Name: Machine Learning for Quantum Matter
Start: 2019-08-26T09:00:00+02:00
End: 2019-08-28T18:00:00+02:00
Location: Nordita, Stockholm

Machine Learning for Quantum Matter

from Monday 26 August 2019 (09:00) to Wednesday 28 August 2019 (18:00)

Monday 26 August 2019
09:30 Introduction - Alexander Balatsky
Introduction
- Alexander Balatsky
09:30 - 09:45
Room: 132:028
09:45 Quantum Machine Learning for Quantum Simulation - Jacob Biamonte
Quantum Machine Learning for Quantum Simulation
- Jacob Biamonte
09:45 - 10:45
Room: 132:028
10:45 Coffee break
Coffee break
10:45 - 11:00
Room: 132:028
11:00 Quantum error correction for the toric code using deep reinforcement learning - Mats Granath
Quantum error correction for the toric code using deep reinforcement learning
- Mats Granath
11:00 - 12:00
Room: 132:028
12:00 Lunch
Lunch
12:00 - 14:00
Room: Albanova Restaurant Entré
14:00 Organic Quantum Matter and the Organic Materials Database - Matthias Geilhufe
Organic Quantum Matter and the Organic Materials Database
- Matthias Geilhufe
14:00 - 15:00
Room: 132:028
15:00 Machine learning modeling of superconducting critical - Valentin Stanev
Machine learning modeling of superconducting critical
- Valentin Stanev
15:00 - 16:00
Room: 132:028
16:00 Coffee break
Coffee break
16:00 - 16:30
Room: 132:028
16:30 Unsupervised detection of topological quantum state equivalences - Oleksandr Balabanov
Unsupervised detection of topological quantum state equivalences
- Oleksandr Balabanov
16:30 - 17:00
Room: 132:028
17:00 Deriving descriptors from fundamental physical models for targeted material property prediction using machine learning - Ali Mazhar
Deriving descriptors from fundamental physical models for targeted material property prediction using machine learning
- Ali Mazhar
17:00 - 17:30
Room: 132:028
18:30 Reception
Reception
18:30 - 21:00
Room: Albanova Entrance
Tuesday 27 August 2019
09:00 Towards Universal Machine-Learning/Physics Model of Molecular Properties in Chemical Space - Alexandre Tkachenko
Towards Universal Machine-Learning/Physics Model of Molecular Properties in Chemical Space
- Alexandre Tkachenko
09:00 - 10:00
Room: 132:028
10:00 Coffee break
Coffee break
10:00 - 10:15
Room: 132:028
10:15 Navigating chemical space with quantum machine learning - Anatole von Lilienfeld
Navigating chemical space with quantum machine learning
- Anatole von Lilienfeld
10:15 - 11:15
Room: 132:028
11:15 Artificial intelligence methods for discovering novel materials and exotic compounds - Artem Oganov
Artificial intelligence methods for discovering novel materials and exotic compounds
- Artem Oganov
11:15 - 12:15
Room: 132:028
12:15 Lunch
Lunch
12:15 - 14:00
Room: Albanova Restaurant Entré
14:00 Effective model estimation for magnetic materials by machine learning - Rio Tamura
Effective model estimation for magnetic materials by machine learning
- Rio Tamura
14:00 - 15:00
Room: 132:028
15:00 Spin wave excitations of magnetic metal organic materials - Johan Hellsvik
Spin wave excitations of magnetic metal organic materials
- Johan Hellsvik
15:00 - 16:00
Room: 132:028
16:00 Coffee break
Coffee break
16:00 - 16:30
Room: 132:028
16:30 New horizons for the fastest, densest and least dissipative brain-inspired computing - Johan Mentink
New horizons for the fastest, densest and least dissipative brain-inspired computing
- Johan Mentink
16:30 - 17:30
Room: 132:028
Wednesday 28 August 2019
09:00 SchNet - An interpretable atomistic neural network - Kristof Schütt
SchNet - An interpretable atomistic neural network
- Kristof Schütt
09:00 - 10:00
Room: 132:028
10:00 Coffee break
Coffee break
10:00 - 10:15
Room: 132:028
10:15 Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomic Geometries - Tess Smidt
Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomic Geometries
- Tess Smidt
10:15 - 11:15
Room: 132:028
11:15 Phylogenetic operads in machine learning - Caroline Brembilla
Phylogenetic operads in machine learning
- Caroline Brembilla
11:15 - 11:45
Room: 132:028
11:45 Microsoft’s Azure ML and AI modeling portfolio - Joana Olivia
Microsoft’s Azure ML and AI modeling portfolio
- Joana Olivia
11:45 - 12:15
Room: 132:028
12:15 Lunch
Lunch
12:15 - 14:00
Room: Albanova Restaurant Entré
14:00 Dirac Materials and Informatics - Alexander Balatsky
Dirac Materials and Informatics
- Alexander Balatsky
14:00 - 15:00
Room: 132:028
15:00 Determining spectrum-structure relation with machine learning techniques - Yi Luo
Determining spectrum-structure relation with machine learning techniques
- Yi Luo
15:00 - 15:30
Room: 132:028
15:30 Coffee and round table discussion
Coffee and round table discussion
15:30 - 16:30
Room: 132:028