Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categories: Knowledge Infrastructure, systems that structure, retrieve, synthesize, and validate scientific information; and Action Systems, systems that execute, coordinate, or automate scientific work across computational and experimental environments. The submissions reveal a shift from single-purpose LLM tools toward integrated, multi-agent workflows that combine retrieval, reasoning, tool use, and domain-specific validation. Prominent themes include retrieval-augmented generation as grounding infrastructure, persistent structured knowledge representations, multimodal and multilingual scientific inputs, and early progress toward laboratory-integrated closed-loop systems. Together, these results suggest that LLMs are evolving from general-purpose assistants into composable infrastructure for scientific reasoning and action. This work provides a community snapshot of that transition and a practical taxonomy for understanding emerging LLM-enabled workflows in materials science and chemistry.

Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They cover methods from ground-state properties to lattice excitations and light-matter interaction, including time-resolved variants. Among the various electronic-structure codes, exciting is an all-electron full-potential package that has a very rich portfolio, with a particular focus on excitations. It implements the linearized augmented planewave plus local orbital (LAPW+LO) basis, which is known as the gold standard for solving the Kohn-Sham equations of density-functional theory (DFT). exciting also offers benchmark-quality results for a wide range of excited-state methods. In this review, we provide an overview of the most recent features implemented in exciting, accompanied by summaries on the state of the art of the underlying methodologies. They comprise DFT and time-dependent density-functional theory (TDDFT), density-functional perturbation theory (DFPT) for phonons and electron-phonon coupling, and many-body perturbation theory in terms of the G W approach and the Bethe-Salpeter equation (BSE). Moreover, exciting can handle resonant inelastic x-ray scattering (RIXS), pump-probe spectroscopy, as well as exciton-phonon coupling (EXPC). Finally, we cover workflows and a view on data and machine learning (ML). All aspects are demonstrated with examples for scientifically relevant materials.

The lack of machine-readable experimental data impedes data-driven discoveries in catalysis research. To advance the FAIR principles — guidelines to improve the Findability, Accessibility, Interoperability and Reuse of digital assets — we have developed a catalysis plugin (called the Catalysis App) for the NOMAD platform that supports standardized data upload and features integrated visualization. This infrastructure provides a robust foundation for machine-learning workflows and the direct comparison of experimental data with theory.

Soft lattices combined with strong electron-phonon coupling in metal halide perovskites result in a complex interplay between electronic and lattice degrees of freedom. This interplay complicates the interpretation of time-resolved excitation spectra like pump-probe spectra. Here, we develop a first-principles approach that combines a nonequilibrium extension of the Bethe-Salpeter equation with ab initio molecular dynamics to resolve the origin of transient absorption. This approach can quantitatively disentangle electronic and thermal lattice contributions across femtosecond-to-picosecond timescales. Exemplified with CH3NH3PbBr3, we find that on the femtosecond scale, both X-ray and optical transient absorption spectra are dominated by electronic contributions: Photoinduced Coulomb screening weakens the effective electron-hole interaction and blueshifts the excitonic resonances, whereas Pauli blocking is negligible. On the picosecond scale, thermal lattice contributions become essential, with distinct mechanisms dominating different spectral regions: Lattice vibrations lead to spectral redistribution in the X-ray transient absorption spectrum, whereas lattice expansion blueshifts the optical transient absorption spectrum.

By means of first-principles calculations, we investigate the role of electron-phonon interaction on the electronic structure of hybrid interfaces formed by MoS2 and monolayers of the organic molecules pyrene and pyridine, respectively. Quasiparticle energies are initially obtained within the G0W0 approximation and are subsequently used to evaluate the electron-phonon self-energy and momentum-resolved spectral functions to assess the temperature renormalization of the band structure. We find that the band-gap renormalization by zero-point vibrations of both hybrid systems is comparable to that of pristine MoS2, with a value of ∼80 meV. Pronounced features of molecular origin emerge in the spectral function of the valence region, which we attribute to satellites arising from out-of-plane vibrational modes of the organic monolayers. For pyrene, this satellite exhibits a predominantly molecular character, while for pyridine, it has a hybrid nature originating from the coupling of molecular vibrations to the MoS2 valence band.

Pump-probe spectroscopy is a powerful technique for investigating ultrafast exciton dynamics. However, developing a theoretical framework for modeling the transient response in photoexcited materials has remained a challenge. Here, we present a first-principles approach based on a non-equilibrium extension to the Bethe-Salpeter equation to simulate pump-probe spectroscopy and disentangle electronic and thermal contributions to the transient response. Applied to three prototypical semiconductors, i.e., the transition-metal dichalcogenide WSe2, the metal halide perovskite CsPbBr3, and the transition-metal oxide TiO2, the method obtains the transient spectra in excellent agreement with experiment. Our analysis reveals that distinct renormalization mechanisms shape the spectral shifts: Photoinduced Coulomb screening, as the dominant electronic effect, drives excitonic blueshifts, while Pauli blocking plays a minor role. Thermal effects induce redshifts on the picosecond time scale. We further demonstrate how key parameters, such as carrier population distribution, pump wavelength, and pump polarization, impact the transient absorption spectra, which offer direct control over the exciton resonance energy. Our approach establishes a framework for interpreting and tailoring pump-probe spectra, providing guidelines for exciton engineering and thus contributing to the design of energy-selective optoelectronic devices.

We present an ab initio framework for predicting resonant inelastic X-ray scattering (RIXS) in optically pumped materials. Our methodology is based on the Kramers-Heisenberg formula for the double-differential cross section formulated using the results of the Bethe-Salpeter equation (BSE) from many-body perturbation theory. To extend this approach to the time domain, we incorporate non-equilibrium charge-carrier distributions obtained from real-time, time-dependent density-functional theory (RT-TDDFT). Generalizing the RIXS implementation with respect to arbitrary polarizations, allows us to consider different orientations of incoming and outgoing light. We demonstrate our method's capabilities by studying RIXS at the K-edge of graphite for various non-equilibrium charge-carrier distributions, representing different delay times after optical pumping. Our results reveal angular dependencies in π- and σ-orbital-derived spectral regions, in good agreement with experiment.

Data-driven approaches to materials discovery rely on numerical representations of atomic structures as input for machine learning models. Inverting these descriptors - recovering atomic structures from their representations - is essential for most generative material design pipelines, yet it remains challenging, particularly for periodic systems. Existing inversion methods are either tailored to specific invertible descriptors or require candidate structures with similar atomic arrangements and compositions, limiting the exploration of novel regions in chemical and configurational space. Here, we propose a generalizable, similarity-driven sampling approach, powered by a novel stage-wise optimization strategy, to recover atom types, atomic positions, and unit cell shapes directly from a descriptor. Our approach requires only descriptor features and parameters as input without any prior structural knowledge. The capability of our method is demonstrated by the averaged Smooth Overlap of Atomic Positions (SOAP) descriptor.

We present a comprehensive investigation of reconstructions on β -Ga 2 O 3 ( 001 ) combining first-principles calculations with experimental observations. Using atomistic thermodynamics and replica-exchange grand-canonical molecular dynamics simulations, we explore the configurational space of possible reconstructions under varying chemical potentials of oxygen and gallium. Our calculations reveal several stable surface reconstructions, most notably a previously unreported 1 × 2 reconstruction consisting of paired GaO 4 tetrahedra that exhibits remarkable stability across a wide range of experimental growth conditions. In this reconstruction, two Ga atoms share one oxygen bond and are separated by a distance of 2.64 Å along the [010] direction. High-angle annular dark-field scanning transmission electron microscopy imaging of homoepitaxially grown (001) layers is consistent with the predicted structure. Additional investigations of possible indium substitution at the surface sites, which can occur during indium-mediated metal-exchange catalysis via molecular beam epitaxial growth, reveal a cooperative effect in In incorporation, with distinct stability regions for In-substituted structures under O-rich conditions. Our findings provide an understanding for controlling surface properties during epitaxial growth of β -Ga 2 O 3 ( 001 ) .

The field of catalysis currently lacks a structured repository for experimental data, and the publication of machine-readable datasets remains uncommon. To address this gap and support FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, we introduce a new plugin within the NOMAD platform for managing and publishing heterogeneous catalysis data, the nomad_catalysis plugin. This plugin enables the upload of structured experimental data and metadata with built-in visualization and alignment to the community-developed vocabulary Voc4Cat, ensuring long-term interpretability. In addition to facilitating efficient data sharing, the catalysis app offers intuitive search functionality, enabling researchers to quickly identify relevant catalytic reactions, catalyst materials, reaction conditions and kinetic properties. This infrastructure lays the foundation for advanced data analytics and machine learning applications, supporting more efficient and reproducible catalyst development.

We investigate the influence of vibrational screening on the excitonic and optical properties of solids based on first-principles electronic-structure calculations. We solve the Bethe-Salpeter equation-the state-of-the-art description of excitons-by explicitly accounting for phonon-assisted screening effects in the screened Coulomb interaction. We go beyond recently studied absorption onsets and account for screening effects on all excitonic states. In the examples of the polar semiconductors ZnS, MgO, and GaN, the exciton binding energies at the absorption onset are found to be renormalized by a few tens of meV. Similar effects are also found for higher-lying unbound electron-hole pairs, leading to red-shifts of the absorption peaks by up to 50 meV. Our analysis reveals that vibrational screening is dictated by long-range Fr & ouml;hlich coupling involving polar longitudinal optical phonons, whereas the remaining vibrational degrees of freedom are negligible. Overall, by elucidating the influence of phonon screening on the excitonic states and absorption spectra of these selected ionic semiconductors, this study contributes to advancing the ab initio methodology and the fundamental understanding of exciton-phonon coupling in solids.

Excitonics is an emerging field focused on exploiting and manipulating excitons generated through light-matter interactions. Advancing the field into X-ray excitonics requires precise energy and time control of core-exciton resonances, enabling non-linear X-ray phenomena such as element-specific X-ray transient gratings, and advancing material characterization. To achieve these objectives, it is essential to comprehend the role of many-body effects governing core-exciton dynamics. In this work, we address this challenge by combining experiments with an ab initio approach specifically developed to interpret pump-probe excitations. Applied to the prototypical wide-bandgap semiconductor ZnO, first-principles calculations reproduce experimental results and unveil how the density and distribution of photoexcited carriers dynamically tune Coulomb screening, thereby controlling core-exciton binding energies, while Pauli blocking remains negligible. These insights inform a method for dynamically controlling core-exciton resonances at absorption edges, achieving either a uniform spectral blue shift caused by thermalized carrier distributions on picosecond timescales, or distinct blue shifts for individual resonances, driven by time-dependent carrier distributions on femtosecond timescales. Excitonics provides a promising way to manipulate light-matter interactions for advanced optical applications, yet controlling core-exciton dynamics in the X-ray regime is challenging. Here, the authors combine experiments with an ab initio approach developed specifically for modelling pump-probe excitations, revealing how photoexcited carrier distributions can be used to control core-exciton resonances at absorption edges.

Hybrid perovskites are interesting optoelectronic materials. The perovskite ABX3 structure offers a vast compositional space, and we have identified over 300 perovskite ions. This flexibility enables tuneable properties and has significantly contributed to the success of perovskite optoelectronics. However, this diversity also leads to confusion, ambiguity, and inconsistencies causing challenges for data mining and machine learning applications. To address this issue, we propose guidelines and a JSON schema to standardize the reporting of perovskite compositions. The schema adheres to IUPAC recommendations and is designed to make data both human- and machine-readable. It captures key descriptors such as perovskite composition, molecular formula, SMILES representation, IUPAC name, and CAS number for each ion. To facilitate adoption, we have developed utilities to automatically generate comprehensive and standardized perovskite descriptions from standard ion abbreviations and stoichiometric coefficients. Additionally, we provide a curated database of all identified perovskite ions with associated descriptive data.

Big data has ushered in a new wave of predictive power using machine-learning models. In this work, we assess what big means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science and condensed matter. Contributions in this roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green’s function approaches, quantum and ab initio simulations.

The combination of two-dimensional materials into heterostructures offers new opportunities for the design of optoelectronic devices with tunable properties. However, computing electronic and optical properties of such systems using state-of-the-art methodology is challenging due to their large unit cells. This is in particular so for highly-precise all-electron calculations within the framework of many-body perturbation theory, which come with high computational costs. Here, we extend an approach that allows for the efficient calculation of the non-interacting polarizability, previously developed for planewave basis sets, to the (linearized) augmented planewave (L)APW method. This approach is based on an additive ansatz, which computes and superposes the polarizabilities of the individual components in their respective unit cells. We implement this formalism in the G_0W_0 module of the exciting code and implement an analogous approach for BSE calculations. This allows the calculation of highly-precise optical spectra at low cost. So-obtained results of the quasi-particle band structure and optical spectra are demonstrated for bilayer WSe_2 and pyridine@MoS_2 in comparison with exact reference calculations.

We present an automatized approach towards maximally localized Wannier functions (MLWFs) applicable to both occupied and unoccupied states. We overcome limitations of the standard optimized projection function (OPF) method and its approximations by providing an exact expression for the gradient of the Wannier spread functional with respect to a single semi-unitary OPF matrix. Moreover, we demonstrate that the localization of the resulting Wannier functions (WFs) can be further improved by including projections on reasonably localized WFs, so-called self-projections.

Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry.