O 67: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 3
Donnerstag, 8. September 2022, 10:30–12:45, S054
 |
10:30 |
O 67.1 |
Quantile Random Forest Model for Extrapolation to the Complete Basis Set Limit in Density Functional Theory Calculations — •Daniel Speckhard, Christian Carbogno, Sven Lubeck, Luca Ghiringhelli, Matthias Scheffler, and Claudia Draxl
|
|
|
|
10:45 |
O 67.2 |
Symmetry and completeness in machine-learning models for atomistic simulations — •Sergey Pozdnyakov and Michele Ceriotti
|
|
|
|
11:00 |
O 67.3 |
Fast, robust, interpretable machine-learning potentials — Stephen R. Xie, Richard G. Hennig, and •Matthias Rupp
|
|
|
| |
11:15 |
O 67.4 |
The contribution has been moved to O 58.16.
|
|
|
|
11:30 |
O 67.5 |
Predicting condensed-phase electron densities using machine learning — •Alan Lewis, Andrea Grisafi, Michele Ceriotti, and Mariana Rossi
|
|
|
|
11:45 |
O 67.6 |
Equivariant N-center representations for machine learning molecular Hamiltonians — •Jigyasa Nigam, Michael Willatt, and Michele Ceriotti
|
|
|
|
12:00 |
O 67.7 |
Similarity-of-materials analysis for reusability and interoperability of data in materials databases — •Šimon Gabaj, Martin Kuban, Santiago Rigamonti, and Claudia Draxl
|
|
|
|
12:15 |
O 67.8 |
Supervised and unsupervised deep Learning of topological phase transitions from entanglement aspect for one- and two-dimensional chiral p-wave superconductors — •Ming-Chiang Chung
|
|
|
|
12:30 |
O 67.9 |
Machine Learning the Square-Lattice Ising Model — •Burak Çivitcioğlu, Andreas Honecker, and Rudolf A. Römer
|
|
|
