Neural-network based simulation of rare event processes at the water/oxide interface
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.
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
- Structure and dynamics of water and ice
- Non-equilibrium work fluctuations
Machine-guided path sampling to discover mechanisms of molecular self-organizationJournal ArticleOpen Access
In: Nature Computational Science, vol. 3, no. 4, pp. 334–345, 2023.
Improved description of atomic environments using low-cost polynomial functions with compact supportJournal ArticleOpen Access
In: Machine Learning: Science and Technology, vol. 2, no. 3, pp. 035026, 2021.
Ab initio structure and thermodynamics of the RPBE-D3 water/vapor interface by neural-network molecular dynamicsJournal ArticleOpen Access
In: The Journal of Chemical Physics, vol. 153, no. 14, pp. 144710, 2020.
Phase stability of the ice XVII-based CO2 chiral hydrate from molecular dynamics simulationsJournal ArticleOpen Access
In: The Journal of Chemical Physics, vol. 151, no. 10, pp. 104502, 2019.
Parallel Multistream Training of High-Dimensional Neural Network PotentialsJournal Article
In: Journal of Chemical Theory and Computation, vol. 15, no. 5, pp. 3075–3092, 2019.
Library-Based LAMMPS Implementation of High-Dimensional Neural Network PotentialsJournal Article
In: Journal of Chemical Theory and Computation, vol. 15, no. 3, pp. 1827–1840, 2019.
Ab initio thermodynamics of liquid and solid waterJournal ArticleOpen Access
In: Proceedings of the National Academy of Sciences, vol. 116, no. 4, pp. 1110–1115, 2019.