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

@article{Romano_2024b,

title = {Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations},

author = {Salvatore Romano and Harsharan Kaur and Moritz Zelenka and Pablo Montero De Hijes and Moritz Eder and Gareth S. Parkinson and Ellen H. G. Backus and Christoph Dellago},

url = {https://arxiv.org/abs/2410.12717},

year  = {2024},

date = {2024-10-16},

urldate = {2024-10-16},

journal = {arXiv},

abstract = {Magnetite, a naturally abundant mineral, frequently interacts with water in both natural settings and various technical applications, making the study of its surface chemistry highly relevant. In this work, we investigate the hydrogen bonding dynamics and the presence of hydroxyl species at the magnetite-water interface using a combination of neural network potential-based molecular dynamics simulations and sum frequency generation vibrational spectroscopy. Our simulations, which involved large water systems, allowed us to identify distinct interfacial species, such as dissociated hydrogen and hydroxide ions formed by water dissociation. Notably, water molecules near the interface exhibited a preference for dipole orientation towards the surface, with bulk-like water behavior only re-emerging beyond 60 Å from the surface. The vibrational spectroscopy results aligned well with the simulations, confirming the presence of a hydrogen bond network in the surface ad-layers. The analysis revealed that surface-adsorbed hydroxyl groups orient their hydrogen atoms towards the water bulk. In contrast, hydrogen-bonded water molecules align with their hydrogen atoms pointing towards the magnetite surface.},

keywords = {P04, P11, P12},

pubstate = {published},

tppubtype = {article}

}

@article{Montero-de-Hijes_2024b,

title = {Density isobar of water and melting temperature of ice: Assessing common density functionals},

author = {Pablo Montero de Hijes and Christoph Dellago and Ryosuke Jinnouchi and Georg Kresse},

url = {https://doi.org/10.1063/5.0227514},

year  = {2024},

date = {2024-10-03},

urldate = {2024-06-06},

journal = {The Journal of Chemical Physics},

volume = {161},

pages = {131102},

abstract = {We investigate the density isobar of water and the melting temperature of ice using six different density functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base functionals. Notably, even the choice of damping can result in substantial differences. Overall, the outcomes obtained through density functional theory are not entirely satisfactory across most utilized functionals. All functionals exhibit significant deviations either in the melting temperature or equilibrium volume, with most of them even predicting an incorrect volume difference between ice and water. Our heuristic analysis indicates that a hybrid functional with 25% exact exchange and van der Waals damping averaged between zero and Becke–Johnson dampings yields the closest agreement with experimental data. This study underscores the necessity for further enhancements in the treatment of van der Waals interactions and, more broadly, density functional theory to enable accurate quantitative predictions for molecular liquids.},

keywords = {P03, P12},

pubstate = {published},

tppubtype = {article}

}

@article{Romano_2024a,

title = {Structure and dynamics of the magnetite(001)/water interface from molecular dynamics simulations based on a neural network potential},

author = {Salvatore Romano and Pablo Montero de Hijes and Matthias Meier and Georg Kresse and Cesare Franchini and Christoph Dellago},

url = {https://arxiv.org/abs/2408.11538},

year  = {2024},

date = {2024-08-21},

urldate = {2024-08-21},

journal = {arXiv},

abstract = {The magnetite/water interface is commonly found in nature and plays a crucial role in various technological applications. However, our understanding of its structural and dynamical properties at the molecular scale remains still limited. In this study, we develop an efficient Behler-Parrinello neural network potential (NNP) for the magnetite/water system, paying particular attention to the accurate generation of reference data with density functional theory. Using this NNP, we performed extensive molecular dynamics simulations of the magnetite (001) surface across a wide range of water coverages, from the single molecule to bulk water. Our simulations revealed several new ground states of low coverage water on the Subsurface Cation Vacancy (SCV) model and yielded a density profile of water at the surface that exhibits marked layering. By calculating mean square displacements, we obtained quantitative information on the diffusion of water molecules on the SCV for different coverages, revealing significant anis},

keywords = {P03, P07, P12},

pubstate = {published},

tppubtype = {article}

}

@article{Falkner_2024b,

title = {Revisiting Shooting Point Monte Carlo Methods for Transition Path Sampling},

author = {Sebastian Falkner and Alessandro Coretti and Baron Peters and Peter G. Bolhuis and Christoph Dellago},

url = {https://arxiv.org/abs/2408.03054},

year  = {2024},

date = {2024-08-06},

journal = {arXiv},

abstract = {Rare event sampling algorithms are essential for understanding processes that occur infrequently on the molecular scale, yet they are important for the long-time dynamics of complex molecular systems. One of these algorithms, transition path sampling, has become a standard technique to study such rare processes since no prior knowledge on the transition region is required. Most TPS methods generate new trajectories from old trajectories by selecting a point along the old trajectory, modifying its momentum in some way, and then "shooting" a new trajectory by integrating forward and backward in time. In some procedures, the shooting point is selected independently for each trial move, but in others, the shooting point evolves from one path to the next so that successive shooting points are related to each other. We provide an extended detailed balance criterion for shooting methods. We affirm detailed balance for most TPS methods, but the new criteria reveals the need for amended acceptance criteria in the flexible length aimless shooting and spring shooting methods.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Gorfer_2024c,

title = {Thermodynamics of alkali feldspar solid solutions with varying Al-Si order: atomistic simulations using a neural network potential},

author = {Alexander Gorfer and David Heuser and Rainer Abart and Christoph Dellago},

url = {https://arxiv.org/abs/2407.17452},

year  = {2024},

date = {2024-07-24},

journal = {arXiv},

abstract = {The thermodynamic mixing properties of alkali feldspar solid solutions between the Na and K end members were computed through atomistic simulations using a neural network potential. We performed combined molecular dynamics and Monte Carlo simulations in the semi-grand canonical ensemble at 800 °C and considered three quenched disorder states in the Al-Si-O framework ranging from fully ordered to fully disordered. The excess Gibbs energy of mixing, excess enthalpy of mixing and excess entropy of mixing are in good agreement with literature data. In particular, the notion that increasing disorder in the Al-Si-O framework correlates with increasing ideality of Na-K mixing is successfully predicted. Finally, a recently proposed short range ordering of Na and K in the alkali sublattice is observed, which may be considered as a precursor to exsolution lamellae, a characteristic phenomenon in alkali feldspar of intermediate composition leading to perthite formation during cooling.},

note = {American Mineralogist, submitted},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Gorfer_2024,

title = {Structure and thermodynamics of defects in Na-feldspar from a neural network potential},

author = {Alexander Gorfer and Rainer Abart and Christoph Dellago},

url = {https://arxiv.org/abs/2402.14640

https://doi.org/10.1103/PhysRevMaterials.8.073602},

year  = {2024},

date = {2024-07-18},

urldate = {2024-07-18},

journal = {Physical Review Materials},

volume = {8},

pages = {073602},

abstract = {The diffusive phase transformations occurring in feldspar, a common mineral in the crust of the Earth, are essential for reconstructing the thermal histories of magmatic and metamorphic rocks. Due to the long timescales over which these transformations proceed, the mechanism responsible for sodium diffusion and its possible anisotropy has remained a topic of debate. To elucidate this defect-controlled process, we have developed a Neural Network Potential (NNP) trained on first-principle calculations of Na-feldspar (Albite) and its charged defects. This new force field reproduces various experimentally known properties of feldspar, including its lattice parameters, elastic constants as well as heat capacity and DFT-calculated defect formation energies. A new type of dumbbell interstitial defect is found to be most favorable and its free energy of formation at finite temperature is calculated using thermodynamic integration. The necessity of including electrostatic corrections before training an NNP is demonstrated by predicting more consistent defect formation energies.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Gorfer_2024b,

title = {Mechanism and kinetics of sodium diffusion in Na-feldspar from neural network based atomistic simulations},

author = {Alexander Gorfer and Rainer Abart and Christoph Dellago},

url = {https://arxiv.org/abs/2405.19008},

year  = {2024},

date = {2024-05-29},

journal = {arXiv},

abstract = {Alkali diffusion is a first-order control for microstructure and compositional evolution of feldspar during cooling from high temperatures of primary magmatic or metamorphic crystallization, and knowledge of the respective diffusion coefficients is crucial for reconstructing thermal histories. Our understanding of alkali diffusion in feldspar is, however, hindered by an insufficient grasp of the underlying diffusion mechanisms. We performed molecular dynamics simulations of sodium feldspar (Albite) containing different point defects using a recently developed neural network potential. A high degree of agreement between the sodium self-diffusion coefficients obtained from model simulations and those determined experimentally in earlier studies motivated a detailed investigation into the interstitial and vacancy mechanisms, corresponding jump rates, correlation factors and anisotropy. We identified a dumbbell shaped double occupancy of an alkali site as an important point defect and a correlation effect originating from the orientation of the dumbbell as a possible cause for the ⊥(001)>⊥(010) diffusion anisotropy, which has been reported in a slew of feldspar cation diffusion experiments.},

note = {Acta Materialia, submitted},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Kyvala_2024a,

title = {Unsupervised identification of local atomic environment from atomistic potential descriptors},

author = {Lukáš Kývala and Pablo Montero De Hijes and Christoph Dellago},

url = {https://arxiv.org/abs/2405.01320},

year  = {2024},

date = {2024-05-02},

journal = {arXiv},

abstract = {Analyzing local structures effectively is key to unraveling the origin of many physical phenomena. Unsupervised algorithms offer an effective way of handling systems in which order parameters are unknown or computationally expensive. By combining novel unsupervised algorithm (Pairwise Controlled Manifold Approximation Projection) with atomistic potential descriptors, we distinguish between various chemical environments with minimal computational overhead. In particular, we apply this method to silicon and water systems. The algorithm effectively distinguishes between solid structures and phases of silicon, including solid and liquid phases, and accurately identifies interstitial, monovacancy, and surface atoms in diamond structures. In the case of water, it is capable of identifying an ice nucleus in the liquid phase, demonstrating its applicability in nucleation studies.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Omranpour_2024,

title = {Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials},

author = {Amir Omranpour and Pablo Montero de Hijes and Jörg Behler and Christoph Dellago},

url = {https://arxiv.org/abs/2401.17875

https://doi.org/10.1063/5.0201241},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {The Journal of Chemical Physics},

volume = {160},

pages = {170901},

abstract = {As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Coretti_2024a,

title = {Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems},

author = {Alessandro Coretti and Sebastian Falkner and Jan Weinreich and Christoph Dellago and Anatole von Lilienfeld},

url = {https://kimreview.org/commentaries/10-25950-bfa99422/

https://arxiv.org/abs/2404.16566},

doi = {10.25950/bfa99422},

year  = {2024},

date = {2024-04-22},

journal = {KIM Review},

volume = {2},

pages = {3},

abstract = {The paper by Noé et al. (Science, 2021) introduced the concept of Boltzmann Generators (BGs), a deep generative model that can produce unbiased independent samples of many-body systems. They can generate equilibrium configurations from different metastable states, compute relative stabilities between different structures of proteins or other organic molecules, and discover new states. In this commentary, we motivate the necessity for a new generation of sampling methods beyond molecular dynamics, explain the methodology, and give our perspective on the future role of BGs.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}