arXiv subjects

Morten M. Smedskjaer

Publications and source records attributed to Morten M. Smedskjaer.

At least 19 recordsLinked to original sources

arXiv2026

Vitrification-Devitrification Enables Tunable Photonic and Gas Sorption Properties of Zeolitic Imidazolate Frameworks

Zeolitic imidazolate framework (ZIF) glasses represent an emerging family of melt-quenched glasses, which exhibit immense potential for applications in gas separation, energy storage, and optics. However, their intrinsic porosity remains elusive due to the inherent challenges in resolving their disordered atomistic structures. Here, we systematically investigate the porosity of ZIF-4 and ZIF-62 crystals and their corresponding glasses. CO2 sorption at 195 K enables quantitative assessment of microporosity in both crystalline and glassy states, allowing the accessible micropore volume of the ZIF glasses to be determined. Moreover, establishing a direct relationship between photonic properties and structural porosity in Zn-based ZIF glasses remains challenging. Here we demonstrate striking broadband blue-light emission from ZIF-4 glass annealed under optimized conditions. A pronounced red shift is observed when increasing the annealing temperature above the glass transition temperature. By correlating the evolution of photoluminescence with structural porosity, we reveal the interplay between the photonic and gas sorption properties of ZIF glasses. These findings provide new insights into the structure-property relationships of ZIF glasses and offer a pathway toward the rational design of multifunctional MOF glasses.

cond-mat.mtrl-sci↗

arXiv2026

Uncovering the deformation mechanism of glasses during indentation through high-resolution X-ray scattering

Indentation experiments can be used to mimic real-life damage events of glasses that lead to surface flaws and thus lower practical strength. Conventional indentation studies often focus on the surface deformation after unloading. However, to understand the link between the surface deformation and structure, it is crucial to characterize the sub-surface deformation during the indentation process. The indentation-induced deformation, consisting of both elastic and plastic zones, is governed by the glass composition and structure, indentation and atmospheric conditions, and stress state. However, only a few experimental methods exist for characterizing the sub-surface indentation deformation mechanism during indentation. In this study, we use synchrotron X-ray nanoscattering to probe the deformation mechanism in situ during indentation of four types of oxide and oxynitride glasses with distinct structural features. This is done by measuring the variation in the position and intensity of the first sharp diffraction peak of the X-ray structure factor with a high spatial resolution down to ~100 nm. We find that the deformation zones of these glasses, which are characterized by the shape, size, and relative contribution between densification and shear flow under different indentation loads, vary with Poisson's ratio. Thus, our work provides new insights into the mechanical behavior of oxide glasses, contributing to the design of more damage-resistant glasses.

cond-mat.mtrl-sci↗

arXiv2026

Local Structure Dictates Ionic Transport and Mechanical Properties in Glassy Solid Electrolytes for Lithium Batteries

Electrolytes composed of sulfide and halide glasses are promising candidates for all-solid-state lithium batteries owing to their processability, lack of grain boundaries, and relatively high ionic conductivity. Nevertheless, their ionic conductivity and mechanical properties are still not satisfying for the real-world applications. Significant advances in solid electrolytes require a thorough understanding of their microstructures. Here, we reveal the connections among structure, ionic transport properties, and mechanical stability in a series of glassy solid electrolytes by employing molecular dynamics simulations based on a machine learning interatomic potential. Specifically, we explore how the interplay between B-S and P-S networks in glassy Li-S-P-B-I (LSPBI) governs ionic conductivity and deformation behavior. The introduction of P2S5 into a B2S3-based glass induces a critical structural transformation, through which both ionic conductivity and mechanical nano-ductility can be enhanced. For a moderate P2S5 content, incorporated PS4 units depolymerize the rigid boron framework, creating percolative diffusion pathways for fast ionic transport. Concurrently, the flexible P-S-P configurations enable energy dissipation through bond bending, leading to the brittle-to-ductile transition. However, excessive P2S5 increases the fraction of polyphosphates (e.g., P2S6 and P2S7), thereby polymerizing the structural network and ultimately impeding Li+ mobility. Our work thus provides atomistic principles for engineering glass electrolytes with balanced ionic conductivity and mechanical robustness.

cond-mat.mtrl-sci↗

arXiv2026

Fast Isotropic Li-Ion Diffusion in Zeolitic Imidazolate Framework Glass Electrolytes for Batteries

All-solid-state lithium batteries require solid electrolytes that combine rapid room-temperature ion transport with mechanical robustness and interfacial compatibility. Zeolitic imidazolate framework (ZIF) glasses, with ZIFs being a sub-set of metal-organic frameworks, offer an attractive yet relatively underexplored platform because they combine an grainboundary-free and amorphous topology with chemically tunable frameworks. Here, we reveal that structural disorder unlocks fast and isotropic lithium diffusion in ZIF glasses. This is realized by using a machine learning interatomic potential to simulate Li+ transport in crystalline and glassy ZIF-4 and ZIF-62. Structural disorder reduces the activation energy for Li+ migration from ~0.35 eV to 0.16 eV and increases the extrapolated room-temperature diffusion coefficient by more than one order of magnitude for ZIF-4 and nearly sevenfold for ZIF-62. Analyses of non-Gaussian dynamics and van Hove correlation functions reveal that Li+ diffusion in crystalline ZIFs occurs via rare, dynamically heterogeneous hopping events among well-defined cages, whereas Li+ diffusion in glassy ZIFs is more homogeneous, continuous, and Fickian-like, benefiting from a wide distribution of coordination geometries and migration barriers. Li+ diffusion in crystalline ZIFs is strongly anisotropic, reflecting that ordered orientations of imidazolate and benzimidazolate rings impose distinct energy barriers along different crystallographic directions. Upon vitrification, these ring orientations become randomized, and hence, the diffusion of Li+ becomes isotropic or near-isotropic. These findings imply that well-designed metal-organic framework glasses are a promising candidate as high-performance solid-state electrolytes.

cond-mat.mtrl-sci↗

arXiv2026

Inverse Design of Amorphous Materials with Targeted Properties

Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range orderings, which depend on composition as well as thermal and pressure history, offer a vast materials design space. To this end, relying on machine learning methods instead of trial and error is promising, and among these, inverse design has emerged as a tool for generating materials with desired properties. Although inverse design methods based on diffusion models have shown success for crystalline materials and molecules, similar methods targeting amorphous materials remain less developed, mainly because of the limited availability of large-scale datasets and the requirement for larger simulation cells. In this work, we propose and validate an inverse design method for amorphous materials, introducing AMDEN (Amorphous Material DEnoising Network), a diffusion model-based framework that generates structures of amorphous materials. These low-energy configurations are typically obtained through a thermal motion-driven random search-like process that cannot be replicated by standard denoising procedures. We therefore introduce an energy-based AMDEN variant that implements Hamiltonian Monte Carlo refinement for generating these relaxed structures. We further introduce several amorphous material datasets with diverse properties and compositions to evaluate our framework and support future development.

cond-mat.mtrl-sci↗

arXiv2026

AMGenC: Generating Charge Balanced Amorphous Materials

Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanced materials. Unlike crystalline materials that can be described by unit cells containing a few to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds to thousands of atoms. To advance the design of amorphous materials with desired properties and facilitate the exploration of their vast design space, generative inverse design has emerged as a promising approach. It aims to directly output materials with properties closely aligned with the desired ones using probabilistic generative models conditioned on desired properties, which can be more resource efficient than the traditional trial-and-error approach. However, due to the inherent stochasticity of probabilistic generative models, when element assignments are unconstrained, a large portion of generated materials may be charge unbalanced, and no existing methods can effectively mitigate this limitation. In this work, we propose AMGenC, a new generative inverse design method for amorphous materials that can guarantee the generation of charge balanced samples, with minimal additional computational overhead and without sacrificing inverse design accuracy. AMGenC achieves this through an element noise that gives the generation process a starting point centered around charge balance, and the combination of a per-step soft projection and a final discrete projection for steering the elements toward exact charge balance throughout the generation. We perform extensive experiments on two amorphous materials datasets. Experimental results provide evidence that AMGenC achieves its design goal.

cs.LG↗

arXiv2026

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials

Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds or often thousands of atoms. Inverse design of amorphous materials with probabilistic generative models aims to generate the atomic positions and elements of amorphous materials given a set of desired properties. It has emerged as a promising approach for facilitating the application of amorphous materials in domains such as energy storage and thermal management. In this paper, we introduce AMShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials. AMShortcut enables accurate inference of diverse short- and medium-range structures in amorphous materials with only a few sampling steps, mitigating the need for an excessive number of sampling steps that hinders inference efficiency. AMShortcut can be trained once with all relevant properties and perform inference conditioned on arbitrary combinations of desired properties, mitigating the need for training one model for each combination. Experiments on three amorphous materials datasets with diverse structures and properties demonstrate that AMShortcut achieves its design goals.

cs.LG↗

arXiv2026

Connecting bond switching to fracture toughness of calcium aluminosilicate glasses

Fracture toughness is a critical mechanical property of glasses, but a detailed understanding of its link to composition and structure is still missing. Here, focusing on the industrially important family of calcium aluminosilicate glasses, we measure the fracture toughness of two glass series using the single-edge precracked beam method, one based on tectosilicate compositions with varying silica contents and the other covering both percalcic and peraluminous compositions with varying Al/Ca ratio. To elucidate the structural origins of the variation in fracture toughness, we perform X-ray total scattering measurements and molecular dynamics simulations. Our findings show that local coordination changes of especially Al atoms, so-called bond switching, feature an overall positive correlation with fracture toughness. We also compare this variation with that in other mechanical properties, including elastic moduli, hardness, and crack initiation resistance. We find that various structural aspects need to be considered to describe and understand the mechanical properties of calcium aluminosilicate glasses.

cond-mat.mtrl-sci↗

arXiv2025

In situ mapping of indentation-induced densification and cracking in vitreous silica by nanofocus X-ray scattering

The practical strength of oxide glasses is greatly reduced by surface flaws that form during processing and use. Instrumented indentation can mimic such real-life damage events and induce flaws and cracking under controlled conditions. At the same time, instrumented indentation allows for systematic examination of the deformation and structural changes of the regions of the glass being indented. However, structural probing is nearly always performed after rather than during the sharp contact event, limiting our understanding of the indentation process. To overcome this, we here demonstrate the use of nanofocus X-ray scattering experiments to probe the local mechanical and structural response of vitreous silica during indentation. Two-dimensional mapping of the scattering pattern in the zone below a sharp diamond wedge indenter reveals local changes in the atomic structure and density as well as cracking behavior. These in situ experiments during indentation reveal the formation and evolution of the densification zone and cracking with nanoscale resolution. Understanding the interplay between structural densification and cracking behavior in glasses is deepened through this work, which is crucial for the development of more damage-resistant and thus stronger glasses as well as fundamental understanding of glass deformation mechanisms.

cond-mat.mtrl-sci↗

arXiv2025

Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters

The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors.

cond-mat.dis-nn↗

arXiv2025

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties

The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavily on ab initio calculations, frequently struggles to reproduce the experimentally observed properties that govern real material behavior. To address this challenge, we present a machine learning-driven, active-learning optimization framework for optimizing classical interatomic potentials to reproduce experimental properties. Our method, here showcased on soda-lime borosilicate glasses, targets both global (density) and local (boron coordination) structural features across a wide range of compositions. By combining a surrogate model with iterative active learning, the framework efficiently explores a five-dimensional parameter space using only 400 molecular dynamics simulations over 17 iterations, making it highly data-efficient and eliminating the need for extensive simulation campaigns. Two transferable parameter sets are identified, each demonstrating good agreement with experimental measurements, including glass density, fraction of four-fold boron, and X-ray structure factor. The framework effectively captures and manages inherent trade-offs between structural objectives and compositional regimes, providing insights into the coordination behavior of boron in complex glass networks. The resulting classical force fields are generalizable and do not require reparameterization for individual compositions. Altogether, this work offers a scalable and experimentally grounded approach for developing transferable interatomic potentials suitable for a broad range of materials, including multi-component glass systems, and beyond.

cond-mat.dis-nn↗

arXiv2025

Zeolitic imidazolate framework glasses emit white light

Zeolitic imidazolate framework (ZIF) glasses represent a newly emerged class of melt-quenched glasses, characterized by their intrinsic nanoporous structure, good processability, and multifunctionalities such as gas separation and energy storage. However, creating photonic functionalities in Zn-based ZIF glasses remains elusive. Here we show a remarkable broadband white light-emitting behavior in a Zn-based ZIF glass, which can be enhanced by annealing. Furthermore, we discovered a sharp red shift upon increasing annealing temperature above the critical temperature of 1.07Tg, where Tg is the glass transition temperature, for a short duration of 30 min. Finally, we achieved a high absolute internal photoluminescence quantum yield of 12.2% upon annealing of ZIF glass at 1.13Tg. Based on the optimally annealed ZIF glass, we fabricated a white light-emitting diode (LED) with the luminous efficacy of 4.2 lm/W and high operational stability, retaining 74.1% of its initial luminous efficacy after 180 min of continuous operation. These results not only demonstrate the feasibility of utilizing ZIF glasses in LED applications but also mark a significant advancement in the development of durable, efficient, and multifunctional photonic materials.

physics.optics↗

arXiv2025

Autonomous Microscopy Experiments through Large Language Model Agents

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents. Further, we develop AFMBench a comprehensive evaluation suite challenging AI agents across the complete scientific workflow from experimental design to results analysis. We find that state of the art models struggle with basic tasks and coordination scenarios. Notably, Claude 3.5 sonnet performs unexpectedly poorly despite excelling in materials domain question answering (QA) benchmarks, revealing that domain specific QA proficiency does not necessarily translate to effective agentic capabilities. Additionally, we observe that LLMs can deviate from instructions, raising safety alignment concerns for SDL applications. Our ablations reveal that multi agent frameworks outperform single-agent architectures. We also observe significant prompt fragility, where slight modifications in prompt structure cause substantial performance variations in capable models like GPT 4o. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. Our findings underscore the necessity for rigorous benchmarking protocols and prompt engineering strategies before deploying AI laboratory assistants in scientific research environments.

cs.CY↗

arXiv2024

Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes

Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophosphates are among the most promising solid electrolytes, as they exhibit superionic conductivity at room temperature. However, the lack of comprehensive understanding regarding their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations of all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. The results show that disorder-driven diffusion dynamics significantly enhances the room-temperature conductivity. We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed "softness". This metric allows the classification of the disorder-induced "soft" hopping lithium ions. Our findings offer insights into ion conduction mechanisms in complex disordered structures, thereby contributing to the development of superior solid-state electrolytes for LIBs.

cond-mat.mtrl-sci↗

arXiv2023

Glass Hardness: Predicting Composition and Load Effects via Symbolic Reasoning-Informed Machine Learning

Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predict quantitatively. Here, using a curated dataset of over approx. 3000 inorganic glasses from the literature comprising the composition, indentation load, and hardness, we develop machine learning (ML) models to predict the composition and load dependence of Vickers hardness. Interestingly, when tested on new glass compositions unseen during the training, the standard data-driven ML model failed to capture the ISE. To address this gap, we combined an empirical expression (Bernhardt law) to describe the ISE with ML to develop a framework that incorporates the symbolic law representing the domain reasoning in ML, namely Symbolic Reasoning-Informed ML Procedure (SRIMP). We show that the resulting SRIMP outperforms the data-driven ML model in predicting the ISE. Finally, we interpret the SRIMP model to understand the contribution of the glass network formers and modifiers toward composition and load-dependent (ISE) and load-independent hardness. The deconvolution of the hardness into load-dependent and load-independent terms paves the way toward a holistic understanding of composition and ISE in glasses, enabling the accelerated discovery of new glass compositions with targeted hardness.

cond-mat.mtrl-sci↗

arXiv2020

On the Allowable or Forbidden Nature of Vapor-Deposited Glasses

Vapor deposition can yield glasses that are more stable than those obtained by the traditional melt-quenching route. However, it remains unclear whether vapor-deposited glasses are "allowable" or "forbidden," that is, if they are equivalent to glasses formed by cooling extremely slowly a liquid or if they differ in nature from melt-quenched glasses. Here, based on reactive molecular dynamics simulation (MD) of silica glasses, we demonstrate that the allowable or forbidden nature of vapor-deposited glasses depends on the temperature of the substrate and, in turn, is found to be encoded in their medium-range order structure.

cond-mat.mtrl-sci↗

arXiv2019

Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement

Although experiments can offer some fingerprints of the atomic structure of glasses (coordination numbers, pair distribution function, etc.), atomistic simulations are often required to directly access the structure itself (i.e., the positions of the atoms). On the one hand, molecular dynamics (MD) simulations can be used to generate by quenching a liquid - but MD simulations remain plagued by extremely high cooling rates. On the other hand, reverse Monte Carlo (RMC) modeling bypasses the melt-quenching route - but RMC often yields non-unique glass structures. Here, we adopt the force-enhanced atomic refinement (FEAR) method to overcome these limitations and decipher the atomic structure of a sodium silicate glass. We show that FEAR offers an unprecedented description of the atomic structure of sodium silicate. The FEAR-generated glass structure simultaneously exhibits (i) enhanced agreement with experimental neutron diffraction data and (ii) higher energetic stability as compared to those generated by MD or RMC. This result allows us to reveal new insights into the atomic structure of sodium silicate glasses. Specifically, we show that sodium silicate glasses exhibit a more ordered medium-range order structure than previously suggested by MD simulations. These results pave the way toward an increased ability to accurately describe the atomic structure of glasses.

cond-mat.mtrl-sci↗

arXiv2019

Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.

cond-mat.mtrl-sci↗