jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lingyun Zhu | Haoran Wei | B. H. Wu | Wenhan Cao
Source: SmartMat (article, 2025-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Terahertz Holographic Image Encryption via Thermally Tunable Directional Janus Metasurface ABSTRACT Terahertz (THz) holography demonstrates immense potential in biomedical detection, virtual reality, and information encryption security. Janus metasurfaces, which can independently control the amplitude and phase of THz waves from opposite directions, offer a promising platform for directional multiplexing in THz holography. However, most existing designs rely on conventional forward design methods, limiting the system to just two degrees of freedom (DOFs) and significantly restricting both channel count and holography imaging quality. In this study, we introduce a bidirectional deep neural network (Bi‐DNN) inverse design method, combined with thermally responsive phase‐change material vanadium dioxide (VO 2 ), for the development of Janus metasurfaces. The proposed design enables independent control of incident direction, frequency, and temperature as three distinct DOFs. The meta‐atoms selected through the Bi‐DNN exhibit a transmittance exceeding 90%, effectively generating low‐crosstalk, eight‐channel THz holographic images with an imaging efficiency of 78%. Furthermore, by combining the metasurface with the inherent superposition properties of Chinese characters to achieve high‐intensity and high‐density holographic encryption in the THz band. This design offers a novel strategy and technical foundation for applications in high‐capacity data storage, holographic encryption, and secure wireless communication. Terahertz radiation ; Holography ; Encryption ; Janus ; Superposition principle ; Multiplexing ; Degrees of freedom (physics and chemistry) ; Computer-generated holography Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Pei‐Kun Yang
Source: ArXiv.org (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Quantum Machine Learning for Predicting Binding Free Energies in Structure-Based Virtual Screening In structure-based virtual screening, it is often necessary to evaluate the binding free energy of protein-ligand complexes by considering not only molecular conformations but also how these structures shift and rotate in space. The number of possible combinations grows rapidly and can become overwhelming. While classical computing has limitations in this context, quantum computing offers a promising alternative due to its inherent parallelism. In this study, we introduce a quantum machine learning approach that encodes molecular information into quantum states and processes them using parameterized quantum gates. The model is implemented and trained using PyTorch, and its performance is evaluated under three settings: ideal simulation, limited-shot sampling, and simulations with quantum noise. With six quantum circuit units, the model achieves an RMSD of 2.37 kcal/mol and a Pearson correlation of 0.650. Even when using 100,000 shots, the predictions remain consistent, indicating that the model is compatible with near-term quantum hardware. Although noise slightly reduces accuracy, the ranking of ligand affinities remains largely unchanged. These findings point to a practical and scalable strategy that balances robustness and predictive power, offering a viable path to accelerate virtual screening through moderately deep quantum circuits. Quantum ; Robustness (evolution) ; Scalability ; Quantum computer ; Quantum algorithm ; Parameterized complexity ; Noise (video) ; Virtual screening Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Klaus Roppert | Lukas Domenig | Alice Reinbacher‐Köstinger | Manfred Kaltenbacher | Laurent Daniel
Source: IEEE Transactions on Magnetics (article, 2025-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Neural Anhysteresis Surrogate for Magneto-Elastic Vector Hysteresis in Device Simulations In many applications, accurately capturing the magneto-mechanical coupling and dissipative effects at the material level is essential for realistic simulations. Embedding the Simplified Multiscale Model (SMSM) inside an energy-based hysteresis framework yields high fidelity but is computationally intensive for three dimensional finite element (FE) analyses. This paper introduces NNSMSM, a physics-informed multi-task deep neural network that emulates the expensive SMSM operator. A hybrid Latin-hypercube/Sobol sampling strategy efficiently explores the magneto-mechanical loading space. The network is trained with a composite loss that simultaneously fits magnetisation and magnetostrictive strain while enforcing reciprocity and positive definiteness of the susceptibility tensor. The traced TorchScript model is linked to the open source FE software openCFS, replacing the SMSM inside the Vector Play Model (VPM) hysteresis model with zero code changes. The benchmark of a PMSM device simulation shows a speed-up of wall clock time by a factor of 11 while preserving global accuracy of hysteresis losses. Dissipative system ; Hysteresis ; Magnetostriction ; Artificial neural network ; Benchmark (surveying) ; Reciprocity (cultural anthropology) ; Finite element method ; Embedding ; Coupling (piping) Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Electric Motor Design and Analysis
Authors: Chen, Haotian | Huang, Chenyang | Rodríguez, Alexander | Mistry, Aashutosh | Viswanathan, Venkatasubramanian
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Differentiable Electrochemistry: A paradigm for uncovering hidden physical phenomena in electrochemical systems Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly linked. Here we introduce Differentiable Electrochemistry, a mew paradigm in electrochemical modeling that integrates thermodynamics, kinetics and mass transport with differentiable programming enabled by automatic differentiation. By making the entire electrochemical simulation end-to-end differentiable, this framework enables gradient-based optimization for mechanistic discovery from experimental and simulation data, achieving approximately one to two orders of improvement over gradient-free methods. We develop a rich repository of differentiable simulators across diverse mechanisms, and apply Differentiable Electrochemistry to bottleneck problems in kinetic analysis. Specifically, Differentiable Electrochemistry advances beyond Tafel and Nicholson method by removing several limitations including Tafel region selection, and identifies the electron transfer mechanism in Li metal electrodeposition/stripping by parameterizing the full Marcus-Hush-Chidsey formalism. In addition, Differentiable Electrochemistry interprets Operando X-ray measurements in concentrated electrolyte by coupling concentration and velocity theories. This framework resolves ambiguity when multiple electrochemical theories intertwine, and establishes a physics-consistent and data-efficient foundation for predictive electrochemical modeling. Differentiable function ; Tafel equation ; Electrochemistry ; Bottleneck ; Electrolyte ; Representation (politics) ; Ambiguity ; Electrochemical kinetics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Sébastien Cueff
Source:  (article, 2025-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable nanophotonics with low-loss phase-change materials Phase change materials (PCMs) are revolutionizing nanophotonics by providing ways to locally tune and reconfigure optical devices. The last decade has witnessed many exciting reports of novel devices exploiting PCMs such as for example beam-steering, tunable light emission, reflection and absorption, programmable metasurfaces and reconfigurable neural networks. A large majority of the first studies were using standard PCMs such as GeSbTe as simple binary on-off switches, in which the ON state is the amorphous phase and the OFF state the fully crystalline phase. However, PCMs present another degree of freedom for tunability: the possibility to encode multilevel non-volatile states via partial change of phase. Furthermore, recently a new class of low-loss PCMs emerged (e.g. Sb2S3 and Sb2Se3), with negligible optical absorption in the near-infrared. In this talk, we will present recent results on original methods to program PCMs into various multilevel states of crystallization. We will then present nanophotonic devices leveraging this multilevel programming and conclude on the perspectives of this technology Nanophotonics ; Photonics ; Reflection (computer programming) ; Optical switch ; State (computer science) ; Metamaterial ; Binary number Liquid Crystal Research Advancements ; Phase-change materials and chalcogenides ; Photonic Crystals and Applications
Authors: Klaus Roppert | Lukas Domenig | A Reinbacher-Köstinger | Manfred Kaltenbacher | Laurent Daniel
Source:  (preprint, 2025-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Neural Anhysteresis Surrogate for Magneto-Elastic Vector Hysteresis in Device Simulations In many applications, accurately capturing the magneto-mechanical coupling and dissipative effects at the material level is essential for realistic simulations. Embedding the Simplified Multiscale Model (SMSM) inside an energy-based hysteresis framework yields high fidelity but is computationally intensive for three dimensional finite element (FE) analyses. This paper introduces NNSMSM, a physics-informed multi-task deep neural network that emulates the expensive SMSM operator. A hybrid Latin-hypercube/Sobol sampling strategy efficiently explores the magneto-mechanical loading space. The network is trained with a composite loss that simultaneously fits magnetisation and magnetostrictive strain while enforcing reciprocity and positive definiteness of the susceptibility tensor. The traced TorchScript model is linked to the open source FE software openCFS, replacing the SMSM inside the Vector Play Model (VPM) hysteresis model with zero code changes. The benchmark of a PMSM device simulation shows a speed-up of wall clock time by a factor of 11 while preserving global accuracy of hysteresis losses. Hysteresis ; Physics ; Magneto ; Statistical physics ; Condensed matter physics ; Quantum mechanics ; Magnet Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Electric Motor Design and Analysis
Authors: Qian-Yu Liu (4255543) | Yichen Li (200944) | Dongxiao Chen (6509291) | Philippe Sautet (1268361)
Source: Figshare (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Size- and Facet-DependentBehavior of Electron Storageat the Au/Anatase TiO<sub>2</sub> Interface under Ambient Conditionsfrom Machine Learning The interfacial structure and electronic properties of gold (Au) nanoclusters supported on anatase TiO<sub>2</sub> surfaces are key determinants of catalytic and photocatalytic activity for Au/TiO<sub>2</sub> systems. Here, we present an atomistic study of realistic, ∼1.5 nm Au nanoclusters on reconstructed TiO<sub>2</sub>(001) and (101) facets under ambient air conditions, bridging the gap between small supported cluster models and experimental catalysts. We focus on the ability of Au/TiO<sub>2</sub> to store photogenerated electrons that can be used later in dark conditions. Leveraging a high-dimensional Au–Ti–O neural-network potential coupled with stochastic surface walking and DFT refinement, we exhaustively sampled atomic configurations over 120,000 minima. Two distinct interfacial motifs emergeType (i), with oxygen adsorbed on Ti sites near the Au nanocluster, and Type (ii), with oxygen occupying Au<sub>3</sub> hollow sites. While both motifs stabilize clusters on both facets, Type (ii) is notably more prevalent on (101), giving rise to deeper-lying O 2p states and a larger fraction of fully oxidized Au<sup>δ+</sup> (δ ≥ 1) that can passivate the cluster. In contrast, Type (i) optimally balances neutral Au<sup>0</sup> and partially oxidized Au<sup>δ+</sup> (0 < δ < 1), enabling more than 0.90 vacant state per interfacial Au atom and enhancing electron-storage capability. Coupled with facet-dependent behavior and oxidation-state distributions, these findings deliver direct, experimentally relevant design principles for tailoring the electronic functionality of Au/TiO<sub>2</sub> catalysts. Nanoclusters ; Passivation ; Cluster (spacecraft) ; Electron ; Bridging (networking) ; Oxygen ; Electronic structure ; Adsorption ; Anatase Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; TiO2 Photocatalysis and Solar Cells
Authors: Zihang Guo | Z. Chen | Zhiyu Li | Shuli Yin | Xikui Ma
Source: IEEE Transactions on Magnetics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-objective Magnetic Design of Powder Core Inductors Based on Deep Reinforcement Learning A novel multi-objective optimization approach for the magnetic design of power inductors is proposed, leveraging the deep reinforcement learning (DRL). The optimization problem is formulated using an artificial neural network (ANN) with the encoder-decoder architecture. The encoder processes the design specifications and material properties to ensure the model’s generalization, while the decoder indirectly generates the optimal design variables by modeling their probability distributions. Trained via reinforcement learning, the DRL-based method addresses the design task without iterations, significantly reducing computational time. Additionally, compared to the heuristic algorithms like non-dominated sorting genetic algorithms (NSGA-II) and the decomposition-based multiobjective evolutionary algorithms (MOEA/Ds), the proposed approach produces consistent Pareto fronts across multiple repeated runs and ensures a more uniform distribution of solutions. The effectiveness of the approach is further validated through a case study on a powder core inductor for inverters, highlighting its advantages in the convergence, stability, solution diversity, and efficiency. Reinforcement learning ; Inductor ; Sorting ; Multi-objective optimization ; Genetic algorithm ; Core (optical fiber) ; Artificial neural network ; Heuristic ; Optimal design Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Engineering Applied Research
Authors: munagala alivelu | Natte Kavitha
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Seeded Cooling Crystallization of Raloxifene Hydrochloride: Real Time Monitering of Nucleation,, MSZW (Meta Stable Zone Width), and PSD (Particle Size Distribution using FBRM (Focused Beam Reflectance Measurement) Crystallization ; Seeding ; Nucleation ; Seed crystal ; Crystal (programming language) ; Particle size ; Particle-size distribution Crystallization and Solubility Studies ; Enzyme Structure and Function ; Crystallography and molecular interactions
Authors: Zhilin Liu (6848051) | Nan Zhang (46264) | Xingyu Zhao (2669941) | Mingxiu Liu (11779013) | Yaru Shi (2497831) | Liujian Qi (4906873) | Yuting Zou (9996598) | Chunlu Chang (19863391) | Fan Tan (11532463) | Dabing Li (4053046) | Shaojuan Li (17898)
Source: Figshare (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

High-PerformanceBroadband Integrated Detection Systemfor Multifunctional Applications In the digital era, photodetectors have become indispensable components in optical communication, imaging, and artificial intelligence, driven by the integration of multidisciplinary technologies and the expanding application scenarios. However, traditional photodetectors, constrained by fixed-bandgap semiconductors, falter in delivering broadband sensitivity and miniaturized integration. While narrow-bandgap two-dimensional (2D) materials offer promising solutions for broad-spectrum detection due to their exceptional spectral absorption capabilities, they are still hindered by excessive dark currents (>10<sup>–5</sup> A) and noise-induced signal ambiguity, which degrade photosensitivity (<i>I</i><sub>light</sub>/<i>I</i><sub>dark</sub>) and limit real-world utility. Here, we transcend these limitations through a monolithically integrated architecture that synergizes a Ta<sub>2</sub>NiSe<sub>5</sub> photodetector with a MoS<sub>2</sub> field-effect transistor (FET) amplification unit, which weakens the noise effect while maintaining its unparalleled photoconductive efficiency. The Ta<sub>2</sub>NiSe<sub>5</sub> photodetector exhibits a broadband photoresponse in the 635–1550 nm range, with a maximum responsivity of 83.5 A/W and detectivity of 2.1 × 10<sup>10</sup> Jones, while the photosensitivity is amplified up to 3.7 × 10<sup>3</sup> with a 3200-fold enhancement under 1550 nm illumination by integrating the FET as an amplification unit. This breakthrough enables high-contrast single-pixel imaging with 99% neural network recognition accuracy, outperforming bare Ta<sub>2</sub>NiSe<sub>5</sub> detectors in training efficiency. Based on the above performance optimization, we demonstrated signal-coded transmission and high-fidelity signal reproduction in fiber-optic communication bands and executed reconfigurable OR/AND logic operations via gate voltage and optical inputs, demonstrating dual-field (optical/electrical) programmability. These results indicate that the system has significant potential for secure optical communications, digital circuits, and artificial vision applications and provides a promising technological path for developing high-performance and multifunctional on-chip optoelectronic systems. Photodetector ; Broadband ; Responsivity ; Detector ; Sensitivity (control systems) ; SIGNAL (programming language) ; Optical communication ; Noise (video) ; Transistor 2D Materials and Applications ; Nanowire Synthesis and Applications ; Thermal Radiation and Cooling Technologies
Authors: Zhizhi Fu | Lu Liu | Qiannan Duan | Liulu Yao | Qianru Wan | Chi Zhou | Weidong Wu | Fei Wang | Jianchao Lee
Source: Talanta (article, 2025-10-03)
Field: Chemistry — Analytical Chemistry

Deep-learning-driven spectral image analysis for intelligent monitoring of multiple pesticides and antibiotics Pesticide ; Oxytetracycline ; Pollutant ; Chemometrics ; Pesticide residue ; Cypermethrin ; Tetracycline ; Absorbance Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Hao Zhou | Khalid Zobaid Adnan | W. Linwood Jones | Tianli Feng
Source: ArXiv.org (preprint, 2025-03-07)
Field: Materials Science — Materials Chemistry

Thermal boundary conductance in standalone and non-standalone GaN/AlN heterostructures predicted using machine learning interatomic potentials GaN/AlN interfaces are essential in advanced high-power and high-frequency electronic devices, where effective thermal management is crucial for optimal performance and reliability. This work investigates the thermal boundary conductance (TBC) of standalone and non-standalone GaN/AlN heterostructures using non-equilibrium molecular dynamics (NEMD) driven by accurate machine learning interatomic potentials trained from density-functional theory calculations. For the standalone interface, the TBC is found to be ~600 MW m-2 K-1 at room temperature after quantum correction, with possible ~60% elastic and ~40% inelastic contributions. The result revises previous NEMD predictions (400 - 2000 MW m-2 K-1) using empirical interatomic potentials. When a second GaN/AlN interface is brought close to the original interface, the TBC of the original interface can increase to 1000 MW m-2 K-1, and this value gradually decreases with increasing distance between the two interfaces. When multiple interfaces are introduced in proximity, the first interface's TBC can be further enhanced to above 1150 MW m-2 K-1. After comparing double interfaces, superlattices, and random multilayers, it is concluded that such enhancement of TBC is not caused by the emergence of coherent superlattice modes but rather by the ballistic transport of the original bulk phonon modes of each material. Additionally, a "critical separation distance" (l_cs) is defined as the threshold beyond which the two interfaces no longer influence each other and behave independently. l_cs is determined by the mean free path of the phonons filtered by the interfaces that transport in the middle layer between two interfaces. Our findings provide insights into the thermal transport mechanisms that may aid the design of future electronic devices. Phonon ; Heterojunction ; Thermal conductivity ; Interatomic potential ; Superlattice ; Conductance ; Work (physics) ; Thermal Thermal properties of materials ; GaN-based semiconductor devices and materials ; Machine Learning in Materials Science
Authors: Marković, Ksenija | Kovinčić, Anika | Ristić, Danijela | Nikolić, Ana | Petrović, Tanja | Branković-Radojčić, Dragana | Ignjatović-Micić, Dragana
Source: RIK (Maize Research Institute Zemun Polje (Belgrade, Serbia)) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Maize variety verification and genetic purity methodologies: Theory vs. practice The basis of sustainable maize production relies on high-quality seed, with variety verification and genetic purity being essential determinants. Conventionally used grow-out tests (GOTs), while indispensable for assessing overall phenotypic expression under field conditions, are time-consuming, resource-intensive, and susceptible to environmental influences, often providing results too late in the seed production cycle for effective intervention. Biochemical markers (seed storage proteins/isozymes) in most cases environmentally independent, enable quick and precise analysis of maize varieties uniformity and purity. Still, biochemical assays cannot always effectively distinguish closely related inbred lines or hybrids. Advancements in molecular marker technologies have revolutionized genetic purity assessment. Simple Sequence Repeats (SSRs) and, increasingly, Single Nucleotide Polymorphisms (SNPs) offer highly precise, rapid, and environmentally independent means for distinguishing varieties and detecting off-types. DNA-based methods enable rapid and efficient analyses, significantly reducing time and land required, compared to traditional field trials. New emerging technologies such as hyperspectral imaging and machine learning algorithms are showing promise for non-destructive and automated seed identification based on miniscule phenotypic differences. Despite these technological leaps, practical integration of advanced methods includes challenges, such as initial investment in laboratory infrastructure, high costs of analytical consumables, the need for standardized protocols and their inclusion into national regulations. Furthermore, the volume and diversity of maize germplasm requires robust reference databases for accurate comparison. In conclusion, an integrated strategy combining the essential field-based validation of GOTs with the precision and speed of laboratory techniques, is vital. This should strengthen seed quality assurance systems, safeguard varietal integrity, and ultimately enhance maize productivity and food security globally, ensuring that farmers receive the precise genetic material they intend to plant. Germplasm ; Identification (biology) ; Food security ; Production (economics) ; Variety (cybernetics) ; Field (mathematics) Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding ; Genetic Mapping and Diversity in Plants and Animals