Nordita, Stockholm, Sweden
Over the past decades we have witnessed an enormous increase of computational power and a rapid development of experimental techniques. Both developments, together with the great advancements of data storage capacities have initiated the application of methods taken from computer and data science into the research of functional quantum materials and quantum many-body physics. For example, interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science opening the path towards an efficient computer based materials design. In the case of supervised learning, large datasets, e.g., of ab initio calculations, provide the necessary training examples. The trained models facilitate high-throughput screening of materials by reducing the search space. Additionally, the models enable dynamic simulation on longer timescales than traditionally feasible. Unsupervised clustering approaches using structural similarity metrics allow for a new way of exploring the large chemical space. In case of the many body problem, machine learning architectures provide versatile wavefunctions that lead to accurate results and prove to be more flexible than traditional methods. Conversely, quantum has also influenced the development of machine learning methods in the case of tensor networks and stimulated the research on developments of machine learning algorithms for potential quantum computers.
To assist the community in developing a coherent and consistent view we hold a three day focus workshop at Nordita titled "Machine Learning for Quantum Matter". We envision a set of leading experts talks combined with the talks of younger participants to present a broad picture of the activities and best ideas on the use of ML methods in quantum matter.
- State-of-the art and method development
- Scientific data and materials databases
- Quantum materials design
- Machine Learning applied to quantum phases and phase transitions
- Machine learning applied to many-body quantum physics
- Tensor network states
- Quantum machine learning
- Machine learning algorithms for quantum computers
[Timetable - available from start of the workshop]
Invited Speakers (tentative)
- Alexandre Tkatchenko (University of Luxembourg)
- Anatole von Lilienfeld (University of Basel)
- Artem Oganov (Skolkovo Institute of Science and Technology)
- Jacob Biamonte (Skolkovo Institute of Science and Technology)
- Valentin Stanev (University of Maryland)
- Tess Smidt (Lawrence Berkeley National Laboratory)
- Johan Mentink (Radboud University)
- Ryo Tamura (National Institute for Materials Science - NIMS)
- Matthias Geilhufe (Nordita)
- Johan Hellsvik (Nordita)
If you want to apply for participation in the workshop, please fill in the application form. You will be informed by the organizers shortly after the application deadline whether your application has been approved. Due to space restrictions, the total number of participants is strictly limited. (Invited speakers are of course automatically approved, but need to register anyway.)
Application deadline: 1 July 2019
There is no registration fee.
Nordita provides a limited number of rooms in the Stockholm apartment hotel BizApartments free of charge for accepted participants.
Please be aware that unfortunately, scammers sometimes approach participants claiming to be able to provide accommodation and asking for credit card details. Please do not give this information to them. For successful applicants, Nordita will be in touch via email regarding accommodation. If you are in any doubt about the legitimacy of an approach, please get in contact with the organisers.