arXiv subjects

Jonas Björk

Publications and source records attributed to Jonas Björk.

8 recordsLinked to original sources

arXiv2026

General and scalable vapor etching and transformation platform for two-dimensional materials

Two-dimensional (2D) nanomaterials derived from non-van der Waals (non-vdW) solids offer exceptional physicochemical properties, yet their synthesis is impeded by intrinsic covalent/metallic bonding and high surface reactivity of the precursors. Here, we report a general vapor-phase etching and transformation platform for producing a library of 36 2D carbides, nitrides, and carbonitrides, exhibiting electrical conductivities spanning six orders of magnitude. Using reactive vapors like hydrogen chloride, we selectively remove A-layers from MAX phases to yield well-defined layers (MXenes), including previously inaccessible semiconducting Hf2CTx. By varying the reactive vapor environment, MXenes can be engineered at X-site and surface-termination site and even be transformed into non-vdW layers such as 2D MAX phases. This general and scalable vapor-phase platform reframes 2D material synthesis, opening new avenues for various applications.

cond-mat.mtrl-sci↗

arXiv2026

Synthesis of Ti2B2Clx MBenes in molten salts from theoretical and experimental perspectives

The unique properties and application possibilities of two-dimensional (2D) materials motivates the exploration of different nanolaminated compounds. Here, by using a molten salt approach, we selectively etch Ti2InB2 with ZnCl2 to produce a multilayer (ml) Ti2B2Clx MBene. Scanning transmission electron microscopy, in combination with energy dispersive X-ray, and electron energy loss spectroscopies show that In atoms are completely removed from the precursor upon etching, being replaced by chlorine surface terminations with a coverage 1.1 < x < 1.4. Further, in situ X-ray diffraction indicates a direct biphasic transformation from Ti2InB2 to ml-MBene, with no signs of intermediate phase formation. A computational framework based on density functional theory further corroborates these experimental observations by showing a negative reaction free energy for the formation of ml-MBene, favourable over all competing processes. In addition, A-element substitution into to the 3D Ti2ZnB2 phase is predicted to be endergonic, consistent with the absence of experimental evidence for its formation. Initial Li-ion battery performance evaluation showed a stable discharge capacity similar or better than MAX phases and other borides. Altogether, the theoretical framework combined with materials synthesis and characterization provides a general approach for 2D materials development, for further expansion of the family of 2D materials.

cond-mat.mtrl-sci↗

arXiv2026

Benchmark Dataset for Catalysis on 2D MXenes

Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) Ti$_2$CT$_y$ MXenes, whose versatile surface chemistry makes them particularly compelling candidates for catalysis. Resolving their composition and structure under realistic conditions exceeds the reach of standard density functional theory (DFT) due to computational cost. To address this challenge, we generate a comprehensive dataset of 50,000 DFT calculations for training and 10,000 for testing, encompassing both Ti$_2$CT$_y$ MXene configurations and molecular systems, along with an additional test dataset with 1000 genuinely new, larger systems to investigate how well models generalise. We train and validate widely used and competitive machine learning interatomic potential (MLIP) models, including EquiformerV2, MACE, MatRIS, and UPET, that accurately predict atomic forces and formation energies -- quantities that DFT must repeatedly compute for structural and catalytic investigations -- for these 2D materials. This combined DFT-ML framework achieves computational acceleration on the order of approximately $1-4 \cdot 10^3$ (on a CPU) while maintaining desired-level accuracy (approximately +/- $10$ meV/A for forces and approximately +/- $1$ meV for per-atom energies), paving the way for more efficient investigations of MXene catalytic behaviour. Moreover, we perform an extensive qualitative evaluation of the trained models, showcasing the importance of comprehensive simulation-based comparison beyond benchmark metrics. The dataset and the trained models with the code are available at https://huggingface.co/datasets/CatalystAnonymous/catalyst_mxenes.

cond-mat.mtrl-sci↗

arXiv2026

Roadmap on Advancements of the FHI-aims Software Package

Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned.

cond-mat.mtrl-sci↗

arXiv2026

Exploring the Structure and Chemistry of 1D and 2D Lepidocrocite TiO2 at Atomic Resolution

Low dimensional materials are critical for enabling next generation applications that are central to addressing critical global challenges. Titanium dioxide nanostructures stand out due to their structural versatility and relevance to catalysis, energy conversion, and environmental remediation. Here, we employ a combination of advanced electron microscopy, spectroscopy, and first principles theoretical calculations to investigate the structural and chemical properties of one and two dimensional lepidocrocite type titania. Special emphasis is placed on the one dimensional material, which exhibits anisotropic growth, extending exclusively along a single crystallographic direction. Our analysis suggests that this unusual growth behavior can be attributed to light element impurities, such as carbon, that are incorporated during the bottom up synthesis. The results extend the understanding for these unexplored low dimensional titania materials and offer fundamental insights into their structure and chemistry.

cond-mat.mtrl-sci↗

arXiv2024

A Masked language model for multi-source EHR trajectories contextual representation learning

Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes).

cs.LG↗

arXiv2015

Unfolding spinor wavefunctions and expectation values of general operators: Introducing the unfolding-density operator

We show that the spectral weights $W_{m\vec K}(\vec k)$ used for the unfolding of two-component spinor eigenstates $| {ψ_{m\vec K}^\mathrm{SC}} > = | α> | {ψ_{m\vec{K}}^\mathrm{SC, α}} > + | β> | {ψ_{m\vec{K}}^\mathrm{SC, β}} >$ can be decomposed as the sum of the partial spectral weights $W_{m\vec{K}}^μ(\vec k)$ calculated for each component $μ= α, β$ independently, effortlessly turning a possibly complicated problem involving two coupled quantities into two independent problems of easy solution. Furthermore, we define the unfolding-density operator $\hatρ_{\vec{K}}(\vec{k}_{i}; \, \varepsilon)$, which unfolds the primitive cell expectation values $φ^{pc}(\vec{k}; \varepsilon)$ of any arbitrary operator $\mathbf{\hatφ}$ according to $φ^{pc}(\vec{k}_{i}; \varepsilon) = \mathit{Tr}(\hatρ_{\vec{K}}(\vec{k}_{i}; \, \varepsilon)\,\,\hatφ)$. As a proof of concept, we apply the method to obtain the unfolded band structures, as well as the expectation values of the Pauli spin matrices, for prototypical physical systems described by two-component spinor eigenfunctions.

cond-mat.mtrl-sci↗

arXiv2014

Visualization and thermodynamic encoding of single-molecule partition functions

Ensemble averaging of molecular states is fundamental for the experimental determination of thermodynamic quantities. A special case occurs for single-molecule investigations under equilibrium conditions, for which free energy, entropy and enthalpy at finite-temperatures are challenging to determine with ensemble-averaging alone. Here, we provide a method to access single-molecule thermodynamics, by confining an individual molecule to a nanoscopic pore of a two-dimensional metal-organic nanomesh, where we directly record finite-temperature time-averaged statistical weights using temperature-controlled scanning tunneling microscopy. The obtained patterns represent a real space equilibrium probability distribution. We associate this distribution with a partition function projection to assess spatially resolved thermodynamic quantities, by means of computational modeling. The presented molecular dynamics based Boltzmann weighting model is able to reproduce experimentally observed molecular states with high accuracy. By an in-silico customized energy landscape we demonstrate that distinct probability distributions can be encrypted at different temperatures. Such modulation provides means to encode and decode information into position-temperature space or to realize nanoscopic thermal probes.

cond-mat.mtrl-sci↗