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, August 26, 2019 (9:00 AM) to Wednesday, August 28, 2019 (6:00 PM)

Monday, August 26, 2019
9:30 AM Introduction - Alexander Balatsky
Introduction
- Alexander Balatsky
9:30 AM - 9:45 AM
Room: 132:028
9:45 AM Quantum Machine Learning for Quantum Simulation - Jacob Biamonte
Quantum Machine Learning for Quantum Simulation
- Jacob Biamonte
9:45 AM - 10:45 AM
Room: 132:028
10:45 AM Coffee break
Coffee break
10:45 AM - 11:00 AM
Room: 132:028
11:00 AM 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 AM - 12:00 PM
Room: 132:028
12:00 PM Lunch
Lunch
12:00 PM - 2:00 PM
Room: Albanova Restaurant Entré
2:00 PM Organic Quantum Matter and the Organic Materials Database - Matthias Geilhufe
Organic Quantum Matter and the Organic Materials Database
- Matthias Geilhufe
2:00 PM - 3:00 PM
Room: 132:028
3:00 PM Machine learning modeling of superconducting critical - Valentin Stanev
Machine learning modeling of superconducting critical
- Valentin Stanev
3:00 PM - 4:00 PM
Room: 132:028
4:00 PM Coffee break
Coffee break
4:00 PM - 4:30 PM
Room: 132:028
4:30 PM Unsupervised detection of topological quantum state equivalences - Oleksandr Balabanov
Unsupervised detection of topological quantum state equivalences
- Oleksandr Balabanov
4:30 PM - 5:00 PM
Room: 132:028
5:00 PM 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
5:00 PM - 5:30 PM
Room: 132:028
6:30 PM Reception
Reception
6:30 PM - 9:00 PM
Room: Albanova Entrance
Tuesday, August 27, 2019
9:00 AM 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
9:00 AM - 10:00 AM
Room: 132:028
10:00 AM Coffee break
Coffee break
10:00 AM - 10:15 AM
Room: 132:028
10:15 AM Navigating chemical space with quantum machine learning - Anatole von Lilienfeld
Navigating chemical space with quantum machine learning
- Anatole von Lilienfeld
10:15 AM - 11:15 AM
Room: 132:028
11:15 AM 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 AM - 12:15 PM
Room: 132:028
12:15 PM Lunch
Lunch
12:15 PM - 2:00 PM
Room: Albanova Restaurant Entré
2:00 PM Effective model estimation for magnetic materials by machine learning - Rio Tamura
Effective model estimation for magnetic materials by machine learning
- Rio Tamura
2:00 PM - 3:00 PM
Room: 132:028
3:00 PM Spin wave excitations of magnetic metal organic materials - Johan Hellsvik
Spin wave excitations of magnetic metal organic materials
- Johan Hellsvik
3:00 PM - 4:00 PM
Room: 132:028
4:00 PM Coffee break
Coffee break
4:00 PM - 4:30 PM
Room: 132:028
4:30 PM 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
4:30 PM - 5:30 PM
Room: 132:028
Wednesday, August 28, 2019
9:00 AM SchNet - An interpretable atomistic neural network - Kristof Schütt
SchNet - An interpretable atomistic neural network
- Kristof Schütt
9:00 AM - 10:00 AM
Room: 132:028
10:00 AM Coffee break
Coffee break
10:00 AM - 10:15 AM
Room: 132:028
10:15 AM 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 AM - 11:15 AM
Room: 132:028
11:15 AM Phylogenetic operads in machine learning - Caroline Brembilla
Phylogenetic operads in machine learning
- Caroline Brembilla
11:15 AM - 11:45 AM
Room: 132:028
11:45 AM Microsoft’s Azure ML and AI modeling portfolio - Joana Olivia
Microsoft’s Azure ML and AI modeling portfolio
- Joana Olivia
11:45 AM - 12:15 PM
Room: 132:028
12:15 PM Lunch
Lunch
12:15 PM - 2:00 PM
Room: Albanova Restaurant Entré
2:00 PM Dirac Materials and Informatics - Alexander Balatsky
Dirac Materials and Informatics
- Alexander Balatsky
2:00 PM - 3:00 PM
Room: 132:028
3:00 PM Determining spectrum-structure relation with machine learning techniques - Yi Luo
Determining spectrum-structure relation with machine learning techniques
- Yi Luo
3:00 PM - 3:30 PM
Room: 132:028
3:30 PM Coffee and round table discussion
Coffee and round table discussion
3:30 PM - 4:30 PM
Room: 132:028

Choose timezone