arXiv subjects

Mohammad Amini

Publications and source records attributed to Mohammad Amini.

At least 19 recordsLinked to original sources

arXiv2026

Interaction-driven electronic ferroelectricity in van der Waals heterostructures

Strong electronic correlations in narrow-band systems provide a promising route to realize emergent quantum phases. While ferroelectricity in van der Waals materials is typically associated with inversion symmetry breaking driven by lattice distortions, interlayer sliding, or moir\'e reconstruction, the possibility of generating ferroelectricity directly from electronic interactions remains largely unexplored. Here, using molecular beam epitaxy, scanning tunneling microscopy, and ab initio calculations, we investigate two stacking geometries of bilayer 1T-TaSe$_2$, A-C and A-C$'$, formed by coupled Star-of-David charge density wave phases. We show that both stackings realize quasi-one-dimensional interacting chains, but are governed by distinct interaction mechanisms. In the A-C stacking, strong interlayer hybridization leads to dimerization and the formation of a band insulating state. In contrast, the A-C$'$ stacking is dominated by interlayer Coulomb interactions, producing a spontaneous charge imbalance between layers that gives rise to an out-of-plane ferroelectric polarization. Furthermore, we demonstrate that ferroelectric and antiferroelectric interchain configurations can be stabilized and electrically switched by an external field. Our results prove that bilayer 1T-TaSe$_2$ is a platform for interaction-driven electronic ferroelectricity, establishing an overlooked family of charge-ordered correlated states in 1T-TaSe$_2$ multilayers.

cond-mat.mtrl-sci↗

arXiv2026

Emergent ferromagnetism in the NiI$_2$-NbSe$_2$ van der Waals heterostructure

Multiferroicity arising from non-collinear spin textures and strong spin-orbit interactions offers a route to magnetoelectric functionality in the monolayer limit. Although theory predicts that the properties of monolayer multiferroics can be tuned by strain, gating, or proximity effects, experimental demonstrations of such control remain scarce. Here we show that the magnetic ground state of monolayer NiI$_2$, a prototypical two-dimensional multiferroic, is altered by proximity to a superconducting NbSe$_2$ substrate. Using low-temperature scanning tunnelling microscopy (STM) and spectroscopy (STS), we show that the metallic substrate renormalizes the exchange interactions within NiI$_2$ and drives it into a ferromagnetic ground state. This can be visualized by probing the Yu-Shiba-Rusinov (YSR) states within the superconducting gap of the NbSe$_2$ substrate. Our results establish YSR states as an in situ probe of two-dimensional magnetism and demonstrate substrate engineering as a means of controlling magnetic order in atomically thin materials.

cond-mat.mes-hall↗

arXiv2026

PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark

Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching - none captured by existing benchmarks. We introduce PARSA-Bench (Persian Audio Reasoning and Speech Assessment Benchmark), the first benchmark for evaluating large audio-language models on Persian language and culture, comprising 16 tasks and over 8,000 samples across speech understanding, paralinguistic analysis, and cultural audio understanding. Ten tasks are newly introduced, including poetry meter and style detection, traditional Persian music understanding, and code-switching detection. Text-only baselines consistently outperform audio counterparts, suggesting models may not leverage audio-specific information beyond what transcription alone provides. Culturally-grounded tasks expose a qualitatively distinct failure mode: all models perform near random chance on vazn detection regardless of scale, suggesting prosodic perception remains beyond the reach of current models. The dataset is publicly available at https://huggingface.co/datasets/MohammadJRanjbar/PARSA-Bench

cs.CL↗

arXiv2026

Universality of Type-II Multiferroicity in Monolayer Nickel Dihalides

The recent discovery of type-II multiferroicity in monolayer NiI${_2}$ indicated a new pathway for intrinsic magnetoelectric coupling in the two-dimensional limit. However, determining whether this phenomenon is a unique anomaly or a general, chemically tunable property of the material class remains unresolved. Here, we demonstrate the universality of type-II multiferroicity in the transition metal dihalides by visualizing the ferroelectric order in monolayer NiBr${_2}$. Using scanning tunneling microscopy (STM), we resolve atomic-scale ferroelectric domains and confirm their magnetoelectric origin through reciprocal manipulation experiments: reorienting magnetic order via electric fields and suppressing the electric polarization with external magnetic fields. Furthermore, we find that the multiferroic state in NiBr${_2}$ is energetically less robust than in its iodide counterpart, consistent with modified superexchange interactions and the reduced spin-orbit coupling. Our results establish the transition metal dihalides as a versatile platform where the stability of magnetoelectric phases can be engineered through chemical substitution.

cond-mat.mtrl-sci↗

arXiv2026

Molecular Hamiltonian learning from setpoint-dependent scanning tunneling spectroscopy

Molecular quantum magnets adsorbed on surfaces exhibit rich spin and orbital excitations that can be probed by scanning tunneling microscopy with inelastic electron tunneling spectroscopy (STM-IETS). However, the quantitative extraction of the underlying multiorbital Hamiltonian from experimental spectra remains a fundamental challenge. Here, we introduce molecular Hamiltonian learning, a machine learning strategy that infers the microscopic Hamiltonian parameters of a single adsorbed molecule directly from the setpoint-dependence of STM-IETS data. The method leverages the systematic evolution of spectral features as the STM tip tunes the local electrostatic environment for different tip-sample distances. We demonstrate this approach on iron phthalocyanine on ferroelectric SnTe, training our algorithm on theory spectra from a realistic multiorbital model, including spin-orbit coupling, electrostatic interactions, local crystal field, and substrate effects. The algorithm, trained solely on theoretical many-body simulations, allows reconstructing Hamiltonian parameters directly from experimental spectra. Our manuscript establishes a flexible and automated strategy for Hamiltonian reconstruction from STM-IETS, transforming setpoint-dependent spectroscopy into quantitative characterization of quantum materials at the atomic scale.

cond-mat.mes-hall↗

arXiv2026

Stimulated cooling in non-equilibrium Bose-Einstein condensate

We report on the experimental observation of stimulated cooling in the non-equilibrium Bose-Einstein condensate (BEC) of weakly interacting exciton-polaritons from approximately room temperature down to 20K. By resolving the condensate in energy-momentum space and performing interferometric measurements, we distinguish the condensate from thermalized particles yet occupying excited states macroscopically. In contrast to the analytical quantum theories of non-equilibrium BEC [Shishkov et al., Phys. Rev. Lett. 128, 065301 (2022)], we observe segmentation of the particle density along the excited states into two fractions both following Bose-Einstein distribution, albeit with different effective temperatures and chemical potentials. Our results indicate that the temperature of the weakly interacting Bose gas is universally set by the density-dependent chemical potential, revealing a defining property of non-equilibrium BECs. Finally, we demonstrate that the stimulated nature of the cooling process directly governs the emergence of quantum coherence of the condensate and shapes the dissipative properties of the excited states.

cond-mat.quant-gas↗

arXiv2025

Atomic-scale probe of molecular magneto-electric coupling

Van der Waals heterostructures are a core tool in quantum material design. The recent addition of monolayer ferroelectrics expands the possibilities of designer materials. Ferroelectric domains can be manipulated using electric fields, thus opening a route for external control over material properties. In this paper we explore the possibility of engineering magneto-electric coupling in ferroelectric heterostructures by studying the interface of bilayer SnTe with iron phthalocyanine molecules as a model system. The molecules act as sensor spins, allowing us to sample the magneto-electric coupling with nanometer precision through scanning tunneling microscopy. Our measurements uncover a structural, and therefore material-independent and intrinsic, mechanism to couple electric and magnetic degrees of freedom at the nanoscale.

cond-mat.mes-hall↗

arXiv2025

Moir\'e modulated quantum spin liquid candidate 1T-TaSe$_2$

Quantum spin liquids are quantum phases of matter featuring collectively entangled states and emergent fractional many-body excitations. While methods exist to probe three-dimensional quantum spin liquids experimentally, these techniques lack the sensitivity to probe two-dimensional quantum spin liquids. This seriously hampers the study of potential monolayer quantum spin liquid candidates such as $\alpha$-RuCl$_3$ and 1T-TaSe$_2$. Scanning tunneling microscopy (STM) and spectroscopy (STS) have recently been suggested as promising probes of the quantum spin liquid state, as they can access the spinon spectrum through inelastic tunneling spectroscopy (IETS). In this work, we employ this approach on the quantum spin liquid candidate material 1T-TaSe$_2$ and directly measure its low-energy inelastic excitations. We observe the emergence of a $\sqrt{3}\times\sqrt{3}$ reconstruction driven by the substrate, equivalent spectroscopy across all spin sites and coexistence of zero and finite energy excitations. We show that these observations are consistent with a modulated $\sqrt{3}\times\sqrt{3}$ spin liquid ground state. Our results demonstrate that IETS provides a powerful route to obtain atomic-scale insight into the magnetic excitations of two-dimensional materials, allowing to explore the effects of moir\'e modulations on potential quantum liquid phases.

cond-mat.str-el↗

arXiv2025

Observation of electromagnons in a monolayer multiferroic

Van der Waals multiferroics have emerged as a promising platform to explore novel magnetoelectric phenomena. Recently, it has been shown that monolayer NiI$_2$ hosts robust type-II multiferroicity down to the two-dimensional limit, a giant dynamical magnetoelectric coupling at terahertz frequencies, and an electrically switchable spin polarization. These developments present the possibility of engineering ultrafast, low-energy-consumption, and electrically-tunable spintronic devices based on the collective excitations of the multiferroic order, electromagnons. However, the direct visualization of these bosonic modes in real space and within the monolayer limit remains elusive. Here, we report the atomic-scale observation of electromagnons in monolayer NiI$_2$ using low-temperature scanning tunneling microscopy. By tracking the thermal evolution of the multiferroic phase, we establish the energy scale and resolve coherent in-gap excitations of the symmetry-broken multiferroic state. Comparison with first-principles and spin-model calculations reveals that the low-energy modes originate from electromagnon excitations. Spatially resolved inelastic tunneling spectroscopy maps show a stripe-like modulation of the local spectral function at electromagnon energies, matching theoretical predictions. These results provide direct evidence of the internal structure of electromagnons and establish a methodology to probe these modes at the atomic scale, opening avenues for electrically tunable spintronics.

cond-mat.mtrl-sci↗

arXiv2025

Prompting Strategies for Language Model-Based Item Generation in K-12 Education: Bridging the Gap Between Small and Large Language Models

This study explores automatic generation (AIG) using language models to create multiple choice questions (MCQs) for morphological assessment, aiming to reduce the cost and inconsistency of manual test development. The study used a two-fold approach. First, we compared a fine-tuned medium model (Gemma, 2B) with a larger untuned one (GPT-3.5, 175B). Second, we evaluated seven structured prompting strategies, including zero-shot, few-shot, chain-of-thought, role-based, sequential, and combinations. Generated items were assessed using automated metrics and expert scoring across five dimensions. We also used GPT-4.1, trained on expert-rated samples, to simulate human scoring at scale. Results show that structured prompting, especially strategies combining chain-of-thought and sequential design, significantly improved Gemma's outputs. Gemma generally produced more construct-aligned and instructionally appropriate items than GPT-3.5's zero-shot responses, with prompt design playing a key role in mid-size model performance. This study demonstrates that structured prompting and efficient fine-tuning can enhance midsized models for AIG under limited data conditions. We highlight the value of combining automated metrics, expert judgment, and large-model simulation to ensure alignment with assessment goals. The proposed workflow offers a practical and scalable way to develop and validate language assessment items for K-12.

cs.CL↗

arXiv2025

Dynamic Risk-Adjusted Monitoring of Time Between Events: Applications of NHPP in Pipeline Accident Surveillance

Monitoring time between events (TBE) is a critical task in industrial settings. Traditional Statistical Process Monitoring (SPM) methods often assume that TBE variables follow an exponential distribution, which implies a constant failure intensity. While this assumption may hold for products with homogeneous quality, it is less appropriate for complex systems, such as repairable systems, where failure mechanisms evolve over time due to degradation or aging. In such cases, the Non-Homogeneous Poisson Process (NHPP), which accommodates time-varying failure intensity, is a more suitable model. Furthermore, failure patterns in complex systems are frequently influenced by risk factors, including environmental conditions and human interventions, and system failures often incur restoration costs. This work introduces a novel approach: a risk-adjusted control chart based on the NHPP model, specifically designed to monitor the ratio of cost to TBE, referred to as the average cost per time unit (AC). The proposed method is evaluated through extensive simulations, demonstrating its superior performance. Additionally, the chart is applied to monitor pipeline accidents over time, accounting for the impact of various risk factors. These results highlight the effectiveness of the developed chart in enhancing monitoring capabilities for complex systems.

stat.ME↗

arXiv2024

Strain-induced two-dimensional topological crystalline insulator

Topological crystalline insulators (TCIs) host topological phases of matter protected by crystal symmetries. Topological surface states in three-dimensional TCIs have been predicted and observed in IV-VI SnTe-class semiconductors. Despite the prediction of a two-dimensional (2D) TCI characterized by two pairs of edge states inside the bulk gap, materials challenges have thus far prevented its experimental realization. Here we report the growth and characterization of bilayer SnTe on the 2$H$-NbSe$_2$ substrate by molecular beam epitaxy and scanning tunneling microscopy. We experimentally observe two anticorrelated, periodically modulated pairs of conducting edge states along the perimeters of the sample with a large band gap exceeding $0.2$ eV. We identify these states with a 2D TCI through first principles calculations. Finally, we probe the coupling of adjacent topological edge states and demonstrate the resulting energy shift driven by a combination of electrostatic interactions and tunneling coupling. Our work opens the door to investigations of tunable topological states in 2D TCIs, of potential impact for spintronics and nanoelectronics applications at room temperature.

cond-mat.mes-hall↗

arXiv2023

Atomic-scale visualization of multiferroicity in monolayer NiI$_2$

Progress in layered van der Waals materials has resulted in the discovery of ferromagnetic and ferroelectric materials down to the monolayer limit. Recently, evidence of the first purely two-dimensional multiferroic material was reported in monolayer NiI$_2$. However, probing multiferroicity with scattering-based and optical bulk techniques is challenging on 2D materials, and experiments on the atomic scale are needed to fully characterize the multiferroic order at the monolayer limit. Here, we use scanning tunneling microscopy (STM) supported by theoretical calculations based on density functional theory (DFT) to probe and characterize the multiferroic order in monolayer NiI$_2$. We demonstrate that the type-II multiferroic order displayed by NiI$_2$, arising from the combination of a magnetic spin spiral order and a strong spin-orbit coupling, allows probing the multiferroic order in the STM experiments. Moreover, we directly probe the magnetoelectric coupling of NiI$_2$ by external electric field manipulation of the multiferroic domains. Our findings establish a novel point of view to analyse magnetoelectric effects at the microscopic level, paving the way towards engineering new multiferroic orders in van der Waals materials and their heterostructures.

cond-mat.mtrl-sci↗

arXiv2023

Emergence of Exotic Spin Texture in Supramolecular Metal Complexes on a 2D Superconductor

Designer heterostructures, where the desired physics emerges from the controlled interactions between different components, represent one of the most powerful strategies to realize unconventional electronic states. This approach has been particularly fruitful in combining magnetism and superconductivity to create exotic superconducting states. In this work, we use a heterostructure platform combining supramolecular metal complexes (SMCs) with a quasi-2D van der Waals (vdW) superconductor NbSe$_2$. Our scanning tunneling microscopy (STM) measurements demonstrate the emergence of Yu-Shiba-Rusinov (YSR) bands arising from the interaction between the SMC magnetism and the NbSe$_2$ superconductivity. Using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements, we show the presence of antiferromagnetic coupling between the SMC units. These result in the emergence of an unconventional $3\times3$ reconstruction in the magnetic ground state that is directly reflected in real space modulation of the YSR bands. The combination of flexible molecular building blocks, frustrated magnetic textures, and superconductivity in heterostructures establishes a fertile starting point to fabricating tunable quantum materials, including unconventional superconductors and quantum spin liquids.

cond-mat.supr-con↗

arXiv2023

Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning

Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios where centralized training is either not possible or not practical. One of the critical challenges in decentralized deep MARL is the non-stationarity of the learning environment when multiple agents are learning concurrently. A commonly used and efficient scheme for decentralized MARL is independent learning in which agents concurrently update their policies independently of each other. We first show that independent learning does not always converge, while sequential learning where agents update their policies one after another in a sequence is guaranteed to converge to an agent-by-agent optimal solution. In sequential learning, when one agent updates its policy, all other agent's policies are kept fixed, alleviating the challenge of non-stationarity due to simultaneous updates in other agents' policies. However, it can be slow because only one agent is learning at any time. Therefore it might also not always be practical. In this work, we propose a decentralized cooperative MARL algorithm based on multi-timescale learning. In multi-timescale learning, all agents learn simultaneously, but at different learning rates. In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate. This speeds up sequential learning, while also minimizing non-stationarity caused by other agents updating concurrently. Multi-timescale learning outperforms state-of-the-art decentralized learning methods on a set of challenging multi-agent cooperative tasks in the epymarl(Papoudakis et al., 2020) benchmark. This can be seen as a first step towards more general decentralized cooperative deep MARL methods based on multi-timescale learning.

cs.LG↗

arXiv2022

Visualization of moir\'e magnons in monolayer ferromagnet

Two-dimensional magnetic materials provide an ideal platform to explore collective many-body excitations associated with spin fluctuations. In particular, it should be feasible to explore, manipulate and ultimately design magnonic excitations in two-dimensional van der Waals magnets in a controllable way. Here we demonstrate the emergence of moir\'e magnon excitations, stemming from the interplay of spin-excitations in monolayer CrBr$_3$ and the moir\'e pattern stemming from the lattice mismatch with the underlying substrate. The existence of moir\'e magnons is further confirmed via inelastic quasiparticle interference, showing the appearance of a dispersion pattern correlated with the moir\'e length scale. Our results provide a direct visualization in real-space of the dispersion of moir\'e magnons, demonstrating the versatility of moir\'e patterns in creating emerging many-body excitations.

cond-mat.mes-hall↗

arXiv2022

Control of molecular orbital ordering using a van der Waals monolayer ferroelectric

Two-dimensional (2D) ferroelectric materials provide a promising platform for the electrical control of quantum states. In particular, due to their 2D nature, they are suitable for influencing the quantum states of deposited molecules via the proximity effect. Here, we report electrically controllable molecular states in phthalocyanine molecules adsorbed on monolayer ferroelectric material SnTe. In particular, we demonstrate that the strain and ferroelectric order in SnTe creates a transition between two distinct orbital orders in the adsorbed phthalocyanine molecules. By controlling the polarization of the ferroelectric domain using scanning tunneling microscopy (STM), we have successfully demonstrated that orbital order can be manipulated electrically. Our results show how ferroelastic coupling in 2D systems allows control of molecular states, providing a starting point for ferroelectrically switchable molecular orbital ordering and ultimately, electrical control of molecular magnetism.

cond-mat.mtrl-sci↗

arXiv2022

Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.

cs.AI↗