examples from some Finnish industry players\, how “AI” is presented in academic environments and how to address the gap between the academy and the industry. In addition\, a general view of VTT’s strategy for materials discovery\, including those concepts\, will be presented and discussed. \nAbout the speaker\nLeonardo holds a BSc in physics from the National University of Colombia\, a Master in Nanoscience\, and a PhD in Computational Materials Science from the University of the Basque Country in Spain (2013). He moved to Finland in 2013 as a postdoc at Aalto University and\, in 2016\, decided to switch his research interests towards the world of machine learning and artificial intelligence. He got a Research fellowship (2017) at Arcada University of Applied Sciences (Finland)\, then was appointed Senior lecturer in Big Data Analytics (2020) and later\, Principal lecturer in Technology (2023). He was the head of the Applied Artificial Intelligence Laboratory and the Degree Programme Director of the Master in Big Data Analytics at the same institution from 2022 until 2025. \nIn 2025\, Leonardo was appointed as Principal Scientist at VTT in the ProperTune AI team. Currently\, His main interest is (but not limited to) the intersection of AI\, Materials Science\, and Quantum computing.
URL:https://ocamm.fi/event/ai-in-chem-leonardo-espinosa-vtt/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250326T131500
DTEND;TZID=UTC:20250326T140000
DTSTAMP:20260510T232033
CREATED:20250220T072711Z
LAST-MODIFIED:20250325T091539Z
UID:293-1742994900-1742997600@ocamm.fi
SUMMARY:CANCELED: AI in CHEM Seminar Series: Erik Berg (Uppsala)
DESCRIPTION:Unfortunately\, this seminar has been canceled due to unforeseeable circumstances. \nThis talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 26 March 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Erik Berg\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nA Self-driving Battery Research Lab \nJackie Yik\, Viktor Vanoppen\, Leiting Zhang\, Erik J. Berg \nDepartment of Chemistry\, Ångström Laboratory\, Uppsala University\, Box 538\, SE-751 21\, Uppsala\, Sweden \nThe need for efficient and sustainable energy storage solutions has never been greater. In our battery research lab\, we address this urgency by developing a self-driving battery electrolyte formulation and analysis platform. In my talk\, I will present a robotic setup dedicated to battery electrolyte formulation\, coin-cell assembly and electrochemical testing. The development trajectory of the setup is presented along with a discussion of the pros/cons of various robot configurations. The integration of an active learning cycle in form of multi-objective optimization to identify the most highly performing electrolytes is showcased. Failing experiments as a result of automation will also be exemplified. Finally\, the need to educate next-generation chemists in the field of automation and to standardize chemical instrumentation and testing protocols is highlighted. \nAbout the speaker\nErik J. Berg is since 2021 Professor of Chemistry at Uppsala University-Sweden. He holds a MSc Physics degree from TU Darmstadt-Germany\, an Engineering Physics degree from the Royal Institute of Technology-Sweden in 2007 and earned in 2012 a Ph.D. at Uppsala University. He joined the Paul Scherrer Institute\, Switzerland as post-doctoral fellow in 2012 where he later was also promoted to Group Leader in 2016\, awarded tenure in 2017\, before returning to Uppsala University in 2018. He is currently a Knut and Alice Wallenberg Academy Fellow and SSF Future Research Leader. Erik’s research focuses since >10 years on fundamental mechanistic understanding of the chemistry governing the performance of rechargeable batteries. His research team primarily develops and applies operando characterization techniques to study battery dis-/charge processes in real-time\, often in close collaboration with industrial funding partners. Recently\, significant effort is invested in automating the battery research process with the aim to accelerate the discovery of high-performing electrolytes
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-erik-berg-uppsala/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2025/02/erik_berg.jpeg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250312T131500
DTEND;TZID=UTC:20250312T140000
DTSTAMP:20260510T232033
CREATED:20250220T072504Z
LAST-MODIFIED:20250307T104449Z
UID:290-1741785300-1741788000@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Erin Makara (VTT)
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 12 March 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Erin Makara\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nML-Assisted Material Simulation and Exploration: Moving Past Symmetry \nErin Makara\, VTT \nSimulating asymmetrical and amorphous materials with diverse elemental compositions presents significant challenges due to their large system sizes and the inability to leverage symmetry-based simplifications. Conventional computational methods struggle to efficiently model these high-entropy materials\, however advances in machine-learned force fields (MLFFs) have provided a promising avenue for accelerating simulations while maintaining accuracy. This talk will discuss how ML-accelerated electronic and dynamic calculations enable the exploration of amorphous materials. It will discuss considerations and workflows for generating training data for the ML models\, as well as discussion on validity of the approach through the lens of accuracy and time. \nAbout the speaker\nErin Makara has found themselves at the crossroads of physics\, chemistry\, mathematics\, and computer science\, working towards advancing computational methods beyond their current limitations. They are currently a Research Scientist at Technical Research Centre of Finland VTT and a PhD student at Aalto University under the guidance of Dr. Anssi Laukkanen and Prof. Antti Karttunen. Erin holds a Master’s degree in Chemistry and is working alongside the PhD on a second Master’s degree in Machine Learning\, Data Science and Artifical Intellignece. Alongside research and studies\, they work to simulate various molecules and materials and predict their properties to aid in their team’s multiscale simulation effort.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-erin-makara-vtt/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250226T131500
DTEND;TZID=UTC:20250226T140000
DTSTAMP:20260510T232033
CREATED:20250220T072132Z
LAST-MODIFIED:20250220T072132Z
UID:288-1740575700-1740578400@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Drug Discovery with Heikki Käsnänen (Orion Pharma)
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 26 February 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Heikki Käsnänen\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nTransforming Drug Discovery: From Computational Tools to Model-Driven Innovation \nHeikki Käsnänen\, Orion Pharma \nThe pharmaceutical industry is undergoing a paradigm shift in how computational methods and models are integrated into the drug discovery process. Historically\, these tools played a supportive yet impactful role\, assisting chemists and biologists in decision-making and innovation. Today\, advancements in predictive and generative AI/ML\, the scaling of physics-based methods\, and the emergence of active learning are propelling us toward a future of truly model-driven drug discovery. This talk will explore how virtual DMTA (Design\, Make\, Test\, Analyze) cycles\, accelerated by AI/ML\, are transforming R&D workflows\, enabling faster and more efficient exploration of chemical space. It will also discuss the challenges and opportunities in scaling physics-based methods for broader applicability and how active learning can enhance the adoption of slower but more accurate computational approaches. While the vision of fully model-driven drug discovery is not yet realized\, this transition marks an exciting new era for innovation and impact in the pharmaceutical industry. \nAbout the speaker\nDr. Heikki Käsnänen describes himself as a digital drug hunter\, working at the intersection of computation\, chemistry\, and biology. He currently leads a research group at Orion Pharma\, focusing on hit discovery and the application of computational chemistry and AI/ML methods in small molecule drug discovery. Heikki holds a Master’s degree in Pharmacy from the University of Kuopio and completed his PhD in Computational Medicinal Chemistry under the guidance of Professor Antti Poso. In 2011\, while finalizing his doctoral studies\, he was invited to join Orion\, where he has since gained over a decade of experience in small molecule drug discovery\, with a particular focus on oncology and pain targets. Since 2020\, Heikki has led the Molecular Prospecting and Modeling unit\, driving innovation and advancing model-driven discovery alongside a talented team of colleagues.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-drug-discovery-with-heikki-kasnanen-orion-pharma/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250121T100000
DTEND;TZID=UTC:20250121T110000
DTSTAMP:20260510T232033
CREATED:20250110T105843Z
LAST-MODIFIED:20250110T105843Z
UID:276-1737453600-1737457200@ocamm.fi
SUMMARY:Special seminar by Prof. Alexandre Tkatchenko
DESCRIPTION:Save the date! OCAMM presents a special seminar by Prof. Alexandre Tkatchenko from the University of Luxembourg\, who will talk about the role of AI in accelerating molecular simulations. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 21 January 2025 @ 10:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to find out how AI can be leveraged to speed up quantum mechanical simulations of atoms and molecules! \nTitle \nRealizing Schrödinger’s dream with AI-enabled molecular simulations \nAbout the speaker \nAlexandre Tkatchenko is a professor at the Department of Physics and Materials Science (and head of this department since January 2020) at the University of Luxembourg\, where he holds a chair in Theoretical Chemical Physics composed of ~35 multidisciplinary scientists. Tkatchenko also holds a distinguished visiting professor position at the Technical University of Berlin. His group develops accurate and efficient first-principles computational and artificial intelligence models to study a wide range of complex materials\, aiming at qualitative understanding and quantitative prediction of their structural\, cohesive\, electronic\, and optical properties at the atomic scale and beyond. He has delivered more than 450 invited talks\, seminars\, and colloquia worldwide\, published 240 articles in prestigious journals (h-index of 89 with more than 45\,000 citations; Top 1% ISI highly cited researcher since 2018 until now)\, and serves on the editorial boards of four society journals: Science Advances (AAAS)\, Physical Review Letters (APS)\, Journal of Physical Chemistry Letters (ACS)\, and Chemical Science (RSC). Tkatchenko has received a number of awards\, including APS Fellow from the American Physical Society\, Fellow of the Royal Society of Chemistry\, Gerhard Ertl Young Investigator Award of the German Physical Society\, Dirac Medal from the World Association of Theoretical and Computational Chemists (WATOC)\, van der Waals prize from ICNI\, Feynman Prize for Nanotechnology from the Foresight Institute\, and five flagship grants from the European Research Council (ERC): a Starting Grant in 2011\, a Consolidator Grant in 2017\, an Advanced Grant in 2022\, and Proof-of-Concept Grants in 2020 and 2023. He is also a co-founder of Quastify GmbH – a start-up that combines quantum and statistical mechanics with machine learning for efficiently exploring chemical spaces.
URL:https://ocamm.fi/event/special-seminar-by-prof-alexandre-tkatchenko/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2025/01/deeplearn-tkatchenko.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250115T131500
DTEND;TZID=UTC:20250115T140000
DTSTAMP:20260510T232033
CREATED:20250110T104214Z
LAST-MODIFIED:20250110T105327Z
UID:273-1736946900-1736949600@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Lab Automation for Scattering Analysis with Andy Anker
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 15 January 2025 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Andy Anker\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nMachine learning experimental scattering data analysis: concept\, practice\, and a future with automated laboratories \nAndy S. Anker1\,2 \n\nDepartment of Energy\, Danish Technical University\, Denmark\, ansoan@dtu.dk\nDepartment of Chemistry\, University of Oxford\, United Kingdom\, andy.anker@chem.ox.ac.uk\n\nThe rapid growth of materials chemistry data has outpaced conventional data analysis and modelling methods\, which can require enormous manual effort. To effectively analyse this wealth of information\, we are using machine learning (ML) models trained on extensive datasets of physics-based simulations for analysis of experimental scattering data [1\,2]. Yet\, relying solely on a single experimental technique often fails to provide sufficient information for resolving complex material structures. To overcome these limitations\, we are integrating diverse datasets into unified ML pipelines. Building on these methodological advances\, we look towards developing automated laboratories capable of accelerating materials synthesis. \nReferences \n\nAndy S. Anker\, Keith T. Butler\, Raghavendra Selvan\, Kirsten M. Ø. Jensen\, Chemical Science 2024\, 48\, 14003–14019.\nEmil T. S. Kjær\, Andy S. Anker\, Marcus N. Weng\, Simon J. L. Billinge\, Raghavendra Selvan\, Kirsten M. Ø. Jensen\, Digital Discovery 2023\, 1\, 69–80.\n\nAbout the speaker\nSee Andy’s Github page for more info \nI have recently been awarded a 4 000 000 DKK (~ £500 000) postdoctoral grant to pursue an academic career in the interface of materials chemistry\, machine learning and robotics. Here\, I am building a self-driving laboratory for controlled synthesis of inorganic nanomaterials in collaboration with Prof. Tejs Vegge and the CAPeX center at Technical University of Denmark\, Assoc. Prof. Volker Deringer’s group at Oxford University and Prof. Kasper Støy’s group at the IT University of Copenhagen. In 2024+2025\, I am physically working from Oxford. \nI obtained my PhD in materials chemistry from the Nanostructure Group UPCH\, University of Copenhagen\, supervised by Assoc. Prof. Kirsten Marie Ørnsbjerg Jensen\, where my main interest was to study nanoparticles and structures in solution with Total X-ray Scattering with Pair Distribution Function (PDF) and Small-Angle X-ray Scattering (SAXS). I applied advanced computer modelling\, in Python\, to combine information of both the local order from PDF and the particle order from SAXS\, which overcome problems that the methods cannot overcome individually. During my career\, the research focus has converged towards developing machine learning (ML) methods to analyse chemical data; especially PDF & SAXS\, after I met Assistant Professor Raghavendra Selvan who I had collaborated with since 2019. I have furthermore spent 6 months during my PhD working at Rutherford Appleton Laboratory with Senior Lecturer Keith Tobias Butler and the Scientific Machine Learning Group to develop an general approach to match simulated and experimental data in materials chemistry. During the last period of my PhD\, I have especially focused on using generative models to analyse scattering-\, and spectroscopy data.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-lab-automation-for-scattering-analysis-with-andy-anker/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2025/01/AndySAnker_Portraet-web-e1736506396902.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20241127T131500
DTEND;TZID=Europe/Helsinki:20241127T140000
DTSTAMP:20260510T232033
CREATED:20241125T075043Z
LAST-MODIFIED:20241127T081310Z
UID:262-1732713300-1732716000@ocamm.fi
SUMMARY:AI in CHEM Seminar Series: Intro session with Miguel Caro
DESCRIPTION:This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments. \nDate and location\n\nWednesday 27 November 2024 @ 13:15-14:00\nA304 Ke2 lecture hall in the main building of the School of Chemical Engineering\, Kemistintie 1\, 02150 Espoo.\n\nAgenda\n\n13:00-13:15. Setup and brief info for the registered students.\n13:15-14:00. Seminar by Miguel Caro\, lecture hall A304.\n14:00-onwards. Coffee\, netwoking and mingling in the lobby adjacent to the lecture hall.\n\nSeminar info\nAchieving a new degree of realism in materials modeling with machine learning \nWe are in the middle of an AI revolution in all aspects of society. However\, machine learning had already been trending in chemistry and materials science for a few years before ChatGPT popularized the use of AI among the general public. After a brief introduction to the field of AI in chemical research\, and links to the upcoming talks in the Seminar Series\, I will give some examples\, from our own group’s research activities\, showcasing the use of atomistic machine learning to achieve a degree of realism in materials modeling that was previously out of reach. \nAbout the speaker\nMiguel Caro is Senior Scientist at the Department of Chemistry and Materials Science\, Aalto University\, as well as main organizer of the AI in CHEM Seminar Series and teacher in charge of course CHEM-E4190. For more info\, visit miguelcaro.org.
URL:https://ocamm.fi/event/ai-in-chem-seminar-series-intro-session-with-miguel-caro/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20241101T131500
DTEND;TZID=UTC:20241101T140000
DTSTAMP:20260510T232033
CREATED:20241023T133256Z
LAST-MODIFIED:20241023T133256Z
UID:253-1730466900-1730469600@ocamm.fi
SUMMARY:Special seminar by Dr. Konstantinos Konstantinou
DESCRIPTION:Save the date! OCAMM presents a special seminar by Dr. Konstantinos Konstantinou from the University of Turku\, who will talk about atomistic simulations of phase-change materials for aerospace applications. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 1 November 2024 @ 13:15 in lecture hall D311 (Ke5) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to find out how order/disorder transitions in a material can be used to construct nanoscale computer memories! \nTitle \nNon-volatile phase-change memory for spaceship application \nAbstract \nRadiation-hard non-volatile memories are in high demand by the space community for implementation in microcontrollers and solid-state data recorders. In phase-change memories\, binary data are represented as changes in structural phase rather than by stored electrical charge; thus\, these devices are supposed to be tolerant to ionizing radiation effects. Ion irradiation corresponds to a process that involves the production of non-equilibrium cascades in the host material\, and the atomistic modelling of such events in glasses is challenging. Here\, radiation damage in amorphous Ge2Sb2Te5 phase-change memory material is modelled by carrying out thermal-spike simulations with ab initio molecular-dynamics calculations. A stochastic boundary-conditions approach is employed to treat the thermal nature of the cascades and drive the modelled system back to equilibrium in a natural way. The dynamics of the cascade evolution shows that the time-scale of the ballistic phase of the cascade inside the glass model is very short. Investigation of the atomic geometry highlights a structural recovery from the damage imposed during ion irradiation\, since the glass manages to maintain its amorphous network. Analysis of the bonding for all the species in the glass structure reveals particular structural modifications in the local atomic environments and the connectivity of the amorphous network. Overall\, the simulations manifest the remarkable ability of Ge2Sb2Te5 phase-change memory material to be radiation-tolerant\, hence indicating its potential applications in future space and other radiation-present environments. \nAbout the speaker \nAfter completing his MSc degree in Computational Physics at the Aristotle University of Thessaloniki in Greece\, Konstantinous moved to the UK to obtain a PhD in Physics from University College London. He then joined the University of Cambridge as a research associate in Chemistry. Konstantinos came to Finland in 2020 as postdoctoral researcher in Tampere University before joining the University of Turku\, where he currently holds the prestigious Academy Fellow position. His current research interests include defects in amorphous semiconductors\, resistive switching memories\, machine-learned molecular-dynamics simulations\, charge trapping processes\, and electronic excitations\, among others.
URL:https://ocamm.fi/event/special-seminar-by-dr-konstantinos-konstantinou/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2024/10/konstantinos.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240318T141500
DTEND;TZID=UTC:20240318T151500
DTSTAMP:20260510T232033
CREATED:20230907T120034Z
LAST-MODIFIED:20240318T083359Z
UID:115-1710771300-1710774900@ocamm.fi
SUMMARY:Seminar by Prof. Volker Deringer on machine-learning-based simulation of amorphous materials
DESCRIPTION:OCAMM presents a special seminar by Prof. Volker Deringer from the University of Oxford\, UK on machine-learning-based simulation of amorphous materials. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 18 March 2024 @ 14:15 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to delve into the intricate atomic structure and fascinating properties of amorphous materials! \nTitle \nMachine-learning-driven advances in modelling amorphous materials \nAbstract \nUnderstanding the connections between the atomic-scale structure of materials and their macroscopic properties is among the most important research challenges in solid-state and materials chemistry. Atomistic simulations based on quantum-mechanical methods have played a key role in this – but they are computationally demanding\, and therefore they will inevitably reach their limits when materials with highly complex structures are to be studied. Machine learning (ML) based interatomic potentials are a rapidly emerging approach that helps to overcome this limitation: being “trained” on a suitably chosen set of quantummechanical data\, ML potentials achieve comparable accuracy whilst giving access to much larger-scale simulations – with thousands or even millions of atoms.\nIn this presentation\, I will showcase some recent advances in the modelling and understanding of inorganic materials that have been enabled by ML-driven simulations. I will argue that ML potentials are particularly useful for modelling non-crystalline (amorphous) structures that are difficult to characterise experimentally. I will survey recent work ranging from structural transitions in amorphous silicon [1] to multicomponent systems – specifically\, chalcogenide phase-change materials used in digital data storage [2]. I will also discuss methodological aspects\, including a perspective for using large synthetic datasets to pre-train neural-network potentials which can subsequently be fine-tuned on quantum-mechanical data [3]. The development of new\, accurate and efficient atomistic ML models promises a way to more fully understand the structure and properties of amorphous materials on the atomic scale.\n[1] V. L. Deringer\, N. Bernstein\, G. Csányi\, C. Ben Mahmoud\, M. Ceriotti\, M. Wilson\, D. A. Drabold\, S. R. Elliott\, Nature 2021\, 589\, 59.\n[2] Y. Zhou\, W. Zhang\, E. Ma\, V. L. Deringer\, Nat. Electron. 2023\, 6\, 746.\n[3] J. L. A. Gardner\, K. T. Baker\, V. L. Deringer\, Mach. Learn.: Sci. Technol. 2024\, 5\, 015003.
URL:https://ocamm.fi/event/seminar-by-prof-volker-deringer-on-machine-learning-based-simulation-of-carbon-materials/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2023/09/volker-deringer.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240311T100000
DTEND;TZID=UTC:20240311T110000
DTSTAMP:20260510T232033
CREATED:20230907T121250Z
LAST-MODIFIED:20240124T131104Z
UID:118-1710151200-1710154800@ocamm.fi
SUMMARY:Seminar by Prof. Janine George on automation and workflows in atomistic simulation
DESCRIPTION:Save the date! OCAMM presents a special seminar by Prof. Janine George on automation and workflows in atomistic simulation. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 11 March 2024 @ 10:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn more about how high-throughput calculations of molecules and materials can be made efficient and tractable! \nTitle\nData-Driven Chemical Understanding with Bonding Analysis \nAbstract\nBonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties [1]. Implementations and algorithms (e.g.\, ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available [2\,3]. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale [3\,4]. The lecture will demonstrate how our tools\, that assess local atomic environments and perform automatic bonding analysis\, help to develop new machine learning models and a new intuitive understanding of materials [5\,6]. Furthermore\, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed [7–10]. \nReferences \n\nJ. George\, G. Hautier\, Trends Chem. 2021\, 3\, 86–95.\nD. Waroquiers\, J. George\, M. Horton\, S. Schenk\, K. A. Persson\, G.-M. Rignanese\, X. Gonze\, G. Hautier\, Acta Cryst B 2020\, 76\, 683–695.\nJ. George\, G. Petretto\, A. Naik\, M. Esters\, A. J. Jackson\, R. Nelson\, R. Dronskowski\, G.-M. Rignanese\, G. Hautier\, ChemPlusChem 2022\, 87\, e202200123.\n“LobsterPy\,” can be found under https://github.com/JaGeo/LobsterPy\, 2022.\nA. A. Naik\, C. Ertural\, N. Dhamrait\, P. Benner\, J. George\, Sci Data 2023\, 10\, 610.\nK. Ueltzen\, A. Naik\, C. Ertural\, P. Benner\, J. George\, Article in Preparation 2024.\nJ. George\, Trends Chem. 2021\, 3\, 697–699.\nA. Ganose\, et al.\, “atomate2\,” can be found under https://github.com/materialsproject/atomate2\, 2023.\nA. S. Rosen\, M. Gallant\, J. George\, J. Riebesell\, H. Sahasrabuddhe\, J.-X. Shen\, M. Wen\, M. L. Evans\, G. Petretto\, D. Waroquiers\, G.-M. Rignanese\, K. A. Persson\, A. Jain\, A. M. Ganose\, Journal of Open Source Software 2024\, 9\, 5995.\nI. Batatia\, P. Benner\, Y. Chiang\, A. M. Elena\, D. P. Kovács\, J. Riebesell\, X. R. Advincula\, M. Asta\, W. J. Baldwin\, N. Bernstein\, A. Bhowmik\, S. M. Blau\, V. Cărare\, J. P. Darby\, S. De\, F. Della Pia\, V. L. Deringer\, R. Elijošius\, Z. El-Machachi\, E. Fako\, A. C. Ferrari\, A. Genreith-Schriever\, J. George\, R. E. A. Goodall\, C. P. Grey\, S. Han\, W. Handley\, H. H. Heenen\, K. Hermansson\, C. Holm\, J. Jaafar\, S. Hofmann\, K. S. Jakob\, H. Jung\, V. Kapil\, A. D. Kaplan\, N. Karimitari\, N. Kroupa\, J. Kullgren\, M. C. Kuner\, D. Kuryla\, G. Liepuoniute\, J. T. Margraf\, I.-B. Magdău\, A. Michaelides\, J. H. Moore\, A. A. Naik\, S. P. Niblett\, S. W. Norwood\, N. O’Neill\, C. Ortner\, K. A. Persson\, K. Reuter\, A. S. Rosen\, L. L. Schaaf\, C. Schran\, E. Sivonxay\, T. K. Stenczel\, V. Svahn\, C. Sutton\, C. van der Oord\, E. Varga-Umbrich\, T. Vegge\, M. Vondrák\, Y. Wang\, W. C. Witt\, F. Zills\, G. Csányi\, 2023\, DOI 10.48550/arXiv.2401.00096.\n\nAbout the speaker\nJanine George received her Bachelor of Science in Chemistry and Master of Science (summa cum laude) also in Chemistry\, both from RWTH Aachen University\, in 2011 and 2013\, respectively. She then obtained a Doctorate (Dr. rer. nat\, summa cum laude) in Computational Solid-State Chemistry under the supervision of Prof. Richard Dronskowski\, RWTH Aachen University in 2017. During 2018-2021 she held a Post-Doc position in the groups of Prof. Geoffroy Hautier at the Université catholique de Louvain (now at Darthmouth College) and Prof. Gian-Marco Rignanese also at the Université catholique de Louvain. Since 2021 she is Junior Group Leader of the Group “Computational Materials Design” at the Federal Institute for Materials Research and Testing (Department Materials Chemistry) in Berlin and\, since 2023\, she holds a joint appointment as Professor for Materials Informatics between the latter and the FSU Jena (Institute of Condensed Matter Theory and Optics).
URL:https://ocamm.fi/event/seminar-by-dr-janine-george-on-automation-and-workflows-in-atomistic-simulation/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2023/09/janine.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240207T141500
DTEND;TZID=UTC:20240207T151500
DTSTAMP:20260510T232033
CREATED:20240131T134025Z
LAST-MODIFIED:20240131T153714Z
UID:169-1707315300-1707318900@ocamm.fi
SUMMARY:Seminar by Tamas Stenczel on machine-learning accelerated ab-initio molecular dynamics
DESCRIPTION:Save the date! OCAMM presents a special seminar by Tamas Stenczel on accelerating ab initio molecular dynamics with on-the-fly machine learning. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 7 February 2024 @ 14:15 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn more about how you can make molecular dynamics orders of magnitude faster while retaining ab initio accuracy! \nTitle\nML Acceleration for Dynamics in Ab-Initio Modelling \nAbstract\nShowcase of existing methods and ongoing work on accelerating various dynamical simulations in ab-initio codes with modern ML. Replacing the ab-initio energy model where possible with an on-the-fly updated ML (GAP/ACE/MACE) one\, we will visit methods and frameworks making materials science & chemical ML models easy to access for all modelling needs. Next generation methods\, frameworks\, scientific challenges\, and opportunities for using and contributing to efforts in the field will be presented\, starting from recently published advances in the CASTEP code\, going to a general framework currently being implemented in a range of simulation tools. \nAbout the speaker\nTamás K. Stenczel is a researcher at the University of Cambridge\, with ties in academia and in various industrial realms. Amongst heading a research and software team at a family office\, conducting open academic research\, and everyday life\, he is a skiier and biker\, always striving to see and understand more of the world.
URL:https://ocamm.fi/event/seminar-by-tamas-stenczel-on-machine-learning-accelerated-ab-initio-molecular-dynamics/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2024/01/tamas.jpeg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20231113T140000
DTEND;TZID=Europe/Helsinki:20231113T150000
DTSTAMP:20260510T232033
CREATED:20230907T113618Z
LAST-MODIFIED:20230907T115837Z
UID:110-1699884000-1699887600@ocamm.fi
SUMMARY:Seminar by Prof. Rocío Mercado on artificial intelligence in biomolecular modeling
DESCRIPTION:OCAMM presents a special seminar by Prof. Rocío Mercado from Chalmers University of Technology\, Sweden on “Deep generative models for biomolecular engineering“. This invited seminar will take place at the Department of Chemistry and Materials Science\, Aalto University on 13 November 2023 @ 14:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus\, Kemistintie 1\, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi for a great talk on how artificial intelligence and molecular modeling are impacting the fields of biomolecular design and drug discovery. \nTitle\nDeep generative models for biomolecular engineering \nAbstract\nAI is transforming our approach to molecular engineering. Driven by the goal of accelerating drug development\, our aim is to develop AI-driven molecular engineering methods which will enhance our approach to biomolecular discovery\, such as drug discovery\, drug repurposing\, and chemical probe identification. This entails the development of generative and predictive tools that can learn from biochemical data\, such as molecular structures\, chemical reactions\, and biomedical data. While AI can be applied to a range of molecular engineering tasks\, one ideal area is de novo molecular design. De novo design is the concept of designing molecules with desired properties from scratch so as to minimize experimental screening\, and is poised to allow scientists to more efficiently traverse chemical space in search of optimal molecules\, and delegate error-prone decisions to computers via the use of predictive and generative models. In drug development\, de novo design methods can aid medicinal chemists in the design and selection of drug candidates\, with the added advantage that they can learn from datasets of billions of molecules in minutes and be constantly updated with new data. Deep molecular generative models are a particular approach to de novo design which uses deep neural networks to generate new molecules in silico\, and works by proposing atom-by-atom (or fragment-by-fragment) modifications to an initial graph structure to generate compounds predicted to achieve a certain property profile. Such models can be applied to a range of therapeutic modalities. \nIn this talk\, I will discuss the development of deep generative models for various molecular engineering tasks relevant to early-stage drug discovery. These include a model for synthesizability-constrained molecular generation\, a reinforcement learning framework for molecular graph optimization\, and recent applications from our group to the design of large modalities for targeted protein degradation. \nAbout the speaker\nRocío is a tenure-track assistant professor in the Data Science and AI division at Chalmers since January 2023. She heads the AI Laboratory for Biomolecular Engineering (AIBE) in the Department of Computer Science and Engineering. \nPreviously\, she was a postdoctoral associate in the Coley group at MIT\, as well as an industrial postdoc in the Molecular AI team at AstraZeneca. Throughout her postdoctoral career\, she worked on the development of deep generative models for small molecule drug discovery. Before AstraZeneca\, she was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. She received her PhD in Chemistry from UC Berkeley in August 2018\, and her BSc in Chemistry from Caltech in June 2013.
URL:https://ocamm.fi/event/seminar-by-prof-rocio-mercado-on-artificial-intelligence-in-biomolecular-modeling/
LOCATION:Aalto University\, School of Chemical Engineering\, Kemistintie 1\, Kemistintie 1\, Espoo\, 02150\, Finland
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ocamm.fi/wp-content/uploads/2023/09/rocio-scaled.jpg
ORGANIZER;CN="Miguel Caro":MAILTO:miguel.caro@aalto.fi
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END:VCALENDAR