Publications

@article{Maqbool_2024a,

title = {Bimetallic CuPd nanoparticles supported on ZnO or graphene for CO_{2} and CO conversion to methane and methanol},

author = {Qaisar Maqbool and Klaus Dobrezberger and Julian Stropp and Martin Huber and Karl-Leopold Kontrus and Anna Aspalter and Julie Neuhauser and Thomas Schachinger and Stefan Löffler and Günther Rupprechter},

url = {https://doi.org/10.1039/D4SU00339J },

doi = {10.1039/D4SU00339J},

year  = {2024},

date = {2024-09-04},

urldate = {2024-09-04},

journal = {RSC Sustainability},

volume = {2},

issue = {11},

pages = {3276--3288},

abstract = {Carbon dioxide (CO_{2}) and carbon monoxide (CO) hydrogenation to methane (CH_{4}) or methanol (MeOH) is a promising pathway to reduce CO_{2} emissions and to mitigate dependence on rapidly depleting fossil fuels. Along these lines, a series of catalysts comprising copper (Cu) or palladium (Pd) nanoparticles (NPs) supported on zinc oxide (ZnO) as well as bimetallic CuPd NPs supported on ZnO or graphene were synthesized via various methodologies. The prepared catalysts underwent comprehensive characterization via high-resolution transmission electron microscopy (HRTEM), energy-dispersive X-ray spectroscopy (EDX) mapping, electron energy loss spectroscopy (EELS), X-ray diffraction (XRD), hydrogen temperature-programmed reduction and desorption (H_{2}-TPR and H_{2}-TPD), and deuterium temperature-programmed desorption (D_{2}O-TPD). In the CO2 hydrogenation process carried out at 20 bar and elevated temperatures (300 to 500 °C), Cu, Pd, and CuPd NPs (<5 wt% loading) supported on ZnO or graphene predominantly yielded CH_{4} as the primary product, with CO generated as a byproduct via the reverse water gas shift (RWGS) reaction. For CO hydrogenation between 400 and 500 °C, the CO conversion was at least 40% higher than the CO_{2} conversion, with CH_{4} and CO_{2} identified as the main products, the latter from water gas shift. Employing 90 wt% Cu on ZnO led to an enhanced CO conversion of 14%, with the MeOH yield reaching 10% and the CO_{2} yield reaching 4.3% at 230 °C. Overall, the results demonstrate that lower Cu/Pd loading (<5 wt%) supported on ZnO/graphene favored CH_{4} production, while higher Cu content (90 wt%) promoted MeOH production, for both CO_{2} and CO hydrogenation at high pressure.},

keywords = {P08},

pubstate = {published},

tppubtype = {article}

}

@article{Schmiedmayer_2024a,

title = {Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles},

author = {Bernhard Schmiedmayer and Georg Kresse},

doi = {https://doi.org/10.1063/5.0217243},

year  = {2024},

date = {2024-08-28},

journal = {The Journal of Chemical Physics},

volume = {161},

issue = {8},

pages = {084703},

abstract = {We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one to predict infrared spectra for complex systems at finite temperatures. The method’s effectiveness is demonstrated in challenging scenarios, such as the analysis of water and the organic–inorganic halide perovskite MAPbI_{3}, where our results consistently align with experimental data. A distinctive feature of the methodology is the incorporation of derivative learning, which proves indispensable for obtaining accurate polarization data in bulk materials and facilitates the training of a machine learning surrogate model of the polarization adapted to rotational and translational symmetries. We achieve polarization prediction accuracies of about 1% for the water dimer by training only on the predicted Born effective charges.},

keywords = {P03},

pubstate = {published},

tppubtype = {article}

}

@article{Tresca2024,

title = {Evidence of Molecular Hydrogen in the N-doped LuH_{3} System: a Possible Path to Superconductivity?},

author = {Cesare Tresca and Pietro Maria Forcella and Andrea Angeletti and Luigi Ranalli and Cesare Franchini and Michele Reticcioli and Gianni Profeta},

url = {https://arxiv.org/abs/2308.03619

https://doi.org/10.1038/s41467-024-51348-z},

year  = {2024},

date = {2024-08-23},

urldate = {2023-08-07},

journal = {Nature Communications},

volume = {15},

pages = {7283},

abstract = {The discovery of ambient superconductivity would mark an epochal breakthrough long-awaited for over a century, potentially ushering in unprecedented scientific and technological advancements. The recent findings on high-temperature superconducting phases in various hydrides under high pressure have ignited optimism, suggesting that the realization of near-ambient superconductivity might be on the horizon. However, the preparation of hydride samples tends to promote the emergence of various metastable phases, marked by a low level of experimental reproducibility. Identifying these phases through theoretical and computational methods entails formidable challenges, often resulting in controversial outcomes. In this paper, we consider N-doped LuH_{3} as a prototypical complex hydride: By means of machine-learning-accelerated force-field molecular dynamics, we have identified the formation of H_{2} molecules stabilized at ambient pressure by nitrogen impurities. Importantly, we demonstrate that this molecular phase plays a pivotal role in the emergence of a dynamically stable, low-temperature, experimental-ambient-pressure superconductivity. The potential to stabilize hydrogen in molecular form through chemical doping opens up a novel avenue for investigating disordered phases in hydrides and their transport properties under near-ambient conditions.},

keywords = {P07},

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{Sokolovic_2024a,

title = {How to cleave cubic perovskite oxides},

author = {Igor Sokolović and Michael Schmid and Ulrike Diebold and Martin Setvín},

url = {https://arxiv.org/abs/2408.08996},

year  = {2024},

date = {2024-08-16},

journal = {arXiv},

abstract = {Surfaces of cubic perovskite oxides attract significant attention for their physical tunability and high potential for technical applications. Bulk-terminated surfaces are desirable for theoretical modelling and experimental reproducibility, yet there is a lack of methods for preparing such well-defined surfaces. We discuss a method for strain-assisted cleaving of perovskite single crystals, using a setup easily transferable between different experimental systems. The details of the cleaving device and the procedure were optimized in a systematic study on the model SrTiO_{3}. The large-area morphology and typical distribution of surface terminations on cleaved SrTiO_{3}(001) is presented, with specific guidelines on how to distinguish well-cleaved surfaces from conchoidally fractured ones. The cleaving is applicable to other cubic perovskites, as demonstrated on KTaO_{3}(001) and BaTiO_{3}(001). This approach opens up a pathway for obtaining high-quality surfaces of this promising class of materials.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{Heid_2024a,

title = {Spatially resolved uncertainties for machine learning potentials},

author = {Esther Heid and Johannes Schörghuber and Ralf Wanzenböck and Georg K. H. Madsen},

url = {https://pubs.acs.org/doi/10.1021/acs.jcim.4c00904},

doi = {10.1021/acs.jcim.4c00904},

year  = {2024},

date = {2024-08-07},

urldate = {2024-08-07},

journal = {Journal of Chemical Information and Modeling},

volume = {64},

issue = {16},

pages = {6377--6387},

abstract = {Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab-initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted on the dataset composition itself. The reliable identification of erroneously predicted configurations to extend a given dataset is therefore of high priority. Yet, uncertainty estimation techniques have largely failed for machine learning potentials. Consequently, a general and versatile method to correlate energy or atomic force uncertainties with the model error has remained elusive to date. In the current work, we show that epistemic uncertainty cannot correlate with model error by definition, but can be aggregated over groups of atoms to yield a strong correlation. We demonstrate that our method correctly estimates prediction errors both globally per structure, and locally resolved per atom. The direct correlation of local uncertainty and local error is used to design an active learning framework based on identifying local sub-regions of a large simulation cell, and performing ab-initio calculations only for the sub-region subsequently. We successfully utilize this method to perform active learning in the low-data regime for liquid water.},

howpublished = {ChemRxiv},

keywords = {P09},

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{PhysRevResearch.6.033041,

title = {Global sampling of Feynman's diagrams through normalizing flow},

author = {Luca Leoni and Cesare Franchini},

url = {https://link.aps.org/doi/10.1103/PhysRevResearch.6.033041},

doi = {10.1103/PhysRevResearch.6.033041},

year  = {2024},

date = {2024-07-08},

journal = {Physical Review Research},

volume = {6},

issue = {3},

pages = {033041},

abstract = {Normalizing flows (NF) are powerful generative models with increasing applications in augmenting Monte Carlo algorithms due to their high flexibility and expressiveness. In this work we explore the integration of NF in the diagrammatic Monte Carlo (DMC) method, presenting an architecture designed to sample the intricate multidimensional space of Feynman's diagrams through dimensionality reduction. By decoupling the sampling of diagram order and interaction times, the flow focuses on one interaction at a time. This enables one to construct a general diagram by employing the same unsupervised model iteratively, dressing a zero-order diagram with interactions determined by the previously sampled order. The resulting NF-augmented DMC method is tested on the widely used single-site Holstein polaron model in the entire electron-phonon coupling regime. The obtained data show that the model accurately reproduces the diagram distribution by reducing sample correlation and observables' statistical error, constituting the first example of global sampling strategy for connected Feynman's diagrams in the DMC method},

keywords = {P07},

pubstate = {published},

tppubtype = {article}

}