O 43: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 1
Mittwoch, 7. September 2022, 10:30–13:00, S054
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10:30 |
O 43.1 |
Structure of Amorphous Phosphorus from Machine Learning-Driven Simulations — •Yuxing Zhou, William Kirkpatrick, and Volker L. Deringer
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10:45 |
O 43.2 |
Realistic Structural Properties of Amorphous SiNx from Machine-Learning-Driven Molecular Dynamics — •Ganesh Kumar Nayak, Prashanth Srinivasan, Juraj Todt, Rostislav Daniel, and David Holec
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11:00 |
O 43.3 |
Combined experimental-computational directed sampling approach to modelling amorphous alumina — •Angela Harper, Steffen Emge, Pieter Magusin, Clare Grey, and Andrew Morris
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11:15 |
O 43.4 |
Structural phases and thermodynamics of BaTiO3 from an integrated machine learning model — •Lorenzo Gigli, Max Veit, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, and Michele Ceriotti
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11:30 |
O 43.5 |
Dielectric properties of BaTiO3 from an integrated machine-learning model — •Max Veit, Lorenzo Gigli, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, and Michele Ceirotti
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11:45 |
O 43.6 |
The first-principles phase diagram of monolayer nanoconfined water — •Venkat Kapil, Christoph Schran, Andrea Zen, Ji Chen, Chris Pickard, and Angelos Michaelides
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12:00 |
O 43.7 |
Exploring amorphous graphene with empirical and machinelearned potentials — •Zakariya El-Machachi, Mark Wilson, and Volker L. Deringer
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12:15 |
O 43.8 |
Machine learning for estimation of spin models in undoped cuprates — •Denys Y. Kononenko, Ulrich K. Rößler, Jeroen van den Brink, and Oleg Janson
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12:30 |
O 43.9 |
Machine-learning Based Screening of Lead-free Perovskites for Photovoltaic Applications — •Elisabetta Landini, Harald Oberhofer, and Karsten Reuter
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12:45 |
O 43.10 |
Equivariant graph neural network for linear scaling electron density estimation and applications in battery materials — •Arghya Bhowmik and Peter Jorgensen
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