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

@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{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}

}

@article{10.1063/5.0197105,

title = {Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks},

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

doi = {https://doi.org/10.1063/5.0197105},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

journal = {The Journal of Chemical Physics},

volume = {160},

number = {114107},

issue = {11},

abstract = {In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.},

keywords = {P03, P12},

pubstate = {published},

tppubtype = {article}

}

@article{Falkner2024,

title = {Enhanced Sampling of Configuration and Path Space in a Generalized Ensemble by Shooting Point Exchange},

author = {Sebastian Falkner and Alessandro Coretti and Christoph Dellago},

url = {https://arxiv.org/abs/2302.08757},

doi = {https://doi.org/10.1103/PhysRevLett.132.128001},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

journal = {Physical Review Letters},

volume = {132},

issue = {12},

pages = {128001},

abstract = {The computer simulation of many molecular processes is complicated by long timescales caused by rare transitions between long-lived states. Here, we propose a new approach to simulate such rare events, which combines transition path sampling with enhanced exploration of configuration space. The method relies on exchange moves between configuration and trajectory space, carried out based on a generalized ensemble. This scheme substantially enhances the efficiency of the transition path sampling simulations, particularly for systems with multiple transition channels, and yields information on thermodynamics, kinetics and reaction coordinates of molecular processes without distorting their dynamics. The method is illustrated using the isomerization of proline in the KPTP tetrapeptide.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{10.1063/5.0169384,

title = {Molecular Hessian matrices from a machine learning random forest regression algorithm},

author = {Giorgio Domenichini and Christoph Dellago},

doi = {https://doi.org/10.1063/5.0169384},

year  = {2023},

date = {2023-11-20},

urldate = {2023-11-20},

journal = {The Journal of Chemical Physics},

volume = {159},

number = {194111},

issue = {19},

abstract = {In this article, we present a machine learning model to obtain fast and accurate estimates of the molecular Hessian matrix. In this model, based on a random forest, the second derivatives of the energy with respect to redundant internal coordinates are learned individually. The internal coordinates together with their specific representation guarantee rotational and translational invariance. The model is trained on a subset of the QM7 dataset but is shown to be applicable to larger molecules picked from the QM9 dataset. From the predicted Hessian, it is also possible to obtain reasonable estimates of the vibrational frequencies, normal modes, and zero point energies of the molecules.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Falkner_2023,

title = {Conditioning Boltzmann generators for rare event sampling},

author = {Sebastian Falkner and Alessandro Coretti and Salvatore Romano and Phillip L. Geissler and Christoph Dellago},

url = {https://arxiv.org/abs/2207.14530},

doi = {10.1088/2632-2153/acf55c},

year  = {2023},

date = {2023-09-22},

urldate = {2023-09-22},

journal = {Machine Learning: Science and Technology},

volume = {4},

number = {035050},

issue = {3},

abstract = {Understanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare events is to generate an ensemble of transition paths using a random walk in trajectory space. This, however, comes with the drawback of strong correlations between subsequently sampled paths and with an intrinsic difficulty in parallelizing the sampling process. We propose a transition path sampling scheme based on neural-network generated configurations. These are obtained employing normalizing flows, a neural network class able to generate statistically independent samples from a given distribution. With this approach, not only are correlations between visited paths removed, but the sampling process becomes easily parallelizable. Moreover, by conditioning the normalizing flow, the sampling of configurations can be steered towards regions of interest. We show that this approach enables the resolution of both the thermodynamics and kinetics of the transition region for systems that can be sampled using exact-likelihood generative models.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Hijes2023a,

title = {The kinetics of the ice–water interface from \textit{ab initio} machine learning simulations},

author = {Pablo Montero Hijes and Salvatore Romano and Alexander Gorfer and Christoph Dellago},

doi = {10.1063/5.0151011},

year  = {2023},

date = {2023-05-24},

urldate = {2023-05-24},

journal = {The Journal of Chemical Physics},

volume = {158},

number = {204706},

publisher = {AIP Publishing},

abstract = {Molecular simulations employing empirical force fields have provided valuable knowledge about the ice growth process in the past decade. The development of novel computational techniques allows us to study this process, which requires long simulations of relatively large systems, with ab initio accuracy. In this work, we use a neural-network potential for water trained on the revised Perdew–Burke–Ernzerhof functional to describe the kinetics of the ice–water interface. We study both ice melting and growth processes. Our results for the ice growth rate are in reasonable agreement with previous experiments and simulations. We find that the kinetics of ice melting presents a different behavior (monotonic) than that of ice growth (non-monotonic). In particular, a maximum ice growth rate of 6.5 Å/ns is found at 14 K of supercooling. The effect of the surface structure is explored by investigating the basal and primary and secondary prismatic facets. We use the Wilson–Frenkel relation to explain these results in terms of the mobility of molecules and the thermodynamic driving force. Moreover, we study the effect of pressure by complementing the standard isobar with simulations at a negative pressure (−1000 bar) and at a high pressure (2000 bar). We find that prismatic facets grow faster than the basal one and that pressure does not play an important role when the speed of the interface is considered as a function of the difference between the melting temperature and the actual one, i.e., to the degree of either supercooling or overheating.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}