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Sackler-CECAM school and workshop on Frontiers in Molecular Dynamics: Machine Learning, Deep Learning and Coarse Graining
From Monday 08 October 2018
To Friday 12 October 2018
Contact Yael Yogev (

School date: Oct 8th - Oct 9th

Workshop date: Oct 10th - Oct 11th

Registration is now open - please press HERE.


Workshop's Poster

Workshop Description









Amir Natan (Tel-Aviv University, Israel)
Yair Shokef (Tel Aviv University, Israel)
Rickard Armiento (Linköping University, Sweden)

Workshop's Poster

 The workshop poster is available here.


Workshop Description

The calculation of the atomistic dynamics of large systems is an important challenge in many fields. The use of classical force fields (FF) [1-2], where specific physical models are built to describe the interaction between atoms and molecules, is a very successful approach that allows a realistic description of forces in large systems at a very low computational cost. While such FF models have been very useful to describe large systems they have several limitations – first - the model for the forces might not be accurate at all configurations, second – chemical reactions in the system or radical changes in the atomic environment are almost impossible to describe with a simple classical FF. In recent years, more sophisticated FF schemes, such as variable charge FF [3], ReaxFF [4-5], COMB [6-7], and others, have appeared. Such FF include a very large set of parameters that can successfully describe a much richer set of cases, this approach was successful in describing complicated system such as surface oxidation, chemical reactions and many others. The limitation of the more sophisticated force fields is that they are still tailored to a limited set of configurations (although much wider) and in addition, a heavy parameterization is now needed. A different approach is building “on the fly” schemes for forces estimation, such schemes use quantum calculations as a training set to estimate the forces. Machine Learning (ML) [8-10] and Deep Learning (DL) [11-13] were shown in recent years to successfully predict energies, forces and electronic properties with a reasonable size datasets that are updated on the fly. A good prediction of forces can lead to an ab-initio accuracy molecular dynamics (MD) at a computational cost that although higher than classical FF is still much faster than fully ab-initio MD. Several groups have achieved such implementations and although this is not in common use of general purpose MD simulations, there is a great promise for future applications and a growing community. Another important aspect of modern MD is coarse graining, enabling to reach even larger systems and to approach mesoscopic and continuum scales. While this seems like a completely different area, there might be future interaction as the modeling of long range forces and coarse graining are somewhat related.

The goal of this 2.5days workshop is to have a meeting of some of the leading researchers in the field to discuss the impact of both machine learning and deep learning on molecular dynamics techniques. We invite also researchers that develop coarse graining techniques and large scale soft matter simulations. The workshop will include lectures of the researchers, discussion panels and a poster session. Prior to the workshop we intend to have a 2 days school with more basic lectures in Machine Learning and Deep Learning in the context of materials research and molecular dynamics.

In the workshop, we would like to compare state of the art machine learning and deep learning approaches. We would also like to check how close are those methods to be able to simulate large and complicated systems.

We have invited some of the leading researchers in this field and plan to invite additional researchers that do ML and DL in materials science.

School dates: Oct 8th - Oct 9th

Workshop dates Oct 10th - Oct 12th.

[1] Understanding Molecular Simulation: From Algorithms to Applications; Daan Frenkel, Berend Smit, Academic Press 2001

[2] Computer Simulation of Liquids, M. P. Allen, D. J. Tildesley, Clarendon Press, 1989

[3] Streitz, F. H., and J. W. Mintmire. "Electrostatic potentials for metal-oxide surfaces and interfaces." Physical Review B 50, no. 16 (1994): 11996.

[4] Van Duin, Adri CT, Siddharth Dasgupta, Francois Lorant, and William A. Goddard. "ReaxFF: a reactive force field for hydrocarbons." The Journal of Physical Chemistry A 105, no. 41 (2001): 9396-9409

[5] Aryanpour, Masoud, Adri CT van Duin, and James D. Kubicki. "Development of a Reactive Force Field for Iron− Oxyhydroxide Systems." The Journal of Physical Chemistry A 114, no. 21 (2010): 6298-6307.

[6] Liang, Tao, Bryce Devine, Simon R. Phillpot, and Susan B. Sinnott. "Variable charge reactive potential for hydrocarbons to simulate organic-copper interactions." The Journal of Physical Chemistry A 116, no. 30 (2012): 7976-7991

[7] Cheng, Yu-Ting, Tzu-Ray Shan, Tao Liang, Rakesh K. Behera, Simon R. Phillpot, and Susan B. Sinnott. "A charge optimized many-body (comb) potential for titanium and titania." Journal of Physics: Condensed Matter 26, no. 31 (2014): 315007

[8] Rupp, Matthias, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole Von Lilienfeld. "Fast and accurate modeling of molecular atomization energies with machine learning." Physical review letters 108, no. 5 (2012): 058301.

[9] Botu, Venkatesh, and Rampi Ramprasad. "Adaptive machine learning framework to accelerate ab initio molecular dynamics." International Journal of Quantum Chemistry 115, no. 16 (2015): 1074-1083.

[10] Li, Zhenwei, James R. Kermode, and Alessandro De Vita. "Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces." Physical Review Letters 114, no. 9 (2015): 096405.

[11] Behler, Jörg, and Michele Parrinello. "Generalized neural-network representation of high-dimensional potential-energy surfaces." Physical review letters 98, no. 14 (2007): 146401.

[12] Behler, Jörg. "High‐Dimensional Neural Network Potentials for Complex Systems." Angewandte Chemie International Edition (2017).

[13] Schütt, Kristof T., Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller. "MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions." arXiv preprint arXiv:1706.08566 (2017).


Confirmed Invited Speakers (in alphabetic order)

George Booth (King''s College London, UK)
Michele Ceriotti (EPFL, Switzerland)
Anand Chandrasekaran
(Georgia Institute of Technology, USA)
Anna Delin (KTH, Sweden)
Thomas Hammerschmidt (ICAMS, Ruhr-Universität Bochum, Germany)
Oded Hod (Tel Aviv University, Israel)
Olexandr Isayev (University of North Carolina at Chapel Hill, USA)
Anatole von Lilienfeld ׂׂׂׂׂ(University of Baselׁ, Switzerland)
Turab Lookman (Los Alamos National Laboratory, USA)
Guy Makov (Ben-Gurion University of the Negev, Israel)
Noa Marom (Carnegie Mellon University, USA)
Dennis Rapaport (Bar-Ilan University, Israel)
Matthias Rupp (Fritz Haber Institute of the Max Planck Society, Germany)
Alexandre Tkatchenko (University of Luxembourg, Luxembourg)
Cormac Toher (Duke University, USA)
Linfeng Zhang (Princeton University, USA)




The program is available HERE.


Participation is free but requires registration in advance. 

Deadline for Registration - August 30th.

Deadline for Abstract submission - August 12th.

Registration Before workshop (till Aug 30th) Late/On-site registration 
Students/Post-Docs Free  100 Euro
Faculty  Free 100 Euro
Tutors and invited
 No registration fees  No registration fees


If you are interested in attending this Workshop, please fill up THIS FORM. You can also contact us directly at with any question.


From the airport: Once you exit the terminal at Ben-Gurion airport, you will find a good (and relatively affordable) taxi service named Hadar-Lod that can take you to your hotel. More information regarding this and other transportation routes to and from the airport (and a lot of other relevant information) can be found on the webpage of the Ministry of Tourism.

By car: Nearest exit to us from the Ayalon is Rokach Boulevard.

By bus: Lines 74, 86, 572, 274, 604, and 475 of the Egged bus company stop near the campus. Lines 7, 13, 24, 25, 27, 45, 49, and 112 of the Dan bus company have also nearby stops.

By train: The Tel Aviv University train station is within walking distance of the campus. Bus 112 can be also used to go back and forth between the train station and the campus. For additional information, see the Israel Railways website.



 Tel-Aviv University (TAU) has signed agreements with several hotels in the Tel-Aviv area to provide attractive prices for TAU affiliates. As participants of a CECAM activity @ TAU you are entitled to book these hotels at a reduced price. 

Available Hotels:

  • Shalom Hotel
    Rate per single room per night including breakfast: 770 NIS
    Rate per double room per night including breakfast: 847 NIS
  • Melody Hotel or Tal Hotel
    Rate per single room per night including breakfast: 715 NIS
    Rate per double room per night including breakfast: 792 NIS
  • Artplus  Hotel or Yam Hotel 
    Rate per single room per night including breakfast: 600 NIS
    Rate per double room per night including breakfast: 666 NIS

As a business client, you can enjoy the following:

  • Free WIFI throughout the hotel
  • Complimentary newspaper available (in English, Russian or Hebrew)
  • Happy Hour; complimentary beverages & snacks available every week day from 17:00h – 19:00h l.
  • Every guestroom comes equipped with coffee corner & mini-fridge
  • Free personal safe
  • In-room welcome refreshments
  • At the Artplus Hotel : Free dry sauna and gym.

For reservations - please fill and send the attached hotel registration form - Hotel Form- to Ms Ayelet Giat -


The workshop will start at the Berglas School of Economics building, lecture hall 012. More detailed directions will be added close to the event.
An interactive map of the campus is available here:



The weather in Tel Aviv in October is difficult to predict - there can be both sunny and rainy days. Temperatures are between 15°C to 29°C. You can check the exact forecast close to your arrival at: weather forecast.


Located in Lausanne, Switzerland, CECAM is a well established (since 1969) European organization devoted to the promotion of fundamental research on advanced computational methods and to their application to important problems in frontier areas of science and technology. CECAM's fields of interest include computational chemistry, materials science, physics, and biology.