jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingyuan Dong (11758532) | Fangfang Ding (12910385) | Yanan Jin (4336120) | Chi Li (297996) | Xiang Lin (525346) | Shuang Lin (2438698)
Source: Figshare (article, 2025-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile Synthesisof Ultrasmooth Au Nanospheres intoMacroscopic 3D Supercrystals for Machine-Learning-Driven Analysisof Thiram in Soil Self-assembly of nanocrystals into a macroscopic 3D superlattice has emerged as a promising nanostructure due to its collective optical and electrical properties. In this work, a macroscale supercrystal was constructed under constant temperature and humidity with an ultrasmooth Au nanosphere (NS) as a building block synthesized by a facile route. In particular, a reproducible and facile strategy was developed to fabricate highly spherical Au NSs with nanoscale size tenability. Importantly, this method does not require a syringe pump and breaks the limitation of instrumentation for the synthesis of Au NS, significantly contributing to the promotion and popularization of Au NSs. Meanwhile, an operable and cost-effective droplet slow evaporation self-assembly approach was applied to form a homogeneous 3D superlattice array against coffee rings with an enhancement factor as high as 1.37 × 10<sup>8</sup> verified by FDTD simulation results. Furthermore, integrating a convolutional neural network (CNN) model with the ultrasensitive 3D superlattice SERS platform, the precise and wide-range concentration prediction of the pesticide thiram in soil was successfully demonstrated, breaking the limitation of high-concentration saturation in the conventional concentration–intensity fitting curve. Consequently, this innovative Au NS 3D supercrystal facilitates the fabrication of novel nanoassemblies with ideal plasmonic functions for extensive applications in the fields of food safety, environment, medicine, and biology. Nanostructure ; Nanoscopic scale ; Evaporation ; Fabrication ; Nanoparticle ; Thiram ; Nanomaterials Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials and Printing Technologies ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Bahrem Serhat Danis | Esra Zayim
Source: arXiv (Cornell University) (preprint, 2025-07-15)
Field: Materials Science — Materials Chemistry

TMMax: High-performance modeling of multilayer thin-film structures using transfer matrix method with JAX Optical multilayer thin-films are fundamental components that enable the precise control of reflectance, transmittance, and phase shift in the design of photonic systems. Rapid and accessible simulation of these structures holds critical importance for designing and analyzing complex coatings. While researchers commonly use the traditional transfer matrix method for designing these structures, its scalar approach to wavelength and angle of incidence causes redundant recalculations and inefficiencies in large-scale simulations. Furthermore, traditional method implementations do not support automatic differentiation, which limits their applicability in gradient-based inverse design approaches. Here, we present TMMax, a Python library that fully vectorizes and accelerates transfer matrix method using the high-performance machine learning library JAX. TMMax supports CPU, GPU, and TPU hardware, and includes a publicly available material database. Our approach, demonstrated through benchmarking, allows us to model thin-film stacks with hundreds of layers within seconds. This illustrates that our method achieves a simulation speedup of x100s over a baseline NumPy implementation, enabling optical engineers and thin-film researchers in optics and photonics to efficiently design complex dielectric multilayer structures through rapid and scalable simulations. Python (programming language) ; Photonics ; Scalability ; Transfer matrix ; Dielectric ; Inverse ; Implementation ; Matrix method ; Speedup Solidification and crystal growth phenomena
Authors: Koushik Pradhan | Soumya Ghorai | Prabuddha Sanyal | Ryan Morrow | B. Büchner | Thirupathaiah Setti | Tanusri Saha Dasgupta
Source: ArXiv.org (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

Unusual Valence of Ru and Prediction of Magnetism, Anomalous Hall Conductivity in a Newly Synthesized Double Perovskite Compound Ca_2CoRuO_6 With a goal to expand on the family of double perovskite compounds, hosting 3d transition metal and 4d or 5d transition metal, two new ordered double perovskite compounds, Ca$_2$FeRuO$_6$ and Ca$_2$CoRuO$_6$ are synthesized following the prediction of a recent high throughput machine-learning study [Phys. Rev. Materials 3, 084418]. Experimentally both compounds are found to stabilize in monoclinic symmetry, which is consistent with the high-throughput prediction for Ca$_2$FeRuO$_6$, but at odd for Ca$_2$CoRuO$_6$. Among the two synthesized compounds, the properties of Ca$_2$CoRuO$_6$, investigated employing the first principles technique and model Hamiltonian calculation, appear promising. The monoclinic structured Ca$_2$CoRuO$_6$ is found to stabilize unusual 6+ valence of Ru, and support a half-metallic ground state with uncompensated net moment. As predicted by our first-principles study, the finite spin-orbit coupling at the Ru site contributes to the non-trivial topology of the band structure of monoclinic Ca$_2$CoRuO$_6$, resulting in a moderately large value of anomalous Hall conductivity. Our theoretical predictions should encourage further experimental investigation of this newly synthesized compound. Monoclinic crystal system ; Valence (chemistry) ; Perovskite (structure) ; Ground state ; Conductivity ; Transition metal ; Hamiltonian (control theory) Machine Learning in Materials Science ; Chemical and Physical Properties of Materials ; Advanced Condensed Matter Physics
Authors: Norma Rivano | Francesco Libbi | Chuin Wei Tan | Christopher T. S. Cheung | José L. Lado | Arash A. Mostofi | Philip Kim | Johannes Lischner | Adolfo O. Fumega | Boris Kozinsky | Zachary A. H. Goodwin
Source: ArXiv.org (preprint, 2025-04-18)
Field: Materials Science — Materials Chemistry

Exploring Charge Density Waves in two-dimensional NbSe2 with Machine Learning Niobium diselenide (NbSe$_2$) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs-capturing effects such as layer number, twist angle, and strain-remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe$_2$.We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe$_2$ and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design. Curse of dimensionality ; Workflow ; Charge density wave ; Lattice (music) ; MNIST database ; Superconductivity ; Hysteresis 2D Materials and Applications ; Machine Learning in Materials Science ; Organic and Molecular Conductors Research
Authors: Thanasee Thanasarnsurapong (14076226) | Sourav Kanti Jana (5237036) | Panyalak Detrattanawichai (21606520) | Waraporn Namunmong (21606523) | Wisit Hirunpinyopas (4515892) | Pawin Iamprasertkun (3369521) | Adisak Boonchun (14076232)
Source: Figshare (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

AcceleratingLattice Thermal Conductivity Calculationsin MXenes: A Machine Learning Force Field Approach Traditionally, lattice thermal conductivity is evaluated using the phonon Boltzmann transport equation (PBTE) in combination with density functional theory (DFT) calculations. However, this approach is computationally intensive. In this study, we predicted lattice thermal conductivity of Ti<sub>2</sub>C and Ti<sub>3</sub>C<sub>2</sub> MXenes, along with their functionalized variants featuring surface terminations (O, F, OH), by using active DFT-based on-the-fly machine learning force fields (MLFF). The predicted thermal conductivities of Ti<sub>2</sub>C and Ti<sub>3</sub>C<sub>2</sub> are 73.10 and 101.15 W m<sup>–1</sup> K<sup>–1</sup>, respectively, close to previously calculated DFT values. The introduction of surface functional groups significantly reduces the lattice thermal conductivity. Furthermore, the MLFF-based predictions of lattice thermal conductivity are tens to thousands of times faster than conventional DFT calculations, dramatically accelerating the study of thermal transport in MXenes. This efficiency highlights the potential of MLFF as a powerful tool for exploring and optimizing the thermal properties of two-dimensional materials. Thermal conductivity ; Lattice (music) ; Phonon ; Lattice Boltzmann methods ; Boltzmann equation ; Thermal Thermal properties of materials ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Christiono Roesdiono | Iwa Mulyana Kartadinata Garniwa | Rudy Setiabudy
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Review of Dielectric Properties Optimization of RTV Silicone Rubber Using Coal Fly Ash Filler with the Backpropagation Neural Network-Genetic Algorithm Method Fly ash ; Backpropagation ; Vulcanization ; Dielectric ; Artificial neural network ; Filler (materials) ; Dielectric strength High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Source: Journal of Physics Conference Series (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Preface The 6th International Conference on Mechanical Instrumentation and Automation (ICMIA 2025), held from March 21 to 23, 2025, in Hangzhou, China, marked another milestone in advancing interdisciplinary research and industrial applications within the fields of mechanical engineering, instrumentation, and automation. As the world continues to embrace rapid technological evolution, ICMIA 2025 served as a pivotal platform for scholars, engineers, and industry leaders to exchange cutting-edge ideas, present groundbreaking innovations, and foster collaborations aimed at addressing global challenges. This proceedings volume encapsulates the intellectual vibrancy and collaborative spirit that defined the event, offering a comprehensive overview of the latest advancements and future directions in the discipline. The keynote speeches set the tone for the conference by addressing transformative technologies and methodologies. Prof. Yufei Gao (Shandong University) opened the discourse with his presentation on Detection Model of Electroplated Diamond Wire Surface Abrasive Distribution Characteristics based on YOLO-EDW. By integrating computer vision and deep learning, his work offers a novel approach to optimizing abrasive tool performance, with profound implications for precision manufacturing and semiconductor industries. Prof. Jiang Guo (Dalian University of Technology) followed with a compelling exploration of Surface Treatment Technology and Applications for Interface Bonding Performance Oriented Multilayer Additive Hot-Compression Bonding of Large Forgings. His research bridges the gap between material science and industrial applications, proposing scalable solutions for enhancing the durability and reliability of heavy machinery components. Concluding the keynote series, Assoc. Prof. Teng Wang (Northwestern Polytechnical University) presented a pioneering Data-Model-Driven Method for Condition Monitoring of Key Equipment and Its Application in Digital Twin Systems. By merging real-time sensor data with predictive analytics, his framework advances the development of smart maintenance systems, offering a blueprint for Industry 4.0 implementation. The agenda was meticulously structured to maximize engagement and knowledge dissemination. The Opening Ceremony underscored the conference’s mission through remarks by general chair and a panel discussion on global trends in automation. Subsequent Oral Presentation Sessions spanned topics such as robotics, sensor technologies, and sustainable manufacturing, with presenters showcasing empirical studies and theoretical breakthroughs. A dedicated Award Ceremony honored exceptional papers selected through rigorous peer review, highlighting contributions to AI-driven predictive maintenance and energy-efficient system design. List of Committee Member is available in this Pdf. Psychology ; Computer science Machine Learning in Materials Science ; Digital Transformation in Industry ; Engineering and Materials Science Studies
Authors: Yan Xie | Wei Li | Jin Wen
Source: Communications in Computational Chemistry (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields for Predicting Thermodynamic Properties of PA6T/6I Copolymers This study employed molecular dynamics (MD) simulations, utilizing both a machine learning force field (MACE-OFF) and a traditional force field (PCFF), to predict the thermal properties of poly(hexamethylene terephthalamide-co-isophthalamide) (PA6T/6I) copolymers. The simulations are benchmarked against experimental data to assess the predictive accuracy of these two methodologies for the thermal properties of PA6T/6I copolymers. Our findings reveal that the MACE-OFF force field, after calibration for the PA6T/6I copolymer, offers significant precision in modeling π-π and hydrogen-bonding interactions, closely mirroring the results from M06 functional simulations. The MD simulations underscore the MACE-OFF model&amp;#39;s ability to deliver more stable thermal properties, including the glass transition temperature (Tg) and density, for copolymer systems with varying PA6T content, aligning well with experimental observations. Furthermore, a comprehensive analysis of dynamic properties, such as mean squared displacement and free volume, within the PA6T/6I copolymers was performed to decipher the mechanisms underlying the temperature-dependent changes in thermal properties observed throughout the simulation process. A thorough examination of the fluctuations in inter-chain and intra-chain hydrogen bonding within the copolymer systems has unveiled the correlation between the molecular packing arrangement and thermal properties. This research establishes that the MACE-OFF model accurately simulates the thermal dynamical behavior of PA6T/6I copolymers, a capability that could be extended to other polyamide systems. Copolymer ; Materials science ; Thermodynamics ; Polymer ; Physics ; Composite material Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Fuel Cells and Related Materials
Authors: Asif Khan | Shihab Tanvir Sadnan | Mahbubul Muttakin | Fazlar Rahman | M. Rahman | Md. Kharshiduzzaman | Md. Shahnewaz Bhuiyan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Strain-Rate-Dependent Optimization of Alkali Treatment in Sugarcane Bagasse Fibers Using Ensemble Learning and Genetic Algorithms Bagasse ; Strain (injury) ; Alkali metal ; Genetic algorithm ; Mathematical optimization ; Computer science ; Algorithm ; Materials science ; Mathematics ; Chemistry ; Biotechnology ; Biology ; Organic chemistry Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry
Authors: M. S. Elkhalafy | M. A. Abd-Allah | M. Elgabalawy | Abdelrahman Said
Source:  (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Optimization of Solid Oxide Electrolysis Cell for Hydrogen Production Powered by Small Modular Reactors using Artificial Intelligent The solid oxide electrolysis cell (SOEC) Stands out as highly efficient way to convert thermal and electrical power into hydrogen. However, typical modeling approaches are limited to a specific or reported SOEC system. In this research, a hybrid Artificial intelligent framework is developed to optimize and predict the SOEC performance system powered by Small Modular Reactors (SMRs). Therefore, the artificial intelligence models, Artificial Neural Network (ANN), Support vector regression (SVR), and Random Forest (RF) are used to predict essential outputs including resistance, current density, and hydrogen rate of production. Deployment of sensitivity analysis and partial dependency plot to explain the influence of these features on the SOEC’s outputs. The combination between the preferred model and a practical Swarm algorithm (PSO) are utilized to determinethe optimal values of all input. Findings present that the boosted ANN technique is the Key of the AI model ofthe process because it has the highest $\mathbf{R}^{2}$ (> 0.93) in the three targets. The descriptors of electrolytic cell have a higher influence on the system performance, contributing up to 50.9%. The voltage,temperature, and effective area are the three most effective factors in the SOEC process, providing 17.8%, 12.0%, and 20.4%, to its performance. High pressure, high temperature, and low effective area are the most favorable conditions for $\mathbf{H}_{2}$ production rate. After developing an optimization with multiobjective, the optimal current density and rate of hydrogen production were obtained to be $\mathbf{1. 4 2 2 ~ A /} \mathrm{cm}^{2}$ and $\mathbf{1. 7 1} \mathbf{L} \mathbf{h} \mathbf{c m}^{\mathbf{2}}$ Modular design ; Hydrogen production ; Artificial neural network ; Electrolysis ; Electrolytic cell ; Process (computing) ; Particle swarm optimization ; Production (economics) ; Electrolytic process Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Hybrid Renewable Energy Systems
Authors: Dawei Sun | Li Li | Cui Han | Chengquan Zhou | Chen Li | Hongbao Ye
Source: Current Research in Food Science (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Near-infrared hyperspectral imaging combined with elemental and isotopic analyses reflects geographical origins of cultured salmonids The geographical tracing of high-value salmonids is essential in mitigating food fraud and ensuring authenticity. This study combined hyperspectral imaging with elemental/isotopic profiling to reflect geographical origin information of salmonids and improve the explainability of discrimination models. Pearson correlation analysis was conducted using spectra and elements/isotopes for spectral fingerprinting, followed by developing machine/deep learning models with full spectra and extracted spectral features, respectively. The key isotopes and elements were further predicted using random forest modeling. The results indicated elements and isotopes highly correlating with spectral regions around 900, 1000, and 1200 nm, reflecting important spectral profiling of geographical origins for fresh salmonid products. Further, all discrimination models achieved acceptable overall accuracies (>65 %), with SFS-ELM achieved the highest precision (88.13 %). Key elements/isotopes were satisfactorily predicted (R<sup>2</sup> > 0.70) with chemical maps for vivid visualization. This study combined HSI with elemental/isotopic analysis to provide a chemometric basis for interpreting spectral regions that are most relevant to origin discrimination, improving the comprehensive understanding of model interpretability and feature validation. Hyperspectral imaging ; Interpretability ; Tracing ; Spectral analysis ; Spectral signature ; Random forest ; Profiling (computer programming) ; Stable isotope ratio Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Identification and Quantification in Food
Authors: Siti Fatimah | Sarto | Moh. Fahrurrozi | Budhijanto | Wiharto
Source:  (article, 2025-10-09)
Field: Materials Science — Biomaterials

A Machine Learning Approach to Predict the Characteristics of Hybridfibers Made of GC-Glu Through Electrospinning Process Hybrid fiber synthesis based on gelatin-chitosan (GC) cross-linked with glucose (Glu) was successfully carried out through a coaxial electrospinning process. This study aims to develop a machine learning (ML) approach to predict the mechanical characteristics of the resulting hybrid fibers, particularly the Young's Modulus, in order to optimize process parameters and reduce experimental effort. A total of 25 datasets were obtained from laboratory experiments under varying operating conditions, including glucose composition, chitosan composition, voltage, conductivity, and viscosity. The collected data were modeled using Random Forest Regression (RFR) and Multiple Linear Regression (MLR), with their predictive performances evaluated using MAPE, MSE, and accuracy metrics. The results show that RFR and MLR achieved MAPE values of 5.64% and 5.80%, and MSE values of 0.035 and 0.040, respectively, corresponding to prediction accuracies of 94.36% and 94.20%. These findings demonstrate that ML can effectively model the electrospinning process, providing accurate predictions that support more efficient optimization, reduce experimental costs and time, and offer practical implications for scaling up hybrid fiber production for potential medical textile applications. Both models can be recommended to provide effective and efficient predictions of coaxial electrospinning process operating conditions. Electrospinning ; Linear regression ; Process (computing) ; Support vector machine ; Fiber ; Random forest ; Coaxial ; Regression Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies
Authors: Bernhard Kretz (4146655) | Ivor Lončarić (4269022)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Tuning ElectronicProperties of Nanoporous Graphene Different nanoporous graphene structures have shown great promise for a wide variety of applications. However, due to limitations in experimental or computational throughput, nanoporous graphenes have not been investigated systematically. In this work, we combine density functional theory and machine learning to study 460 structures of nanoporous graphene made from four different templates. We shed light on structure-band gap relations and perform molecular dynamics simulations and phonon calculations in order to determine the role of electron–phonon coupling on the renormalization of temperature-dependent band gaps. Our results uncover that certain subsets of nanoporous graphene exhibit a similar trend in the band gap as a function of a structural parameter as has been observed for armchair graphene nanoribbons. Furthermore, we find that electron–phonon coupling varies over a large range in the investigated nanoporous graphenes and that it drives the closing of the band gap with larger temperatures. Finally, we suggest nanoporous graphene structures for different applications, such as field-effect transistors. Thus, our work can help guide the development and improvement of nanoporous graphene-based devices. Nanoporous ; Graphene ; Band gap ; Coupling (piping) ; Renormalization ; Density functional theory Graphene research and applications ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis