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

Our research efforts focus on the development of simulation algorithms and their application to investigate dynamical processes in condensed matter systems based on the principles of equilibrium and non-equilibrium statistical mechanics. In particular, we have helped to create the transition path sampling methodology for the simulation of rare but important events, such as nucleation aprocesses, chemical reactions and biomolecular reorganizations. More recently, we have worked on applying machine learning methods to molecular structure recognition and the representation of potential and free energy surfaces.

Recent research topics include:

  • Self-assembly of nanocrystals
  • Folding and unfolding of biopolymers
  • Interfaces in aqueous systems
  • Phase separation in alloys
  • Thermo-polarisation
  • Structure and dynamics of water and ice
  • Cavitation
  • Crystallization
  • Non-equilibrium work fluctuations

Team

Christoph Dellago
PI

Pablo Montero de Hijes
PostDoc

Alexander Gorfer
PhD Student

Salvatore Romano
PhD Student

Associates

Alessandro Coretti
PostDoc

Andreas Tröster
PostDoc

Sebastian Falkner
PhD Student

Publications

17 entries « 1 of 2 »

2024

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

de Hijes, Pablo Montero; Dellago, Christoph; Jinnouchi, Ryosuke; Schmiedmayer, Bernhard; Kresse, Georg

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 160, iss. 11, no. 114107, 2024.

Abstract | Links | BibTeX | Tags: P03, P12

Enhanced Sampling of Configuration and Path Space in a Generalized Ensemble by Shooting Point Exchange

Falkner, Sebastian; Coretti, Alessandro; Dellago, Christoph

Enhanced Sampling of Configuration and Path Space in a Generalized Ensemble by Shooting Point Exchange

Journal ArticleOpen Access

In: Physical Review Letters, vol. 132, iss. 12, pp. 128001, 2024.

Abstract | Links | BibTeX | Tags: P12

Structure and thermodynamics of defects in Na-feldspar from a neural network potential

Gorfer, Alexander; Abart, Rainer; Dellago, Christoph

Structure and thermodynamics of defects in Na-feldspar from a neural network potential

Journal ArticleSubmittedarXiv

In: arXiv, 2024.

Abstract | Links | BibTeX | Tags: P12

Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials

Omranpour, Amir; de Hijes, Pablo Montero; Behler, Jörg; Dellago, Christoph

Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials

Journal ArticleSubmittedarXiv

In: arXiv, 2024.

Abstract | Links | BibTeX | Tags: P12

2023

Molecular Hessian matrices from a machine learning random forest regression algorithm

Domenichini, Giorgio; Dellago, Christoph

Molecular Hessian matrices from a machine learning random forest regression algorithm

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 159, iss. 19, no. 194111, 2023.

Abstract | Links | BibTeX | Tags: P12

Conditioning Boltzmann generators for rare event sampling

Falkner, Sebastian; Coretti, Alessandro; Romano, Salvatore; Geissler, Phillip L.; Dellago, Christoph

Conditioning Boltzmann generators for rare event sampling

Journal ArticleOpen Access

In: Machine Learning: Science and Technology, vol. 4, iss. 3, no. 035050, 2023.

Abstract | Links | BibTeX | Tags: P12

The kinetics of the ice–water interface from ab initio machine learning simulations

Hijes, Pablo Montero; Romano, Salvatore; Gorfer, Alexander; Dellago, Christoph

The kinetics of the ice–water interface from ab initio machine learning simulations

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 158, no. 204706, 2023.

Abstract | Links | BibTeX | Tags: P12

Machine-guided path sampling to discover mechanisms of molecular self-organization

Jung, Hendrik; Covino, Roberto; Arjun, A.; Leitold, Christian; Dellago, Christoph; Bolhuis, Peter G.; Hummer, Gerhard

Machine-guided path sampling to discover mechanisms of molecular self-organization

Journal ArticleOpen Access

In: Nature Computational Science, vol. 3, pp. 334–345, 2023.

Abstract | Links | BibTeX | Tags: P12

Minimum in the pressure dependence of the interfacial free energy between ice Ih and water

Hijes, Pablo Montero; Espinosa, Jorge R; Vega, Carlos; Dellago, Christoph

Minimum in the pressure dependence of the interfacial free energy between ice Ih and water

Journal ArticleOpen Access

In: The Journal of Chemical Physics, vol. 158, no. 12, pp. 124503, 2023.

Abstract | Links | BibTeX | Tags: P12

2022

Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields

Tröster, Andreas; Verdi, Carla; Dellago, Christoph; Rychetsky, Ivan; Kresse, Georg; Schranz, Wilfried

Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields

Journal Article

In: Physical Review Materials, vol. 6, no. 9, pp. 094408, 2022.

Abstract | Links | BibTeX | Tags: P03, P12

17 entries « 1 of 2 »