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Researchers at the Helmholtz-Zentrum Dresden-Rossendorf rely on a variety of systems and tools when it comes to administer their research data. Processes involving research data management include the project planning phase (proposal submission to the beamtime proposal management system, the creation of data management plans and data policies), the documentation during the experiment or simulation campaign (electronic laboratory notebooks, wiki pages), backup- and archival systems and the final journal and data publications (collaborative authoring tools, meta-data catalogs, software and data repositories, publication systems). In addition, modern research projects are often required to interact with a variety of software stacks and workflow management systems to allow reproducibility on the underlying IT infrastructure. The "HELmholtz ScIentific Project WORkflow PlaTform" (HELIPORT), which is currently developed by researchers at HZDR and their collaborators, tries to facilitate the management of research data and metadata by providing an overarching guidance system which combines all the information by interfacing the underlying processes and even includes a workflow engine which can be used to automate processes like data analysis or data retrieval.

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as \textit{dhcp}, \textit{hcp}, and \textit{fcc}, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several new low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials.

In recent years, density functional theory (DFT) has revolutionzed simulations in the domain of warm dense matter (WDM) which focuses on unraveling behavior of matter in fusion reactor, planetary interiors and other areas of high energy physics. The success of DFT relies on accurate approximations of the exchange-correlation (XC) effects based on exact quantum monte carlo (QMC) results. In this contribution, we summarize our recent work toward improving XC at finite temperatures, which is called thermal PBE[1] and incorporting the improvements in LIBXC[2], a library widely used in the material science community. We highlight the software engineering challenges in the field of high performance computing including: (i) deployment of continuous integration (CI) using Gitlab runners, (ii) incorporating containers for CI and reproducibility and (iii) refactoring strategies. We also highlight the importance of research software engineering (RSE) for validating accurate utilization of community high-performance-computing codes. References [1] https://doi.org/10.48550/arXiv.2308.03319 [2] https://doi.org/10.1016/j.softx.2017.11.002

Recently, machine-learned interatomic potentials (ML-IAPs) have emerged as a solution to the computational limitations of density functional theory (DFT)-based approaches, enabling the modeling of large systems with hundreds or even thousands of atoms. Here, we demonstrate the efficacy of automated and systematic methods for training and validating transferable ML-IAPs through global optimization techniques. We utilize the PyFLAME code [1] to construct a highly transferable neural network potential. With this accurate and fast potential, we systematically investigate the potential energy surfaces (PESs) of FeH through global sampling using the minima hopping method [2] over a wide range of pressures. This comprehensive exploration enables us to predict stable and metastable iron hydrides from 0 to 100 GPa. Our analysis reveals the experimentally observed global minimum structures -the dhcp, hcp, and fcc phases- in agreement with previous studies. Furthermore, our exploration of the PESs of FeH at various pressures uncovers numerous interesting modifications and stacking faults of the aforementioned phases, including several remarkably low-enthalpy structures. This investigation led to the discovery of a rich array of novel stoichiometric crystal phases of FeH across a wide pressure range, confirming the presence of coexisting regions containing known FeH structures. This finding demonstrates one of the benefits of using large-scale structure prediction techniques to uncover the PESs of materials. [1] H. Mirhosseini, H. Tahmasbi, S. R. Kuchana, S. A. Ghasemi, and T. D. Kühne, Comput. Mater. Sci. 197, 110567 (2021). [2] M. Amsler and S. Goedecker, J. Chem. Phys. 133, 224104 (2010).

Here, we provide the training datasets and the resulting neural network potential for exploring the potential energy surfaces of the FeH system using the minima hopping method. Additionally, data for the minima structures identified in this work are included.

Using the methodology of Kozlowski et al. [arXiv 2308.03319 (2023)] to extend the temperature dependence of the Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation, we implement the thermal equivalent of the PBE functional (tPBE) in a plane wave code to study the equilibrium properties such as energies, pressures, and forces of warm dense matter using density functional theory and linear-response properties such as the electrical conductivity, dynamic structure factor using time-dependent density functional theory. In addition, we compare the effects with the thermal equivalent of LDA and the ground-state LDA and PBE functionals.

Understanding the composition of Earth’s core and mantle is a major challenge in geoscience and materials science. The core is primarily made of iron, but its density is known to be slightly lower than pure iron. Hydrogen contributes to this density deficit, leading to significant interest in the properties and structure of iron hydrides under high pressure. Previous studies have shown that the dhcp phase of FeH remains stable at lower pressures (10-40 GPa) but undergoes phase transitions to hcp and fcc phases at higher pressures. This study focuses on a theoretical exploration of the potential energy surfaces (PESs) of FeH under varying pressure conditions. The objective is to demonstrate the effectiveness of automated and systematic methods for training and validating transferable machine-learned interatomic potential (ML-IAP) using global optimization techniques. Utilizing this potential, which significantly reduces computational costs, the phase diagram of the stoichiometric Fe-H system is analyzed across a range of pressures. To achieve this, we utilize the PyFLAME code to construct a highly transferable ML-IAP. With this accurate potential, the PESs of bulk FeH structures are systematically investigated through global sampling using the minima hopping method. This comprehensive exploration enables the prediction of stable and metastable iron hydrides from 0 to 100 GPa. Density functional theory calculations are conducted to refine the predicted structures and evaluate their dynamical stability. The findings of this study reveal a wide range of novel low-energy polymorphs of FeH at each pressure level, alongside the recovery of well-known structures in the literature.

The structure and properties of iron hydrides under pressure have been of interest to geoscientists. At ambient conditions, there are no stable solid iron hydrides. Previous theoretical and experimental studies suggest that the double hcp phase of FeH is stable at low pressures with phase transitions to the hcp and fcc phases up to 80 and 140 GPa, respectively. Here, we present a theoretical investigation of the potential energy surfaces of FeH at high pressure. We construct a highly transferable machine-learned interatomic potential with a hierarchical approach using the PyFLAME code. Then, using this fast and accurate neural network potential, we systematically explore the potential energy surfaces of bulk structures of FeH by global sampling using the minima hopping method, to predict stable and metastable iron hydrides up to 200 GPa. We have carried out density functional theory calculations to refine the predicted structures and to evaluate the dynamical stability of selected structures as well. In an automated and systematic approach, we are going to show how a transferable machine-learned interatomic potential can be trained and validated using global optimization and analyze the phase diagram of the stoichiometric Fe-H system under pressure.

Understanding the composition of Earth's core and mantle is a major challenge in geoscience and materials science. The core is primarily made of iron, but its density is known to be slightly lower than pure iron. Hydrogen contributes to this density deficit, leading to significant interest in the properties and structure of iron hydrides under high pressure. Previous studies have shown that the dhcp phase of FeH remains stable at lower pressures (10-40 GPa) but undergoes phase transitions to hcp and fcc phases at higher pressures. This study focuses on a theoretical exploration of the potential energy surfaces (PESs) of FeH under varying pressure conditions. The objective is to demonstrate the effectiveness of automated and systematic methods for training and validating transferable machine-learned interatomic potential (ML-IAP) using global optimization techniques. By utilizing this potential, which significantly reduce computational costs, the phase diagram of the stoichiometric Fe-H system is analyzed across a range of pressures. To achieve this, we utilize the PyFLAME code to construct a highly transferable ML-IAP. With this accurate potential, the PESs of bulk FeH structures are systematically investigated through global sampling using the minima hopping method. This comprehensive exploration enables the prediction of stable and metastable iron hydrides from 0 to 100 GPa. Density functional theory calculations are conducted to refine the predicted structures and evaluate their dynamical stability. The findings of this study reveal a wide range of novel low-energy polymorphs of FeH at each pressure level, alongside the recovery of well-known structures in the literature.

AiiDA is an open-source Python infrastructure for devising complex workflows associated with modern computational science and streamlining the four core pillars of the ADES model: Automation, Data, Environment, and Sharing. In this contribution, we showcase features of AiiDA like workflow-forging, high-throughput capability and data provenance as implemented in the AiiDA-FLEUR plugin. Finally, we address the possibility of managing AiiDA-projects through HELIPORT.

Zur Analyse der entstehenden Detektordaten bei dem Mu2e Experiment am Fermilab soll die Datenauswertung mit Field Programmable Gate Array (FPGA) erfolgen. Diese übernehmen die notwendige Vorverarbeitung und Reduktion der Messdaten, noch während der Durchführung der Messung. Die dabei ausgeführten Anwendungen werden standardmäßig durch Algorithmen realisiert. Eine dieser Anwendungen führt die Klassifikation der ermittelten Pulsdaten durch. Mit den Testläufen an der gELBE Bremstrahlungs-Beamline am Helmholtz-Zentrum Dresden-Rossendorf (HZDR) konnte für das zukünftige Experiment eine große Menge dieser Datensätze erfasst werden. Diese dienen zur Charakterisierung des Detektorsystems und wurden mit einem Lanthanbromid (LaBr) Detektor gemessen. Für die Pulsdatenklassifikation wird auf der Basis des Algorithmus und der erfassten Datensätze, ein neuronales Netzwerk erstellt, trainiert und validiert. Um bei diesen Schritten etablierte Machine Learning Frameworks zu verwenden, wird für die Portierung des Netzwerks in eine High-Level Synthese (HLS) Sprache die Software hls4ml verwendet. Dabei werden verschiedene Konfigurationen genutzt, um unterschiedlich optimierte Implementierungen zu generieren. Zum Evaluieren erfolgt die Ausführung der Implementierungen auf einer Xilinx Alveo Accelerator Card.

When dealing with research data management, researchers at Helmholtz- Zentrum Dresden – Rossendorf (HZDR) face a variety of systems and tools. These range from the project planning phase (proposal management, data management plans and policies), over documentation during the experiment or simulation campaign, to the publication (collaborative authoring tools, metadata catalogs, publication systems, data repositories). In addition, modern research projects usually are required to interact with a variety of software stacks and workflow management systems to allow comprehensi- ble and FAIR science on the underlying IT infrastructure (HPC, data storage, network file systems, archival). This article first demonstrates the data management systems and services provided at HZDR, followed by an overview of a self-developed guidance system. It is concluded by a real-world example.

Modeling warm dense matter is relevant for various applications including the interior of gas giants and exoplanets, inertial confinement fusion, and ablation of metals. Ongoing and upcoming experimental campaigns in photon sources around the globe rely on numerical simulations that are accurate on the level of electronic structures. In that regard, density functional theory molecular dynamics (DFT-MD) simulations [1] have been widely used to compute thermodynamical properties of warm dense matter. However, two challenges impede further progress: (1) DFT-MD becomes computationally infeasible with increasing temperature (2) finite-size effects render many computational observables inaccurate because DFT-MD is limited to a few hundred atoms on current HPC platforms. Recently, molecular dynamics simulations using machine learning-based interatomic potentials (ML-IAP) could overcome these computational limitations. Here, we propose a method to construct ML-IAPs from DFT data based on SNAP descriptors [2]. We investigate the transferability of ML-IAPs over a large range of temperatures (1,000 to 100,000 K) which currently is a topic of active research. References: [1] G. Kresse and J. Hafner, Physical Review B 47, 558 (1993). [2] A. P. Thompson, L. P. Swiler, C. R. Trott, S. M. Foiles, and G. J. Tucker, Journal of Computational Physics, 285, 316-330, 2015.

HELIPORT is a data management solution that aims at making the components and steps of the entire research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles. Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment’s life cycle steps. Computational agents can interact with HELIPORT via a REST API that allows access to all components, and landing pages that allow for export of digital objects in various standardized formats and schemas. An overall digital object graph combining the metadata harvested from all sources provides scientists with a visual representation of interactions and relations between their digital objects, as well as their existence in the first place. Through the integrated computational workflow systems, HELIPORT can automate calculations using the collected metadata. By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for data management.

Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein a new machine-learned ab-initio derived molecular-spin dynamics (MSD) methodology with explicit treatment for longitudinal spin-fluctuations is utilized to probe the dynamic phase-diagram of iron. This framework uniquely enables an accurate resolution of the phase-transition kinetics and Earth-core elastic properties, as highlighted by compressional wave velocity and adiabatic bulk moduli measurements. In addition, a unique coupling of MSD with time-dependent density functional theory enables gauging electronic transport properties, critically important for resolving geodynamo dynamics.

Electron dynamics of anatase TiO2 under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO2 to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of 1×1010 to 9×1012 W/cm2, with a transition from multiphoton to tunneling ionization occurring at 9×1012 W/cm2. We observe a sudden increase in energy and the number of excited electrons beyond 1×1013 W/cm2, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO2 for 800 nm to be 0.1 J/cm2. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index (n2) of TiO2 to be 3.54×10−11 cm2/W.

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties under Earth-core conditions calls for sophisticated theoretical methods that can support diagnostics. We present results of the electrical conductivity within the pressure and temperature ranges found in Earth's core from simulating microscopic Ohm's law using time-dependent density functional theory. Our predictions provide a new perspective on resolving discrepancies in recent experiments.

We study the electrical and thermal conductivity of iron at high pressures using time-dependent density functional theory. In doing so, we particularly consider the impact of a Hubbard correction (+\textit{U}) specifically for regions where strong electron correlations are present. Using the TDDFT+U methodology, we examine the anisotropy in the thermal conductivity of HCP iron, which may provide insights into the transport properties at conditions relevant to the core-mantle boundary and the interior of the Earth.

At the High-Field High-Repetition-Rate Terahertz facility (TELBE) [1] at the Helmholtz-Zentrum Dresden-Rossendorf, ultrafast terahertz-induced dynamics can be probed in various states of matter with highest precision. The TELBE source is available for external users who are not familiar with the experiments's data management pipelines. This is especially a challenge as the measurements at TELBE are data intensive, producing as much as 20GB per experiment over few minutes. In this contribution, we present the guidance system HELIPORT [3] which manages the metadata of the associated project proposal and integrates systems for workflow management, electronic lab documentation and others. We showcase the integrated workflow for post-processing of the experimental data at TELBE with in-built exchange of metadata between our proposal submission service, the experiment control software and workflows managed and executed using UNICORE [2]. Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment's life cycle steps. By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for FAIR data management. [1] https://doi.org/10.1063/1.4978042 [2] https://doi.org/10.1109/HPCSim.2016.7568392 [3] https://doi.org/10.1145/3456287.3465477

HELIPORT is a data management solution that aims at making the components and steps of the entire research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles. Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment’s life cycle steps. Computational agents can interact with HELIPORT via a REST API that allows access to all components, and landing pages that allow for export of digital objects in various standardized formats and schemas. An overall digital object graph combining the metadata harvested from all sources provides scientists with a visual representation of interactions and relations between their digital objects, as well as their existence in the first place. Through the integrated computational workflow systems, HELIPORT can automate calculations using the collected metadata. By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for data management.

We present a comprehensive investigation of the electrical and thermal conductivity of iron under high pressures at ambient temperature, employing the real-time formulation of time-dependent density functional theory (RT-TDDFT). Specifically, we examine the influence of a Hubbard correction (+U) to account for strong electron correlations. Our calculations based on RT-TDDFT demonstrate that the evaluated electrical conductivity for both high-pressure body-centered cubic (BCC) and hexagonal close-packed (HCP) iron phases agrees well with experimental data. Furthermore, we explore the anisotropy in the thermal conductivity of HCP iron under high pressure, and our findings are consistent with experimental observations. Interestingly, we find that the incorporation of the +U correction significantly impacts the ground state and linear response properties of iron at pressures below 50 GPa, with its influence diminishing as pressure increases. This study offers valuable insights into the influence of electronic correlations on the electronic transport properties of iron under extreme conditions.

Linear-response time-dependent density functional theory (LR-TDDFT) simulations of disordered extended systems require averaging over different snapshots of ion configurations to minimize finite size effects due to the snapshot-dependence of the electronic density response function and related properties. We present a consistent scheme for the computation of the macroscopic Kohn–Sham (KS) density response function connecting an average over snapshot values of charge density perturbations to the averaged values of KS potential variations. This allows us to formulate the LR-TDDFT within the adiabatic (static) approximation for the exchange–correlation (XC) kernel for disordered systems, where the static XC kernel is computed using the direct perturbation method [Moldabekov et al., J. Chem. Theory Comput. 19, 1286 (2023)]. The presented approach allows one to compute the macroscopic dynamic density response function as well as the dielectric function with a static XC kernel generated for any available XC functional. The application of the developed workflow is demonstrated for the example of warm dense hydrogen. The presented approach is applicable for various types of extended disordered systems, such as warm dense matter, liquid metals, and dense plasmas.

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

Electron dynamics of anatase TiO2 under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO2 to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of 1×1010 to 9×1012 W/cm2, with a transition from multiphoton to tunneling ionization occurring at 9×1012 W/cm2. We observe a sudden increase in energy and the number of excited electrons beyond 1×1013 W/cm2, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO2 for 800 nm to be 0.1 J/cm2. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index (n2) of TiO2 to be 3.54×10−11 cm2/W.

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties under Earth-core conditions calls for sophisticated theoretical methods that can support diagnostics. We present results of the electrical conductivity within the pressure and temperature ranges found in Earth's core from simulating microscopic Ohm's law using time-dependent density functional theory. Our predictions provide a new perspective on resolving discrepancies in recent experiments.

When dealing with research data management, researchers at Helmholtz- Zentrum Dresden – Rossendorf (HZDR) face a variety of systems and tools. These range from the project planning phase (proposal management, data management plans and policies), over documentation during the experiment or simulation campaign, to the publication (collaborative authoring tools, metadata catalogs, publication systems, data repositories). In addition, modern research projects usually are required to interact with a variety of software stacks and workflow management systems to allow comprehensi- ble and FAIR science on the underlying IT infrastructure (HPC, data storage, network file systems, archival). This article first demonstrates the data management systems and services provided at HZDR, followed by an overview of a self-developed guidance system. It is concluded by a real-world example.

Data processing or analysis workflows are generally understood as processes running without any user intervention where usually only a small set of parameters being provided upon workflow submission, adjustment of which is also limited by low turnaround rates of workflow runs due to scheduling alone. Many types of experimental data analyses require manual experimentation with parameters to succeed, necessitating interactivity and fast iteration. In this talk we present examples of interactive workflow applications at HZDR, from data analysis and simulation; discuss challenges arising from differences to completely automated workflows and lay-out the related data-provenance and project-resource management features we envision for the HELIPORT workflow platform.

Understanding the composition of Earth’s core and mantle is a major challenge in geoscience and materials science. The core is primarily made of iron, but its density is known to be slightly lower than pure iron. Hydrogen contributes to this density deficit, leading to significant interest in the properties and structure of iron hydrides under high pressure. Previous studies have shown that the dhcp phase of FeH remains stable at lower pressures (10-40 GPa) but undergoes phase transitions to hcp and fcc phases at higher pressures. This study focuses on a theoretical exploration of the potential energy surfaces (PESs) of FeH under varying pressure conditions. The objective is to demonstrate the effectiveness of automated and systematic methods for training and validating transferable machine-learned interatomic potential (ML-IAP) using global optimization techniques. Utilizing this potential, which significantly reduces computational costs, the phase diagram of the stoichiometric Fe-H system is analyzed across a range of pressures. To achieve this, we utilize the PyFLAME code to construct a highly transferable ML-IAP. With this accurate potential, the PESs of bulk FeH structures are systematically investigated through global sampling using the minima hopping method. This comprehensive exploration enables the prediction of stable and metastable iron hydrides from 0 to 100 GPa. Density functional theory calculations are conducted to refine the predicted structures and evaluate their dynamical stability. The findings of this study reveal a wide range of novel low-energy polymorphs of FeH at each pressure level, alongside the recovery of well-known structures in the literature.

Understanding the composition of Earth's core and mantle is a major challenge in geoscience and materials science. The core is primarily made of iron, but its density is known to be slightly lower than pure iron. Hydrogen contributes to this density deficit, leading to significant interest in the properties and structure of iron hydrides under high pressure. Previous studies have shown that the dhcp phase of FeH remains stable at lower pressures (10-40 GPa) but undergoes phase transitions to hcp and fcc phases at higher pressures. This study focuses on a theoretical exploration of the potential energy surfaces (PESs) of FeH under varying pressure conditions. The objective is to demonstrate the effectiveness of automated and systematic methods for training and validating transferable machine-learned interatomic potential (ML-IAP) using global optimization techniques. By utilizing this potential, which significantly reduce computational costs, the phase diagram of the stoichiometric Fe-H system is analyzed across a range of pressures. To achieve this, we utilize the PyFLAME code to construct a highly transferable ML-IAP. With this accurate potential, the PESs of bulk FeH structures are systematically investigated through global sampling using the minima hopping method. This comprehensive exploration enables the prediction of stable and metastable iron hydrides from 0 to 100 GPa. Density functional theory calculations are conducted to refine the predicted structures and evaluate their dynamical stability. The findings of this study reveal a wide range of novel low-energy polymorphs of FeH at each pressure level, alongside the recovery of well-known structures in the literature.

HELIPORT is a data management solution that aims at making the components and steps of the entire research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles. Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment’s life cycle steps. Computational agents can interact with HELIPORT via a REST API that allows access to all components, and landing pages that allow for export of digital objects in various standardized formats and schemas. An overall digital object graph combining the metadata harvested from all sources provides scientists with a visual representation of interactions and relations between their digital objects, as well as their existence in the first place. Through the integrated computational workflow systems, HELIPORT can automate calculations using the collected metadata. By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for data management.

The structure and properties of iron hydrides under pressure have been of interest to geoscientists. At ambient conditions, there are no stable solid iron hydrides. Previous theoretical and experimental studies suggest that the double hcp phase of FeH is stable at low pressures with phase transitions to the hcp and fcc phases up to 80 and 140 GPa, respectively. Here, we present a theoretical investigation of the potential energy surfaces of FeH at high pressure. We construct a highly transferable machine-learned interatomic potential with a hierarchical approach using the PyFLAME code. Then, using this fast and accurate neural network potential, we systematically explore the potential energy surfaces of bulk structures of FeH by global sampling using the minima hopping method, to predict stable and metastable iron hydrides up to 200 GPa. We have carried out density functional theory calculations to refine the predicted structures and to evaluate the dynamical stability of selected structures as well. In an automated and systematic approach, we are going to show how a transferable machine-learned interatomic potential can be trained and validated using global optimization and analyze the phase diagram of the stoichiometric Fe-H system under pressure.

AiiDA is an open-source Python infrastructure for devising complex workflows associated with modern computational science and streamlining the four core pillars of the ADES model: Automation, Data, Environment, and Sharing. In this contribution, we showcase features of AiiDA like workflow-forging, high-throughput capability and data provenance as implemented in the AiiDA-FLEUR plugin. Finally, we address the possibility of managing AiiDA-projects through HELIPORT.

Modeling warm dense matter is relevant for various applications including the interior of gas giants and exoplanets, inertial confinement fusion, and ablation of metals. Ongoing and upcoming experimental campaigns in photon sources around the globe rely on numerical simulations that are accurate on the level of electronic structures. In that regard, density functional theory molecular dynamics (DFT-MD) simulations [1] have been widely used to compute thermodynamical properties of warm dense matter. However, two challenges impede further progress: (1) DFT-MD becomes computationally infeasible with increasing temperature (2) finite-size effects render many computational observables inaccurate because DFT-MD is limited to a few hundred atoms on current HPC platforms. Recently, molecular dynamics simulations using machine learning-based interatomic potentials (ML-IAP) could overcome these computational limitations. Here, we propose a method to construct ML-IAPs from DFT data based on SNAP descriptors [2]. We investigate the transferability of ML-IAPs over a large range of temperatures (1,000 to 100,000 K) which currently is a topic of active research. References: [1] G. Kresse and J. Hafner, Physical Review B 47, 558 (1993). [2] A. P. Thompson, L. P. Swiler, C. R. Trott, S. M. Foiles, and G. J. Tucker, Journal of Computational Physics, 285, 316-330, 2015.

Zur Analyse der entstehenden Detektordaten bei dem Mu2e Experiment am Fermilab soll die Datenauswertung mit Field Programmable Gate Array (FPGA) erfolgen. Diese übernehmen die notwendige Vorverarbeitung und Reduktion der Messdaten, noch während der Durchführung der Messung. Die dabei ausgeführten Anwendungen werden standardmäßig durch Algorithmen realisiert. Eine dieser Anwendungen führt die Klassifikation der ermittelten Pulsdaten durch. Mit den Testläufen an der gELBE Bremstrahlungs-Beamline am Helmholtz-Zentrum Dresden-Rossendorf (HZDR) konnte für das zukünftige Experiment eine große Menge dieser Datensätze erfasst werden. Diese dienen zur Charakterisierung des Detektorsystems und wurden mit einem Lanthanbromid (LaBr) Detektor gemessen. Für die Pulsdatenklassifikation wird auf der Basis des Algorithmus und der erfassten Datensätze, ein neuronales Netzwerk erstellt, trainiert und validiert. Um bei diesen Schritten etablierte Machine Learning Frameworks zu verwenden, wird für die Portierung des Netzwerks in eine High-Level Synthese (HLS) Sprache die Software hls4ml verwendet. Dabei werden verschiedene Konfigurationen genutzt, um unterschiedlich optimierte Implementierungen zu generieren. Zum Evaluieren erfolgt die Ausführung der Implementierungen auf einer Xilinx Alveo Accelerator Card.

Top-Level Architecture of the proposed HZDR Data Management Strategy with additional description of the various systems and services.&nbsp

The guidance system HELIPORT aims to make the entire life cycle of a project at the HZDR searchable, accessible, complete and reusable according to the FAIR principles, mentioned below. In particular, our data management solution deals with the areas from the generation of the data to the publication of primary research data, the workflows carried out and the actual research results. For this purpose, a concept was developed which shows the various essential components and their connections. Descriptions of the individual components can be found in our RODARE publication: 10.14278/rodare.193

In our HELIPORT workshop, we provided insights into our project and share our results. In addition, we would like to provide a platform for the presentation of similar projects, as well as extensions or integrations from the surrounding research areas. The overall goal of the workshop is bringing together different institutions with similar challenges and establishing a community around our HELIPORT project. We therefore encouraged our community to submit an abstract for a talk or poster. The submissions are dedicated to four thematic points: • HELIPORTuse-cases, • Scientificprojectandmetadatamanagement, • Experiment-specificandoverallmetadataand • Scientificworkflows. HELIPORT (Helmholtz ScIentific Project WORkflow PlaTform) itself is a project funded by the Helmholtz Metadata Collaboration, and runs from July 2021 until June 2023. HELIPORT aims to make the entire life cycle of a scientific project findable, accessible, interoperable and reusable according to the FAIR principles. In particular, our data management solution deals with the areas from the generation of the data to the publication of primary research data, the workflows carried out and the actual research results. For this purpose, a concept was developed which shows the various essential components and their connections.

Simulation and literature data on the electrical and thermal conductivity of iron. The raw simulation data was generated from time-dependent density functional theory calculations. Post-processing was applied to obtain the transport properties (conductivities) as described in the associated journal publication. The literature data was compiled from available publication data as referenced in the associated journal publication.

Data and input scripts of the project "Ab initio insights on the ultrafast strong-field dynamics of anatase TiO2".

Simulation and literature data on the electrical and thermal conductivity of high-pressure iron. The raw simulation data was generated from time-dependent density functional theory calculations. Post-processing was applied to obtain the transport properties (conductivities) as described in the associated journal publication. The literature data was compiled from available publication data as referenced in the associated journal publication.

Here, we provide FitSNAP and DAKOTA input scripts and DFT-MD training data sets used for the generation of transferable SNAP ML-IAP for aluminum.

Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein a new machine-learned ab-initio derived molecular-spin dynamics (MSD) methodology with explicit treatment for longitudinal spin-fluctuations is utilized to probe the dynamic phase-diagram of iron. This framework uniquely enables an accurate resolution of the phase-transition kinetics and Earth-core elastic properties, as highlighted by compressional wave velocity and adiabatic bulk moduli measurements. In addition, a unique coupling of MSD with time-dependent density functional theory enables gauging electronic transport properties, critically important for resolving geodynamo dynamics.

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as \textit{dhcp}, \textit{hcp}, and \textit{fcc}, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several new low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials.

Electron dynamics of anatase TiO2 under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO2 to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of 1×1010 to 9×1012 W/cm2, with a transition from multiphoton to tunneling ionization occurring at 9×1012 W/cm2. We observe a sudden increase in energy and the number of excited electrons beyond 1×1013 W/cm2, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO2 for 800 nm to be 0.1 J/cm2. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index (n2) of TiO2 to be 3.54×10−11 cm2/W.

We study the electrical and thermal conductivity of iron at high pressures using time-dependent density functional theory. In doing so, we particularly consider the impact of a Hubbard correction (+\textit{U}) specifically for regions where strong electron correlations are present. Using the TDDFT+U methodology, we examine the anisotropy in the thermal conductivity of HCP iron, which may provide insights into the transport properties at conditions relevant to the core-mantle boundary and the interior of the Earth.

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties under Earth-core conditions calls for sophisticated theoretical methods that can support diagnostics. We present results of the electrical conductivity within the pressure and temperature ranges found in Earth's core from simulating microscopic Ohm's law using time-dependent density functional theory. Our predictions provide a new perspective on resolving discrepancies in recent experiments.

Modeling warm dense matter is relevant for various applications including the interior of gas giants and exoplanets, inertial confinement fusion, and ablation of metals. Ongoing and upcoming experimental campaigns in photon sources around the globe rely on numerical simulations which are accurate on the level of electronic structures. In that regard, density functional theory molecular dynamics (DFT-MD) simulations have been widely used to compute dynamical and thermodynamical properties of warm dense matter. However, two challenges impede further progress: (1) DFT-MD becomes computational infeasible with increasing temperature (2) finite-size effects render many computational observables inaccurate, because DFT-MD is limited to a few hundred atoms on current HPC platforms. Recently, molecular dynamics simulations using machine learning-based interatomic potentials (ML-IAP) could overcome these computational limitations. Here, we propose a method to construct ML-IAPs from DFT data based on SNAP descriptors [1]. We demonstrate our workflow for aluminum. In particular, we investigate the transferability of ML-IAPs over a large range of temperatures and pressures, which currently is a topic of active research. References: [1] A. P. Thompson, L. P. Swiler, C. R. Trott, S. M. Foiles, and G. J. Tucker, Journal of Computational Physics, 285, 316-330, 2015.

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties calls for sophisticated theoretical methods that can support diagnostics. We present electrical conductivity results from simulating microscopic Ohm’s law using the real-time formalism of time-dependent density functional theory for conditions ranging from high-pressure solid at ambient temperature to earth-core conditions.