Neural-network based simulation of rare event processes at the water/oxide interface

Subproject P12

Atomistic computer simulations of processes occurring at the water/oxide interface are challenging in several ways. The calculation of atomic forces based on ab initio methods is computationally very demanding, and barrier crossing events may lead to long computation times. Both these aspects severely limit accessible system sizes and simulation times.

In project P12, we will address these challenges using a combination of machine learning and advanced rare event sampling methods. In particular, using software developed in our group and collaborating with P03 Kresse, we will train neural network potentials based on the Behler-Parrinello approach for oxide/water interfaces, starting with the Fe3O4/water system studied in P11 Backus. We will pay special attention to error estimation and the correct treatment of long-range interactions. With the new potential, we will study the structure and dynamics of water near the oxide surface to provide the atomistic information necessary to rationalize the spectroscopy experiments of P11 Backus. Another important goal of P12 is to explore how deep generative models can be used to enhance rare event simulations. For this purpose, we will apply normalizing flows, represented by deep neural networks, to trajectory space. The resulting improved transition path sampling simulations will be used to study reactive processes investigated experimentally in other subprojects of TACO.

Christoph Dellago
PI

Expertise

Team

Christoph Dellago
PI

Publications

2021

Improved description of atomic environments using low-cost polynomial functions with compact support

Bircher, Martin P; Singraber, Andreas; Dellago, Christoph

Improved description of atomic environments using low-cost polynomial functions with compact support Journal Article

Machine Learning: Science and Technology, 2 (3), pp. 035026, 2021.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

2020

Ab initio structure and thermodynamics of the RPBE-D3 water/vapor interface by neural-network molecular dynamics

Wohlfahrt, Oliver; Dellago, Christoph; Sega, Marcello

Ab initio structure and thermodynamics of the RPBE-D3 water/vapor interface by neural-network molecular dynamics Journal Article

The Journal of Chemical Physics, 153 (14), pp. 144710, 2020.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

2019

Phase stability of the ice XVII-based CO2 chiral hydrate from molecular dynamics simulations

Michl, Jakob; Sega, Marcello; Dellago, Christoph

Phase stability of the ice XVII-based CO2 chiral hydrate from molecular dynamics simulations Journal Article

The Journal of Chemical Physics, 151 (10), pp. 104502, 2019.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

Parallel Multistream Training of High-Dimensional Neural Network Potentials

Singraber, Andreas; Morawietz, Tobias; Behler, Jörg; Dellago, Christoph

Parallel Multistream Training of High-Dimensional Neural Network Potentials Journal Article

Journal of Chemical Theory and Computation, 15 (5), pp. 3075–3092, 2019.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

Singraber, Andreas; Behler, Jörg; Dellago, Christoph

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials Journal Article

Journal of Chemical Theory and Computation, 15 (3), pp. 1827–1840, 2019.

Abstract | Links | BibTeX | Tags: P12, pre-TACO

Ab initio thermodynamics of liquid and solid water

Cheng, Bingqing; Engel, Edgar A; Behler, Jörg; Dellago, Christoph; Ceriotti, Michele

Ab initio thermodynamics of liquid and solid water Journal Article

Proceedings of the National Academy of Sciences, 116 (4), pp. 1110–1115, 2019.

Abstract | Links | BibTeX | Tags: P12, pre-TACO