Credits: Mike Mackenzie

AI4Science Colloquium

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The AI4Science Colloquium is a bi-weekly colloquium series, held on alternating Tuesdays at 14:00 Central European Time. In this colloquium our very own Teodora Pandeva and Fiona Lippert invite renowned speakers to present and discuss their state-of-the-art AI solutions for scientific discovery. Interested? Subscribe to our Email-list to be notified.

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To stay up to date with our activities and be invited to our biweekly AI4Science colloquium series, you may send a request to be included in our emaillist via an email to us with your name, affiliation and a one-sentence motivation for joining.

Next Colloquium

Can artificial intelligence help understand and predict molecular dynamics?

Date: 22-06-2021 14:00-1500 Central European Summer time


Speaker: Pratyush Tiwary, Asst. Prof University of Maryland, Washington DC

The ability to rapidly learn from high-dimensional data to make reliable predictions about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing terabytes of data guiding complex human actions. Modern day artificial intelligence (AI) aims to mimic this fidelity and has been successful in many domains of life. It is tempting to ask if AI could also be used to understand and predict the dynamics of complex molecules with millions of atoms. In this talk I will show that certain flavors of AI can indeed help us understand generic molecular dynamics and also predict it even in situations with arbitrary long memories. However this requires close integration of AI with old and new ideas in statistical mechanics. I will talk about such methods developed by my group (1-3). I will demonstrate the methods on different problems, where we predict mechanisms at timescales much longer than milliseconds while keeping all-atom/femtosecond resolution. These include ligand dissociation from flexible protein/RNA and crystal nucleation with competing polymorphs. I will conclude by discussing some generic challenges and solutions regarding reliability, interpretability and extrapolative powers of AI when used to guide and complement simulations and perhaps even experiments in chemistry.


  1. Wang, Y., Ribeiro, J.M.L. & Tiwary, P. Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics. Nat. Commun. 10, 3573 (2019).
  2. Tsai, S.T, Kuo, E.J. & Tiwary, P. Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network. Nat. Commun. 11, 5115 (2020).
  3. Wang, Y., Ribeiro, J.M.L. and Tiwary, P. Machine learning approaches for analyzing and enhancing molecular dynamics simulations. Curr. Op. Sruc. Bio., 61, 139 (2020).


  • 19 January 2021 - Dim Coumou
  • 2 February 2021 - Mario Geiger
  • 16 February 2021 - Eliu Huerta Escudero
  • 2 March 2021 - Jakub Tomczak
  • 30 March 2021 - Benjamin Miller
  • 13 April 2021 - Ferry Hooft
  • 11 May 2021 - Chandler Squires
  • 25 May 2021 - Alexander Tkatchenko
  • 22 June 2021 - Pratyush Tiwary

Previous Colloquium

Title: On Electrons and Machine Learning Force Fields

Date: 25-05-2021 14:00-1500 Central European Summer time


Speaker: Alexander Tkatchenko, Professor of Physics, University of Luxembourg

Machine Learning Force Fields (MLFF) should be accurate, efficient, and applicable to molecules, materials, and interfaces thereof. The first step toward ensuring broad applicability and reliability of MLFFs requires a robust conceptual understanding of how to map interacting electrons to interacting “atoms”. Here I discuss two aspects: (1) how electronic interactions are mapped to atoms with a critique of the “electronic nearsightedness” principle, and (2) our developments of symmetry-adapted gradient-domain machine learning (sGDML) framework for MLFFs generally applicable for modeling of molecules, materials, and their interfaces. I highlight the key importance of bridging fundamental physical priors and conservation laws with the flexibility of non-linear ML regressors to achieve the challenging goal of constructing chemically-accurate force fields for a broad set of systems. Applications of sGDML will be presented for small and large (bio/DNA) molecules, pristine and realistic solids, and interfaces between molecules and 2D materials.

For more information, see references: Sci. Adv. 3, e1603015 (2017); Nat. Commun. 9, 3887 (2018); Comp. Phys. Comm. 240, 38 (2019); J. Chem. Phys. 150, 114102 (2019); Sci. Adv. 5, eaax0024 (2019).

Alexandre 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. Tkatchenko also holds a distinguished visiting professor position at the Berlin Big Data Centre (BBDC) of the Technical University of Berlin. His group develops accurate and efficient first-principles computational 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 250 invited talks, seminars and colloquia worldwide, published 180 articles in prestigious journals (h-index of 67 with more than 22,000 citations; Top 1% ISI highly cited researcher in 2018-2020), and serves on the editorial boards of Science Advances and Physical Review Letters. Tkatchenko has received a number of awards, including APS Fellow from the American Physical Society, 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 of ICNI-2021, and three flagship grants from the European Research Council: a Starting Grant in 2011, a Consolidator Grant in 2017, and Proof-of-Concept Grant in 2020.

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For an overview of more previous colloquia, please have a look at out blog.