Wolfgang Wenzel

Interoperability is a central challenge in modern materials science as scientific workflows increasingly need to integrate heterogeneous computational methods, experimental infrastructures, data platforms, and AI‐assisted systems. This perspective presents interoperability concepts from European digitalization initiatives in materials science namely, Platform MaterialDigital, NFDI‐MatWerk, and MaterialsCommons, which focus on workflow management systems (WfMSs), semantic representations, and knowledge graph technologies, while relating these developments to the broader landscape of materials informatics. Since various WfMSs address different requirements, interoperable abstraction layers such as the Python Workflow Definition are needed and enable workflows to be exchanged between systems while preserving reproducibility. Beyond technical interoperability, the review highlights the growing importance of semantic interoperability through ontology‐driven frameworks, including atomRDF, the Abstract Workflow Language, and semantikon. These approaches enrich workflows with machine‐readable semantic annotations, provenance information, and Resource Description Framework‐based knowledge graphs, enabling workflows to become semantically transparent and reusable scientific objects. The growing importance of semantically annotated workflows for the integration of computational and experimental workflows within Materials Acceleration Platforms and the emerging role of large language models and AI‐assisted workflow orchestration are also emphasized. Together, these developments outline a multilayered interoperability ecosystem that combines workflow execution, semantic reasoning, provenance tracking, and AI‐driven scientific automation within unified materials research infrastructures.

Materials science produces heterogeneous data across experiments, simulations, and industrial processes that often remain bound to local formats, manual procedures, and project‐specific software. Digital workflows address this fragmentation through explicit, repeatable, and machine‐actionable pipelines. This article examines 13 workflow contributions from the second and third funding phases of the MaterialDigital initiative. The contributions cover data acquisition and FAIR storage, simulation automation, multiscale integration, and AI/ML‐driven optimization. Here, AI‐readiness denotes the documented capacity of workflows and their artifacts to be reliably interpreted, executed, assessed, reused, and, where intended, invoked or adapted by automated systems; it does not require the direct application of artificial intelligence. Eight comparison aspects capture data and metadata, reusable artifacts, orchestration, robustness, cross‐scale coupling, transfer validation, learning and optimization, and adaptive or agent‐accessible operation. By distinguishing demonstrated capabilities from plans, the comparison shows how workflows support AI‐ready research both through integrated AI methods and through structured, traceable, and reusable pipelines. Across the projects, stable data structures, persistent artifacts, executable orchestration, and scientific validation recur as foundations, whereas adaptive operation and agent‐accessible interfaces remain specialized. Wider interoperability would additionally benefit from shared interfaces and explicit, portable descriptions of workflow artifacts.

In the advancement of organic semiconductor devices, computer simulations now provide performance predictions and valuable insights into the inner mechanisms of potential device architectures, streamlining the need for time-intensive laboratory experiments. The Kinetic Monte Carlo method is a well-established approach for simulations of organic semiconductors on a mesoscopic scale, modeling charge carrier hopping by sampling discrete time steps. However, this method exhibits slow convergence for complex device architectures and systems with high charge carrier densities as typical for doped injection layers or transport layers. Here, we demonstrate an approach to compute charge transport even in samples with high charge carrier densities, utilizing a Resistor Network model with time-independent resistances. Using this approach we can reproduce current densities for a variety of device architectures while drastically reducing the computational time. Hence, this method can be used to simulate charge transport in non-emitting device layers tenfold faster than with a pure Kinetic Monte Carlo simulation.

Solvatochromic shifts in UV-vis absorption of photoreactive chromophores and microenvironment effects in their light-induced [2+2] cycloaddition reactions are presently not fully understood. Spectral shifts observed experimentally could not be yet resolved in silico. The understanding of the reactivity of chromophores is not possible without the foundational reasoning of their solvatochromism. Here, using quantum mechanical (QM) calculations coupled to a multiscale computational scheme with molecular dynamics (MD) simulations, we demonstrate the role of structural flexibility of the styrylpyrene chromophore, its solvent-induced conformational, electrostatic and vibronic effects in adequate capturing of absorption spectral shifts. The methodological insights suggest computational schemes relevant to the design of advanced photoprogrammed systems and the capture of their photoreactivity.

Noncanonical nucleosomes at the centromere contain the histone variant CENP-A, playing a crucial role in chromosome segregation. CENP-A is highly regulated, including by the centromere protein N (CENP-N). Despite its importance, the protein-protein interactions within the CENP-A nucleosome-CENP-N complex remain poorly understood at a molecular level. Here, we employ the SIRAH coarse-grained molecular dynamics (MD) simulations to investigate dynamic mechanisms through which CENP-N binding to CENP-A nucleosome modulates structural changes of histones, contributing to the regulation processes. By the set of $\mu$s-long MDs, we reveal enhanced flexibility in the N-terminal region of CENP-A and the stabilization of its RG loop in the complex with CENP-N, thus reshaping the conformational structure of the CENP-A protein. We characterize these allosteric changes and analyze their influence on histone-DNA contacts. Moreover, we demonstrate that they have rather minor effects on the overall stability of the nucleosome and its compactness. A distance-based contact map analysis further elucidates key residues mediating the interaction between CENP-A and CENP-N, while umbrella sampling simulations quantify their binding free energy, which remains challenging to measure experimentally.

Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of Integrated Computational Materials Engineering (ICME), where state-of-the-art codes exist but remain cumbersome for non-experts. We address this bottleneck with GENIUS, an AI-agentic workflow that fuses a smart Quantum ESPRESSO knowledge graph with a tiered hierarchy of large language models supervised by a finite-state error-recovery machine. Here we show that GENIUS translates free-form human-generated prompts into Quantum ESPRESSO input files that pass early execution validation for ≈ 80% of 295 diverse benchmarks. Zero-shot generation succeeds for 14.2% of all prompts, and among cases that do not succeed initially, 76.3% are autonomously recovered by the automated error-handling loop, with the attempt-wise success rate decaying exponentially toward a 7% baseline. Compared with LLM-only baselines, GENIUS increases inference and computational efficiency and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, enabling large-scale screening and accelerating ICME design loops worldwide across academia and industry.

Interoperability and cross‐validation remain significant challenges in the computational materials discovery community. In this context, we introduce a common input/output standard designed for internal translation by various workflow managers (AiiDA, PerQueue, Pipeline Pilot, and SimStack) to produce results in a unified schema. This standard aims to enable engine‐agnostic workflow execution across multiple density functional theory (DFT) codes, including CASTEP, GPAW, Quantum ESPRESSO, and VASP. As a demonstration, we have implemented a workflow to calculate the open‐circuit voltage across several battery cathode materials using the proposed universal input/output schema. We analyze and resolve the challenges of reconciling energetics computed by different DFT engines and document the code‐specific idiosyncrasies that make straightforward comparisons difficult. Motivated by these challenges, we outline general design principles for robust automated DFT workflows. This work represents a practical step toward more reproducible and interoperable workflows for high‐throughput materials screening, while highlighting challenges of aligning electronic properties, especially for nonpristine structures.

Copper(I) complexes exhibit efficient luminescent properties, such as thermally activated delayed fluorescence (TADF), making them attractive for applications in organic light-emitting diodes or sensors. While copper(I) complexes with 2-pyridyl phosphine ligands have been well studied, this work explores the effect of positional isomerism by using 3-pyridyl phosphine instead of 2-pyridyl phosphine, leading to a previously unreported double-bridged distorted tetrahedral structure with an unusually long average of the Cu···Cu distance of 6.238 Å. This new structural motif, enabled by the unique coordination behavior of the 3-pyridyl ligand, leads to excitation-dependent emission behavior with typical TADF behavior and photoluminescence quantum yields ranging from 0.11 to 0.83. Quantum mechanical calculations indicate that the excited-state transitions exhibit primarily metal-to-ligand and halide-to-ligand charge transfer character or a combination of both, which enhances the TADF response. This study highlights how small changes in the ligand topology can lead to fundamentally different coordination modes and photophysical response. The results offer structure-property relationships for copper(I)-based emitters and the development of future ligand derivatives for tunable optical properties.

The first funding period of NFDI4Chem established a robust foundation for research data management (RDM) in chemistry by promoting FAIR data principles and creating a cohesive infrastructure to capture well-annotated data early in the lab through electronic lab notebooks (ELNs) and making this data available in public repositories. Key achievements include standardised data formats and metadata, a federated repository environment, and improved data visibility and accessibility. Training programs and outreach have significantly increased awareness and adoption of best RDM practices. In the second funding period, the consortium aims to advance these achievements by consolidating this infrastructure, developing a model for its sustainable maintenance and operation, and fostering cultural change for its widespread adoption. Goals include ensuring seamless data workflows from laboratories to open repositories, enhancing interoperability, and supporting innovative research through AI-ready data. The work plan is organised into six task areas (TAs). TA1 (Management) provides leadership and supports all other TAs in achieving their objectives. TA2 (Smart Lab) aims to develop a fully digital research environment, including an ELN as a modular platform. This environment will support data collection, management, storage, analysis, and sharing. Integrating devices and external resources will enable seamless data transfer to repositories. TA3 (Repositories) will consolidate the repository ecosystem. The goal is to integrate repositories into a federated system for better accessibility and interoperability, ensuring long-term data availability and sustainability. TA4 (Metadata, Data Standards, and Publication Standards) focuses on developing and promoting new data and metadata standards in an international community process. This includes applying ontologies to create a semantic foundation for linking research data, making it machine-readable and enabling knowledge graphs. TA5 (Community and Training) is dedicated to fostering a cultural shift towards digital chemistry through continuous engagement, collecting requirements, and providing extensive training and support through workshops and open education resources. It will promote FAIR-compliant machine learning applications, embedding RDM into academic curricula to ensure future scientists are well-versed in these practices. TA6 (Synergies and Cross-Cutting Topics) aims to enhance collaboration across NFDI consortia and beyond. This includes developing ontologies, terminology services, the search service, and other cross-cutting solutions, integrating these developments into existing infrastructure, enabling interdisciplinary data harmonisation and fostering machine learning applications.

Noncanonical nucleosomes at the centromere contain the histone variant CENP-A, playing a crucial role in chromosome segregation. CENP-A is highly regulated and its centromeric organization is partially regulated by the centromere protein N (CENP-N). Despite its importance, the protein–protein interactions within the complex formed between CENP-A nucleosomes and CENP-N remain poorly understood at a molecular level. Here, we employ the SIRAH coarse-grained molecular dynamics (MD) simulations to investigate the binding interface and structural rearrangements of proteins in the CENP-A nucleosome complexed with CENP-N. We aim to assess the stability of the CENP-A nucleosome and the change in its plasticity upon the CENP-N binding. By the set of µ s-long MDs, we reveal enhanced flexibility in the N-terminal region of CENP-A and stabilization of its RG loop in the complex with CENP-N. This is demonstrated to have rather minor effects on the overall stability of the nucleosome and changing its compactness. Nevertheless, the data suggest the binding of CENP-N allosterically changes the conformational states of CENP-A and impacts its interactions with other proteins in the histone core. A distance-based contact map analysis further elucidates key residues mediating the interaction between CENP-A and CENP-N, while umbrella sampling simulations quantify their binding free energy, which remains challenging to measure experimentally.