jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingjing Zhou | Jingqi Zhang | Huakun Xia | Rongcao Yang
Source: Physica Scripta (article, 2025-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-enhanced multifunctional THz metasurfaces via conditional variational autoencoder Abstract The conventional process for developing multifunctional metasurfaces relies on trial-and-error simulation-based optimization methods. This approach is not only time-consuming, but also requires a high level of expertise from the designers and often does not produce the desired results. Deep learning can automate the design process, enabling rapid and intelligent design of multifunctional metasurfaces. However, there are still limitations in deep learning-assisted modeling and design, such as limited design freedom and single-function realization. In this paper, a deep learning framework based on a conditional variational autoencoder (CVAE) network is proposed for the design of multifunctional terahertz (THz) metasurfaces. We introduce vanadium dioxide (VO 2 ) phase-change material into the design of metasurfaces with random patterns to achieve switchable functionality between absorption and polarization conversion. The adopted network architecture incorporates a conditional discriminative mechanism on the basis of a variational autoencoder, enabling the network to more accurately identify spectral characteristics and generate corresponding metasurface structures. The absorption and the polarization conversion rate (PCR) of the generated structures have been shown to closely match the target spectra. With the development of intelligent design technology, the practical applications of metasurface absorbers and polarization converters in fields such as stealth technology and optical communication will be significantly accelerated. Autoencoder ; Terahertz radiation ; Deep learning ; Materials science ; Computer science ; Optoelectronics ; Artificial intelligence ; Nanotechnology Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Superconducting and THz Device Technology
Authors: F. F. Tong | J. L. Han
Source: Digest Journal of Nanomaterials and Biostructures (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

GNN-Directed Design of MXene–MOF Nanozymes Enabling High-Performance Peroxidase-Like Biosensing for Glucose Assay The rational integration of artificial intelligence into materials science is demonstrated through the development of a Graph Neural Network (GNN)-directed MXene–MOF hybrid nanozyme for glucose biosensing. A GNN trained on 150 density functional theory (DFT)- calculated structures achieved a mean absolute error of 0.08 eV and an R² of 0.985 in predicting • OH binding energies, enabling high-throughput screening of 1,500 candidates. The model identified a Cu-BTC/Ti₃C₂Tx composite at a 2:1 ratio as the optimal architecture, which was subsequently synthesized and thoroughly characterized. Structural analyses confirmed uniform MOF anchoring on delaminated MXene sheets, strong Ti–O–Cu interfacial bonding, and a surface area of 820 m²/g. The resulting hybrid displayed significantly enhanced peroxidase-like activity, with catalytic rates 8-fold higher than pure MOF and more than 20-fold greater than MXene. Steady-state kinetic analysis revealed a Km of 0.12 mM for H₂O₂, over 30 times lower than natural horseradish peroxidase (3.7 mM), and an elevated Vmax, indicating strong substrate affinity and rapid turnover. In the detection of glucose, the nanozyme facilitated a highly responsive colorimetric method, delivering a broad linear response range from 1.0 to 250 μM with an excellent correlation coefficient of 0.998. The approach achieved a detection limit as low as 0.72 μM. Furthermore, the system demonstrated remarkable specificity, showing minimal interference from commonly encountered species. Application to spiked human serum samples yielded recovery rates of 97.2–104.5% with RSD &lt;5%, confirming robustness for clinical diagnostics. These results highlight the power of GNN-directed inverse design in accelerating the discovery of high-performance catalytic materials for biosensing applications. Biosensor ; Detection limit ; Horseradish peroxidase ; Linear range ; Catalysis ; Robustness (evolution) ; Response surface methodology ; Inverse Advanced Nanomaterials in Catalysis ; MXene and MAX Phase Materials ; Nanoplatforms for cancer theranostics
Authors: Kellie
A. Stellmach (13001535) | McKinley K. Paul (10283138) | Yong-Liang Su (4533271) | Rampi Ramprasad (1355760) | Anthony C. Engler (17040763) | Will R. Gutekunst (1312422)
Source: Figshare (article, 2025-04-13)
Field: Materials Science — Polymers and Plastics

Improving theAccuracy of Ceiling Temperature Measurements:Best Practices and Common Pitfalls The investigation of low ceiling temperature (<i>T</i><sub>c</sub>) monomers is an active area of research in the field of polymer science to address modern challenges in waste and recycling. Many commendable contributions have been made to this field; however, a thorough survey of the literature has revealed common oversights in the calculations of the changes in enthalpy (Δ<i>H</i><sub>p</sub>) and entropy (Δ<i>S</i><sub>p</sub>) upon polymerization as well as <i>T</i><sub>c</sub>. This Perspective aims to clarify how to avoid these pitfalls, accurately calculate these values, and outline best practices for experimentally measuring these key thermodynamic parameters. In an era where researchers are increasingly reliant on the quality of data, especially for endeavors in machine learning and artificial intelligence, it is important to establish a unified approach for making these calculations to optimize precision and accuracy. Ceiling (cloud) ; Best practice ; Entropy (arrow of time) ; Perspective (graphical) ; Set (abstract data type) ; Field (mathematics) Polymer crystallization and properties ; Membrane Separation and Gas Transport ; Thermal and Kinetic Analysis
Authors: Saeid Zahedi Asl | Shayan Abaei | Hosein Alimardani | Mohammadreza Beydaghdari | Fahimeh HooriAbadSaboor | Mehrdad Asgari
Source: ChemRxiv (article, 2025-08-14)
Field: Chemistry — Inorganic Chemistry

Transforming MOF Design Paradigms: Data-Driven Insights into MIL-100(Fe) Synthesis for Application-Specific Optimization of Dye Removal Performance Traditional MOF design often maximizes generic metrics such as BET surface area and crystallinity, assuming they universally predict performance. We present a machine-learning-guided optimization framework for MIL-100(Fe) from experimentally synthesized samples. All materials were synthesized via an acid-free, water-based hydrothermal route. We trained small-data ML models to link synthesis parameters, including temperature, time, metal-to-ligand ratio, and ion concentration, to key properties comprising surface area, total pore volume, average crystallite size, crystallinity, yield, and methylene blue (MB) removal. SHAP analysis showed that time and metal-to-ligand ratio dominated dye removal, whereas surface area was more sensitive to temperature and time. The most accurate model, gaussian process regression was coupled with a genetic algorithm (GA) to optimize synthesis for property-specific targets. Through optimization, we achieved a 5.37% rise in BET surface area (1748\ to 1841.9 m²/g) and a 12.1% gain in MB removal (88.6 to 98.3%) over the dataset’s best results. The MB removal optimized sample with a surface area of 1274.3 m²/g and 17.4% crystallinity, delivers the highest MB uptake of 98.3%. While, optimized sample for surface area, reaches 1841.9 m²/g (about 44% higher) with 34.9% crystallinity but achieves only 85.1% removal, about 13% lower than optimized MB sample; indicating that even substantial increases in surface area do not govern adsorption performance. A t-SNE analysis further revealed distinct clusters in property space, with optimized samples occupying separate neighborhoods, elucidating that MOF synthesis should be tailored to the target application rather than a single metric such as surface area. Computer science ; Process engineering ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications
Authors: L. Böttcher
Source: IMAPSource Proceedings (article, 2025-01-14)
Field: Materials Science — Biomaterials

High Density Organic Substrates for Chiplet Technologies The industry’s increasing demand for high-performance electronic devices with better functionality, lower power consumption and higher speed is driving innovation in advanced packaging technology. Improving semiconductor chip performance and advancing package characteristics are critical. These ever-increasing demands on electronic systems have led to the current trend of increasingly relying on so-called chiplets instead of individual chips. In detail, this means that individual silicon building blocks or functional units, which can also be developed and manufactured independently of each other, are combined to form a very powerful overall system. These then find application, for example, in neural networks or advanced driver assistance systems, and generally in all types of AI systems. Since miniaturization of the semiconductor chip is a major challenge, minimizing the wiring length in the package by using denser and thinner interconnects is crucial. The high number of inputs and outputs (I/O) in these high-density packages reduces the structural size of lines and spaces (L/S), and the quality of interconnection of these fine spaces can drastically affect the performance of the device. This paper presents the development of the required technology blocks for high-density redistribute layers needed to realize such organic substrates. The technology approach used is an advanced semi-additive processing (aSAP) at large panel level. This technology involves the use of dielectric layers, such as ABF or similar, PVD seeding and additive electrolytic copper deposition. The following various interconnect blocks required are discussed in detail: Vertical interconnects can be made by laser drilling, lithography when photo-imageable dielectrics are used, or plasma with reactive ion etching (RIE). The process options and limitations are discussed in detail. Horizontal interconnects are fabricated by seeding, photo-imaging of plating masks and subsequent copper deposition, and resist and seed removal. Photo-imaging is an important process step in this process. The evolution towards line and space structures targeting 5µm and further to 2µm on large panels up to 610x457mm² is described in detail. Finally, first electrical test data and the resulting process yield for different structure sizes are presented and discussed. Substrate (aquarium) ; Biology ; Ecology Advanced Cellulose Research Studies
Authors: Bo Gao | Youcai Xiao | Wanqian Yu | Kepeng Qu | Yi Sun
Source: Polymers (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Mesoscale Damage Evolution, Localization, and Failure in Solid Propellants Under Strain Rate and Temperature Effects High-energy solid propellants are multiphase engineering materials, whose mechanical behavior is predominantly governed by the characteristics of embedded crystalline particles. While microstructural influences have been extensively examined, quantitative correlations between microstructure and macroscopic mechanical properties remain underexplored. This work develops a cohesive finite element method (CFEM) framework to quantify the thermomechanical response of high-energy solid propellants at the microstructural scale. The analysis focuses on impact loading at strain rates ranging from 10<sup>3</sup> to 10<sup>4</sup> s<sup>-1</sup>, accounting for large deformation, thermomechanical coupling, and microcrack-induced failure. Damage evolution under impact conditions was evaluated using a combined neural network-based inverse identification method and a three-dimensional cohesive finite element model to determine temperature-dependent bilinear-polynomial cohesive parameters. Results demonstrate a strong dependence of the propellant's mechanical behavior on both strain rate and temperature. Validation against experimental data confirms that the proposed temperature-sensitive CFEM accurately predicts both damage progression and macroscopic mechanical responses. Materials science ; Propellant ; Work (physics) ; Strain rate ; Composite material ; Finite element method ; Deformation (meteorology) ; Microstructure ; Bilinear interpolation ; Mesoscale meteorology ; Structural engineering ; Thermodynamics ; Computer science High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Electromagnetic Launch and Propulsion Technology
Authors: Angel Albavera-Mata (14289786) | Pawan Prakash (598525) | Jason B. Gibson (20979186) | Eric Fonseca (17703198) | Sijin Ren (3600509) | Xiao-Guang Zhang (605730) | Hai-Ping Cheng (565794) | Michael Shatruk (547894) | S. B. Trickey (6083618) | Richard G. Hennig (1330377)
Source: Figshare (article, 2025-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discovery of Spin-CrossoverMaterials with EquivariantGraph Neural Networks and Relevance-Based Classification Swift discovery of spin-crossover materials for their potential application in electronic and quantum devices requires techniques that enable efficient identification of suitable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolution neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly 4-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening. Artificial neural network ; Equivariant map ; Graph ; Convolution (computer science) ; Identification (biology) ; Graph theory Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Oswaldo Daniel Rodriguez De Luca | Abduljabar Q. Alsayoud | Hussein Aladwan | I.B. Obot
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

AI-driven Inhibitors Discovery of Organic Corrosion Inhibitors, Implementing Experimental and Computational Descriptors Abstract Sour corrosion (SC) presents significant challenges to oil and gas infrastructure, driving the need for efficient and scalable organic-based inhibitors. This study presents an approach to accelerate the discovery of these molecules based on the prediction of the inhibitors' power (IP) through the implementation of machine learning (ML) procedures. A validated dataset has been constructed with 209 entries and was expanded using cheminformatics libraries, such as RDKit, based on the SMILES representation of the inhibitor. Three different machine learning (ML) models were trained and evaluated: Adaptive Boosting (ABDT), Adaptive Support Vector Machine (ASVM), and Catboost (CAT). They were assessed, with CAT emerging as the most effective with a R2test = 0.757, demonstrating a strong capability to predict them. Adaptive Boosting Decision Tree and Adaptive Support Vector highlighting the critical need for expanded datasets and optimized feature selection. These results demonstrate the potential of ML for corrosion inhibitor discovery but also highlight the current challenge in the design of a robust model. Future work will focus on the dataset expansion and incorporation of quantum chemical descriptors and advanced regularization techniques to enhance the predictive accuracy for future applications. Support vector machine ; Decision tree ; Scalability ; Cheminformatics ; Boosting (machine learning) ; Training set ; Artificial neural network ; Random forest Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: A. K. Nair (21780538) | C. M. Da Silva (15470098) | C. H. Amon (15470101)
Source: Figshare (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Predicting InterfacialThermal Conductance and ThermalConductivity across Multilayer TiS<sub>2</sub>/MoS<sub>2</sub> vander Waals Heterostructures Using Moment Tensor Potentials Thermal conductivity calculations using classical molecular dynamics (MD) simulations are significantly influenced by the selection of accurate interatomic potentials. However, these interatomic potentials are not readily available for several novel two-dimensional (2D) materials and their heterostructures. Here, we propose a machine learning interatomic potential (MLIP) and D3-dispersion correction approach to determine the out-of-plane thermal conductivity and interfacial thermal conductance of multilayer TiS<sub>2</sub>/MoS<sub>2</sub> heterostructures. We employ an MLIP-based moment tensor potential (MTP) to characterize the intralayer interactions within layers, while the interlayer interactions are characterized using the D3-dispersion correction. This approach demonstrates greater accuracy compared with Lennard-Jones-based potentials for interlayer interactions. Furthermore, this work uses a simplified supercell of the TiS<sub>2</sub>/MoS<sub>2</sub> heterostructure and cost-effective ab initio molecular dynamics (AIMD) simulations with a duration of less than 1 ps. Additionally, this work investigates the effects of tuning the ratio of TiS<sub>2</sub> to MoS<sub>2</sub> layers on the interfacial thermal conductance and the overall out-of-plane thermal conductivity of the multilayer heterostructure. This MLIP-based methodology provides a reliable approach for estimating the thermal properties of multilayer heterostructures. Thermal conductivity ; Interatomic potential ; van der Waals force ; Supercell ; Molecular dynamics ; Work (physics) ; Tensor (intrinsic definition) ; Thermal Thermal properties of materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Jinan Jiang | Nasrullah Wazir | Han Yan | Zhixin Chen | Zichao Ma
Source:  (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Synaptic Plasticity in MoS <sub>2</sub> Phototransistors Synthesized by Low-Temperature Chemical Vapor Deposition This work demonstrates a photonic synapse based on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{MoS}_{2}$</tex> films synthesized via chemical vapor deposition (CVD) at 450° C. The resulting phototransistor exhibits key synaptic functionalities. When integrated into neural network-based image classification, the device-enabled model achieves a recognition accuracy of 97.08%, approaching the software baseline. Chemical vapor deposition ; Synapse ; Deposition (geology) ; Artificial neural network ; Synaptic plasticity ; Photodiode ; Work (physics) ; Metalorganic vapour phase epitaxy 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Andrey Yachmenev | Emil Vogt | Álvaro Fernández Corral | Yahya Saleh
Source: ArXiv.org (preprint, 2025-06-25)
Field: Chemistry — Spectroscopy

Taylor-mode automatic differentiation for constructing molecular rovibrational Hamiltonian operators We present an automated framework for constructing Taylor series expansions of rovibrational kinetic and potential energy operators for arbitrary molecules, internal coordinate systems, and molecular frame embedding conditions. Expressing operators in a sum-of-products form allows for computationally efficient evaluations of matrix elements in product basis sets. Our approach uses automatic differentiation tools from the Python machine learning ecosystem, particularly the JAX library, to efficiently and accurately generate high-order Taylor expansions of rovibrational operators. The implementation is available at https://github.com/robochimps/vibrojet. Rotational–vibrational spectroscopy ; Taylor series ; Hamiltonian (control theory) ; Automatic differentiation ; Embedding ; Algebra over a field ; Python (programming language) ; Operator theory ; Series expansion Molecular spectroscopy and chirality ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Kamila Kucharska-Ambrożej | Joanna Karpińska | Artur Ratkiewicz
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Performance of Classification Tools for Small Lamiaceae Herb Samples Lamiaceae ; Chemometrics ; Herb ; Medicinal herbs ; Pattern recognition (psychology) ; Training set Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Phytochemistry and Biological Activities
Authors: Academic Journal of Manufacturing Engineering
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-15)
Field: Materials Science — Polymers and Plastics

EMPLOYING ANN AND MLR TO PREDICT FLEXURAL STRENGTH OF CORN COB REINFORCED LOW-DENSITY POLYETHYLENE WASTE COMPOSITES The growing problem of plastic waste worldwide calls for creative methods of material recycling and the production of sustainable composites. The mechanical improvement of low-density polyethylene waste (LDPEw) using maize cob agricultural waste reinforcement is examined in this work, which addresses waste valorization and sustainable material development. The use of FTIR spectroscopy, thermogravimetric analysis (TGA), and differential thermal analysis (DTA) to characterize maize cob filler verified its thermal stability up to 250°C and the existence of functional groups linked to lignin that are necessary for interactions between polymers and fibers. Standardized testing procedures were used to systematically assess the mechanical characteristics of the composites, which were produced via compression molding with corn cob loadings ranging from 0 to 10%. The results of statistical analysis showed that the ideal filler loadings rely on the properties: the greatest impact strength was attained at 2 weight percent (0.4343 ± 0.02 J/mm), the maximum flexural strength was at 4 weight percent (35.94 ± 0.8 MPa), and the maximum hardness peaked at 8 weight percent (53.13 ± 1.2 HV). At 10 weight percent loading, tensile strength showed a notable recovery pattern, first dropping by 51% and then surpassing baseline matrix strength by 21.2% (61.74 ± 1.5 MPa). Artificial neural networks (ANN) and multiple linear regression (MLR) were used in predictive modeling. The results showed that MLR was more accurate than ANN in predicting flexural strength (Mean Absolute Error (MAE) = -0.000167 vs 0.005333). Flexural strength ; Ultimate tensile strength ; Filler (materials) ; Thermogravimetric analysis ; Izod impact strength test ; Compression molding ; Molding (decorative) ; Raw material Natural Fiber Reinforced Composites ; Microplastics and Plastic Pollution ; Fiber-reinforced polymer composites