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Gianaurelio Cuniberti

Publications and source records attributed to Gianaurelio Cuniberti.

At least 19 recordsLinked to original sources

Toward the Rational Design of Molecular Field-Coupled Nanocomputing Candidates

Molecular Field-Coupled Nanocomputing (MolFCN) is a promising beyond-CMOS paradigm in which information is propagated electrostatically rather than through charge transport, enabling ultra-low-power logic. Identifying molecules with stable logic states, efficient clock-field switching, and reliable information propagation, however, remains an open challenge. In this Letter, we introduce LUFFY (Layered Unified Framework for MolFCN systematic analYsis), a framework for the rational design and validation of molecular candidates for MolFCN architectures. Starting from 27 synthetically accessible molecules, we combine conformational sampling and electrostatic analysis in neutral and oxidized states to derive robust descriptors of molecular response. In particular, we extract the V${in}$-to-Aggregated-Charge Transcharacteristics (VACTs), capturing the field-induced charge response, and introduce energy-averaged models validated via ab initio molecular dynamics to account for conformational diversity. Finally, we use the resulting molecular responses to evaluate device-level propagation and demonstrate stable information transfer. These results directly link molecular structure to functional information flow, identifying conformationally robust electrostatic response as a key requirement for MolFCN operation. Overall, this work establishes a unified and transferable framework for the identification and validation of MolFCN molecular candidates, bridging molecular design and circuit-level functionality. By unifying previously fragmented approaches into a sustainable methodology, LUFFY enables rational and scalable molecular discovery and establishes a foundation for data-driven design strategies that accelerate the development of ultra-low-power information processing technologies.

cond-mat.mtrl-sci↗

Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks

Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large surface areas. However, like most materials, COFs inevitably contain synthesis-induced defects, which-similar to graphene-can strongly influence intrinsic properties, such as thermal transport and mechanical strength. To address this challenge, we have assessed the performance of a set of machine learning interatomic potentials (MLIP) capable of efficient large-scale simulations of COFs with quantum accuracy. In doing so, QCOF models (Quantum COF) were developed by tuning the state-of-the-art MACE architecture on an extensive dataset of non-equilibrium COF conformations generated from high-fidelity density functional theory calculations. The accuracy, computational efficiency, memory footprint, and transferability to unseen chemical environments of these models were benchmarked against general-purpose MACE models and their fine-tuned variants. Our results show that an invariant QCOF model with a small descriptor dimensionality and cutoff outperforms all other models in most validation tasks, including scalability to large systems, force prediction in defective COFs, and phonon dispersion calculations. The best-performing QCOF model was then used to run large-scale simulations of thermal conductivity for defective CTF-1 and COF-LZU1 systems via non-equilibrium MD, revealing a more pronounced sensitivity of CTF-1 to structural defects. Stress-strain curves were also investigated, showing that the mechanical response remains nearly invariant at low defect densities, while asymmetric behaviour emerges at large strains. This work thus provides a foundation for the design of robust quantum-informed MLIP for large-scale property simulations of defective of extended network materials.

cond-mat.mtrl-sci↗

Perspective: Towards sustainable exploration of chemical spaces with machine learning

Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.

cs.LG↗

Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials

Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challenge, we developed MORE-ML, a computational framework that integrates quantum-mechanical (QM) property data of e-nose molecular building blocks with machine learning (ML) methods to predict sensing-relevant properties. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between BOV molecules and mucin-derived receptors. This dataset provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of MORE-QX property space revealed weak correlations between QM properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Explainable AI methods further highlighted which QM properties most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for molecular receptors in BOV sensing. This approach establishes a foundation for advancing artificial sensing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.

physics.chem-ph↗

Vanadium-doped HfO$_2$, multiferroic uncompromised

Ab initio density-functional calculations show that orthorhombic Pca21 hafnia HfO2 mixed with vanadium at low concentration is a ferroelectric and ferromagnetic insulator. The multiorbital degeneracy of singly-occupied V states in the nominally 4+ ionic state is broken by magnetism, reduced symmetry, and local distortion, causing a single one-electron majority state per V atom to be occupied. A gap of order 1 eV thus survives at all V concentrations, and intrinsic polarization is preserved, at the level of two-thirds the hafnia value. Ferromagnetic magnetization is found to increase linearly with V content, with values of 30-40 emu/cm3 at concentrations near the end of the stability range.

cond-mat.mtrl-sci↗

Chirality-induced Spin-Orbit Coupling and Spin Selectivity

We show that a spinor traveling along a one-dimensional helical path develops a spin-orbit coupling as a result of the curvature of the path. We estimate the magnitude of the associated spin polarization and obtain values typical of many helical molecular structures that showcase the Chirality-induced Spin Selectivity (CISS) effect. We find that this chirality-induced spin-orbit coupling ($χ$-SOC), in conjunction with broken time-reversal symmetry, may be an important ingredient for the microscopic underpinning of the CISS phenomenon.

cond-mat.mes-hall↗

On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions

The development of resilient and lightweight Aluminum alloys is central to advancing structural materials for energy-efficient engineering applications. To address this challenge, in this study, we explore the elastic properties of Al-Mg-Zr solid solutions by integrating advanced machine learning (ML) techniques with quantum-mechanical (QM) atomistic simulations. For this purpose, we develop accurate and transferable machine-learned interatomic potentials (MLIPs) using two complementary approaches: (i) an on-the-fly learning scheme combined with Bayesian linear regression during ab initio molecular dynamics simulations, and (ii) the equivariant neural network architecture MACE. Both MLIPs facilitate the prediction of composition-dependent elastic properties while drastically reducing the computational cost compared to conventional QM methods. Comparison with ultrasonic measurements shows that the deviation between simulation and experiment remains within a few GPa across all Al-Mg-Zr systems investigated. These potentials also enable the systematic exploration of the Al-Mg-Zr solid solution phase space and provide insights into the elastic behavior as a function of alloying element concentration. Hence, our findings demonstrate the reliability and transferability of the parameterized on-the-fly MLIPs, making them valuable for accelerating the design of Al alloys with tailored physicomechanical properties in complex compositional spaces. While the present study focuses on homogeneous phases, it establishes a foundation for future multiscale simulations that include microstructural features such as precipitates and grain boundaries.

cond-mat.mtrl-sci↗

What do Large Language Models know about materials?

Large Language Models (LLMs) are increasingly applied in the fields of mechanical engineering and materials science. As models that establish connections through the interface of language, LLMs can be applied for step-wise reasoning through the Processing-Structure-Property-Performance chain of material science and engineering. Current LLMs are built for adequately representing a dataset, which is the most part of the accessible internet. However, the internet mostly contains non-scientific content. If LLMs should be applied for engineering purposes, it is valuable to investigate models for their intrinsic knowledge -- here: the capacity to generate correct information about materials. In the current work, for the example of the Periodic Table of Elements, we highlight the role of vocabulary and tokenization for the uniqueness of material fingerprints, and the LLMs' capabilities of generating factually correct output of different state-of-the-art open models. This leads to a material knowledge benchmark for an informed choice, for which steps in the PSPP chain LLMs are applicable, and where specialized models are required.

physics.app-ph↗

Charge Carrier Mobilities in gamma-Graphynes: A computational approach

Graphynes, a class of two-dimensional carbon allotropes, exhibit exceptional electronic properties, similar to graphene, but with intrinsic band gaps, making them promising for semiconducting applications. The incorporation of acetylene linkages allows for systematic modulation of their properties. However, the theoretical characterization of graphynes remains computationally demanding, particularly for electron-phonon coupling (EPC) analyses. Here, we employ the density functional tight binding method within the DFTBephy framework, providing an efficient and accurate approach for computing EPC and transport properties. We investigate the structural, mechanical, electronic, and transport properties of graphynes, comparing transport calculations using the constant relaxation-time approximation and the self-energy relaxation-time approximation (SERTA) alongside analytical models based on parabolic- and Kane-band approximations. For graphyne, the SERTA relaxation time is 0.63 (1.69) ps for holes (electrons). In graphdiyne, the relaxation time is 0.04 (0.14) ps for holes (electrons). While the hole mobilities in graphyne are on the order of 10$^3$ cm$^2/$Vs, the electron mobilities reach up to 10$^4$ cm$^2/$Vs. In graphdiyne, the mobility values for both types of charge carriers are on the order of 10$^2$ cm$^2/$Vs. The phonon-limited mobilities at room temperature in graphyne fall between those of graphene and MoS$_2$, while in graphdiyne, they are comparable to those of MoS$_2$.

cond-mat.mtrl-sci↗

Are nonequilibrium effects relevant for chiral molecule discrimination?

Sensing and discriminating between enantiomers of chiral molecules remains a significant challenge in the design of sensor platforms. In the case of chemoresistive sensors, where detection relies on changes in electrical response upon analyte adsorption, the sensor substrate is typically functionalized with chirality sensitive molecular receptors. In this computational study, we investigate whether a chirality-blind substrate, such as a graphene nanoribbon, is still capable of discriminating between enantiomers. To this end, we employ a density-functional parametrized tight-binding method combined with nonequilibrium Green functions. For a small set of chiral amino acids, we demonstrate that accounting for the nonequilibrium response of the device leads to significant differences in the electrical currents of enantiomeric pairs of the order tens of nanoamperes. This effect is further amplified when structural fluctuations of the device's active region are considered (1-2 microAmp). Moreover, we propose new quantum-mechanical quantities for enantioselective discrimination in molecular sensors, with an emphasis on binding features and property-property correlations. Therefore, our work demonstrates the significance of nonequilibrium effects in chiral discrimination, laying the foundation for future investigations addressing the design of chiral molecular sensors.

physics.chem-ph↗

Tuning the Planarity of an Aromatic Thianthrene-Based Molecule on Au(111)

Non-planar aromatic molecules are interesting systems for organic electronics and optoelectronics applications due to their high stability and electronic properties. By using scanning tunneling microscopy and spectroscopy, we investigated thianthrene-based molecules adsorbed on Au(111), which are non-planar in the gas phase and the bulk solid state. Varying the molecular coverage leads to the formation of two different kinds of self-assembled structures: close-packed islands and quasi one-dimensional chains. We found that the molecules are non-planar within the close-packed islands, while the configuration is planar in the molecular chain and for single adsorbed molecules. Using vertical tip manipulation to isolate a molecule from the island, we demonstrate the conversion of a non-planar molecule to its planar configuration. We discuss the two different geometries and their electronic properties with the support of density functional theory calculations.

cond-mat.mtrl-sci↗

Si1-x-yGeySnx alloy formation by Sn ion implantation and flash lamp annealing

For many years, Si1-yGey alloys have been applied in the semiconductor industry due to the ability to adjust the performance of Si-based nanoelectronic devices. Following this alloying approach of group-IV semiconductors, adding tin (Sn) into the alloy appears as the obvious next step, which leads to additional possibilities for tailoring the material properties. Adding Sn enables effective band gap and strain engineering and can improve the carrier mobilities, which makes Si1-x-yGeySnx alloys promising candidates for future opto- and nanoelectronics applications. The bottom-up approach for epitaxial growth of Si1-x-yGeySnx, e.g., by chemical vapor deposition and molecular beam epitaxy, allows tuning the material properties in the growth direction only; the realization of local material modifications to generate lateral heterostructures with such a bottom-up approach is extremely elaborate, since it would require the use of lithography, etching, and either selective epitaxy or epitaxy and chemical-mechanical polishing giving rise to interface issues, non-planar substrates, etc. This article shows the possibility of fabricating Si1-x-yGeySnx alloys by Sn ion beam implantation into Si1-yGey layers followed by millisecond-range flash lamp annealing (FLA). The materials are investigated by Rutherford backscattering spectrometry, micro Raman spectroscopy, X-ray diffraction, and transmission electron microscopy. The fabrication approach was adapted to ultra-thin Si1-yGey layers on silicon-on-insulator substrates. The results show the fabrication of single-crystalline Si1-x-yGeySnx with up to 2.3 at.% incorporated Sn without any indication of Sn segregation after recrystallization via FLA. Finally, we exhibit the possibility of implanting Sn locally in ultra-thin Si1-yGey films by masking unstructured regions on the chip.

cond-mat.mtrl-sci↗

Structural changes in Ge1-xSnx and Si1-x-yGeySnx thin films on SOI substrates treated by pulse laser annealing

Ge1-xSnx and Si1-x-yGeySnx alloys are promising materials for future opto- and nanoelectronics applications. These alloys enable effective band-gap engineering, broad adjustability of their lattice parameter, exhibit much higher carrier mobility than pure Si, and are compatible with the CMOS technology. Unfortunately, the equilibrium solid solubility of Sn in Si1-xGex is less than 1% and the pseudomorphic growth of Si1-x-yGeySnx on Ge or Si can cause in-plane compressive strain in the grown layer, degrading the superior properties of these alloys. Therefore, post-growth strain engineering by ultrafast non-equilibrium thermal treatments like pulse laser annealing (PLA) is needed to improve the layer quality. In this article, Ge0.94Sn0.06 and Si0.14Ge0.8Sn0.06 thin films grown on silicon-on-insulator substrates by molecular beam epitaxy were post growth thermally treated by PLA. The material is analyzed before and after the thermal treatments by transmission electron microscopy, X-ray diffraction (XRD), Rutherford backscattering spectrometry, secondary ion mass spectrometry, and Hall effect measurements. It is shown that after annealing, the material is single-crystalline with improved crystallinity than the as-grown layer. This is reflected in a significantly increased XRD reflection intensity, well-ordered atomic pillars, and increased active carrier concentrations up to 4x1019 cm-3.

cond-mat.mtrl-sci↗

Network-theory based modeling of avalanche dynamics in percolative tunnelling networks

Brain-like self-assembled networks can infer and analyze information out of unorganized noisy signals with minimal power consumption. These networks are characterized by spatiotemporal avalanches and their crackling behavior, and their physical models are expected to predict and understand their computational capabilities. Here, we use a network theory-based approach to provide a physical model for percolative tunnelling networks, found in Ag-hBN system, consisting of nodes (atomic clusters) of Ag intercalated in the hBN van der Waals layers. By modeling a single edge plasticity through constitutive electrochemical filament formation, and annihilation through Joule heating, we identify independent parameters that determine the network connectivity. We construct a phase diagram and show that a small region of the parameter space contains signals which are long-range temporally correlated, and only a subset of them contains crackling avalanche dynamics. Physical systems spontaneously selforganize to this region for possibly maximizing the efficiency of information transfer.

cond-mat.dis-nn↗

On-liquid-gallium surface synthesis of ultra-smooth conductive metal-organic framework thin films

Conductive metal-organic frameworks (MOFs) are emerging electroactive materials for (opto-)electronics. However, it remains a great challenge to achieve reliable MOF-based devices via the existing synthesis methods that are compatible with the complementary metal-oxide-semiconductor technology, as the surface roughness of thus-far synthetic MOF films or pellets is rather high for efficient electrode contact. Here, we develop an on-liquid-gallium surface synthesis (OLGSS) strategy under chemical vapor deposition (CVD) conditions for the controlled growth of two-dimensional conjugated MOF (2D c-MOF) thin films with ten-fold improvement of surface flatness (surface roughness can reach as low as ~2 Å) compared with MOF films grown by the traditional methods. Supported by theoretical modeling, we unveil a layer-by-layer CVD growth mode for constructing flattening surfaces, that is triggered by the high adhesion energy between gallium (Ga) and planar aromatic ligands. We further demonstrate the generality of the as-proposed OLGSS strategy by reproducing such a flat surface over nine different 2D c-MOF films with variable thicknesses (~2 to 208 nm) and large lateral sizes (over 1 cm2). The resultant ultra-smooth 2D c-MOF films enable the formation of high-quality electrical contacts with gold (Au) electrodes, leading to a reduction of contact resistance by over ten orders of magnitude compared to the traditional uneven MOF films. Furthermore, due to the efficient interfacial interaction benifited from the high-quality contacts, the prepared van der Waals heterostructure (vdWH) of OLGSS c-MOF and MoS2 exhibits intriguing photoluminescence (PL) enhancement, PL peak shift and large work function modulation. The establishment of the reliable OLGSS method provides the chances to push the development of MOF electronics and the construction of multicomponent MOF-based heterostructure materials.

physics.app-ph↗

Electronic quantum wires in extended quasiparticle picture

A one-dimensional quantum wire of Fermions is considered and ground state properties are calculated in the high density regime within the extended quasiparticle picture and Born approximation. Expanding the two-particle Green functions determines the selfenergy and the polarization as well as the response function on the same footing. While the on-shell selfenergies are strictly zero due to Pauli-blocking of elastic scattering, the off-shell behaviour shows a rich structure of a gap in the damping of excitation which is closed when the momentum approaches the Fermi one. The consistent spectral function is presented completing the first two energy-weighted sum rules. The excitation spectrum shows a splitting due to holons and antiholons as non-Fermi liquid behaviour. A renormalization procedure is proposed by subtracting an energy constant to render the Fock exchange energy finite. The effective mass derived from meanfield shows a dip as onset of Peierls instability. The correlation energy is calculated with the help of the extended quasiparticle picture which accounts for off-shell effects. The corresponding response function leads to the same correlation energy as the selfenergy in agreement with perturbation theory. The reduced density matrix or momentum distribution is calculated with the help of a Padé regularization repairing deficiencies of the perturbation theory. A seemingly finite step at the Fermi energy indicating Fermi-liquid behaviour is repaired in this way.

cond-mat.str-el↗

Structural Reinforcement in Mechanically Interlocked Two-Dimensional Polymers by Suppressing Interlayer Sliding

Preserving the superior mechanical properties of monolayer two-dimensional (2D) materials when transitioning to bilayer and layer-stacked structures poses a great challenge, primarily arising from the weak van der Waals (vdW) forces that facilitate interlayer sliding and decoupling. Here, we discover that mechanically interlocked 2D polymers (2DPs) offer a means for structural reinforcement from monolayer to bilayer. Incorporating macrocyclic molecules with one and two cavities into 2DPs backbones enables the precision synthesis of mechanically interlocked monolayer (MI-M2DP) and bilayer (MI-B2DP). Intriguingly, we have observed an exceptionally high effective Young's modulus of 222.4 GPa for MI-B2DP, surpassing those of MI-M2DP (130.1 GPa), vdW-stacked MI-M2DPs (2 MI-M2DP, 8.1 GPa) and other reported multilayer 2DPs. Modeling studies demonstrate the extraordinary effectiveness of mechanically interlocked structures in minimizing interlayer sliding (~0.1 Å) and energy penalty (320 kcal/mol) in MI-B2DP compared to 2 MI-M2DP (~1.2 Å, 550 kcal/mol), thereby suppressing mechanical relaxation and resulting in prominent structural reinforcement.

physics.chem-ph↗

Electron Spin Polarization as a Predictor of Chiroptical Activity in Helical Molecules

Chiral structures, breaking spatial inversion symmetry, exhibit non-zero chiroptical activity (COA) due to the interaction between their electric and magnetic responses under external electromagnetic fields, an effect that is otherwise absent in achiral systems. Non-magnetic chiral structures also exhibit Chiral Induced Spin Selectivity (CISS), where spin-polarization (SP) emerges without external magnetic influence. We have obtained a COA-SP connection for a model system of an electron constrained to a helix including spin-orbit coupling (SOC), and in the presence of an external electromagnetic field. Despite its simplicity, this model captures the relevant physics required to address the problem. In particular, our results reveal that the norm of the SP vector can be used as a predictor of COA. In addition to SOC and the breaking of space inversion, a non-vanishing SP requires the breaking of time-reversal symmetry (TRS), as demanded by Onsager's reciprocity. Beyond the relationship between SP and COA, we obtain the novel result that TRS breaking is also necessary to yield a non-vanishing contribution of the SOC to the COA.

cond-mat.mes-hall↗