jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pei-Sze Tan | Karen Jia-Jun Koh | Sailaja Rajanala | Arghya Pal | Raphaël C.-W. Phan | Nan Ze | Fuad Noman | Chee-Ming Ting | Norfadilah Dolmat | Nik Nur Wahidah Nik Hashim | A Tumian
Source:  (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Res-SH: Unbiased Residual Learning for Self-Healing Interface Toughness Prediction with Limited Data The development of self-healing materials is often hindered by the high costs and material waste associated with traditional characterization methods. Current approaches to toughness prediction, primarily based on convolutional neural networks (CNNs), are limited by their tendency to capture only surface-level features, which can lead to biased predictions. Moreover, working with small datasets, which is common in materials science, further increases the risk of biased training due to overfitting, posing a critical challenge to the reliability and generalizability of predictive models. This study introduces an unbiased residual learning framework designed explicitly for predicting self-healing interface toughness under limiteddata conditions. Our approach, ResNet-inspired approach for predicting self-healing material toughness, named Res-SH, used the power of residual networks to capture deeper, more complex patterns in the data, thereby addressing critical challenges in materials research. Res-SH minimises resource consumption and experimental overhead by focusing on unbiased learning, achieving accurate predictions with fewer training epochs and lower R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score and root mean square prediction errors compared to conventional CNN and lightweight model MobileNetv2. This novel framework provides a cost-effective and resource-efficient alternative to traditional material characterization methods, reducing material waste and accelerating the discovery and optimization of self-healing material systems. Residual ; Reliability (semiconductor) ; Generalizability theory ; Toughness ; Artificial neural network ; Convolutional neural network ; Overhead (engineering) Machine Learning in Materials Science ; 2D Materials and Applications ; Fiber-reinforced polymer composites
Authors: H. F. Feng | B. Liu | Xin-Gao Gong | Zhi‐Xin Guo
Source: Physical review. B./Physical review. B (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Exceptional four-phonon scattering effects on low-temperature thermal conductivity in two-dimensional materials First-principles-based predictions of lattice thermal conductivity (TC) from perturbation theory have achieved significant success. Usually, it only includes three-phonon (3ph) scattering processes; only recently, four-phonon (4ph) scattering processes have been found to have an impact that is comparable to 3ph scattering at medium and high temperatures in various materials, while the influence of 4ph scattering on TC at low temperatures is generally believed to be insignificant. By combining the first-principles calculations, machine-learning techniques, and Boltzmann transport equation, we find that there are unusually strong 4ph processes even in the low-frequency range of two-dimensional (2D) materials such as h-$X\mathrm{N}$ ($X=\mathrm{B}$, Al, Ga), which have a remarkable influence on the low-temperature TC. Such strong 4ph processes originate from the out-of-plane acoustic (ZA) phonon mode of 2D materials. Furthermore, we find that the intensity of 4ph scattering and thus TC can be effectively manipulated by changing the dispersion of the ZA phonon mode, which can be easily achieved through strain engineering. The present study provides distinct insights into low-temperature phonon transport and its manipulation in 2D materials. Scattering ; Phonon ; Thermal conductivity ; Phonon scattering ; Boltzmann constant ; Thermal ; Conductivity ; Dispersion (optics) Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Mohammadfarid Alvansazyazdi | Brisa Samantha Dolor Bailón | Gilbert Emmanuel Palacios Santos | Nelson Salgado–Reyes
Source: Alpha International Journal (article, 2025-08-05)
Field: Materials Science — Polymers and Plastics

Comparative review of sisal fiber and cellulose nanofiber reinforced concrete Objective: The performance of sisal fibers and cellulose nanofibrils (CNFs) as complementary reinforcements in concrete was analyzed, evaluating their benefits in strength, durability, and sustainability, and incorporating the use of artificial intelligence (AI) for mixture prediction and optimization. Methodology: A comparative study was conducted based on the review and synthesis of previous research, focusing on the effect of sisal at the macroscopic scale and of cellulose nanofibrils (CNFs) on the microstructure of the cementitious matrix. Experimental data on strength, microstructural behavior, and environmental performance were considered, integrating predictive models thru machine learning. Results: Sisal increased compressive and flexural strength by up to 29.69% and 34.4%, respectively, provided its dosage was controlled and treatments were applied to improve adhesion and stability in alkaline environments. NFCs promoted densification of the interfacial transition zone, reducing porosity and chloride diffusion by 27.9%, with positive effects on durability and microcrack sealing. In environmental terms, sisal offered advantages due to its low cost and renewable origin, while NFCs, although entailing a greater manufacturing impact, contributed to optimizing structural efficiency and extending the service life of the concrete. Conclusions: The combination of sisal and NFC showed complementary potential to improve the mechanical performance and durability of concrete, offering solutions aligned with sustainability criteria. The use of AI facilitated the optimization of mixtures, enabling more accurate prediction of their behavior and reducing the need for extensive testing. SISAL ; Nanofiber ; Cellulose ; Composite material ; Materials science ; Fiber ; Engineering ; Chemical engineering Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Advanced Cellulose Research Studies
Authors: Sridatri Nandy (11140619) | K. V. Jovan Jose (9728324)
Source: Figshare (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Geometric GuidanceIntegrated with Directed ElectrostaticsStrategy within a Graph Neural Network Approach for Nanocluster StructurePrediction We introduce the Geometric-DESIGNN method, which integrates Geometric Guidance with Directed Electrostatics Strategy within a Graph Neural Network framework to predict the stable configuration of nanoclusters on their potential energy surfaces. This approach merges the geometric and electronic strategies using graph neural network-based models to predict structures of large atomic clusters with specific size and point-group symmetries. This approach aids in constructing atomic metal cluster structures by predicting building frames through a geometric approach and locating the minima in the molecular electrostatic potential (MESP) landscape. By following alternate geometric and DESIGNN building strategies for each shell of parent clusters, we efficiently achieve close-packed daughter structures along their evolutionary paths. The geometric-DESIGNN approach is validated on the prototype Mg<sub><i>n</i></sub> clusters, by building structures for sizes up to <i>n</i> < 561. Furthermore, constraining the point-group symmetry of the parent clusters, we identify new symmetric isomers of medium to large Mg<sub><i>n</i></sub> clusters with <i>n</i> < 150. This methodology is also employed to construct stable Mg<sub><i>n</i></sub> nanoclusters with <i>n</i> = 332, 338, and 561. Benchmarking results show that the geometric-DESIGNN approach is an efficient tool for accelerated prediction of the nanocluster structure. Maxima and minima ; Nanoclusters ; Artificial neural network ; Benchmarking ; Electrostatics ; Graph ; Cluster (spacecraft) ; Construct (python library) ; Geometric design Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Graph Neural Networks
Authors: Xin Xu | Ting Ying | Zhanghuan Yang | Ze Zhang | Liang Hu | Tong Shen | Dejian Li | Rui Zhu | Dong Chang
Source: Talanta (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

A Mn–O–Ce interface-engineered nanozyme platform for visual stratified exosome screening of lung cancer: Toward primary care application Chemistry ; Cancer ; Cancer research ; Nanotechnology ; Internal medicine Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Weisong Shi | Jiaying Chen | Kai Huang | Jian Mao | K. L. Li | Taikang Liu
Source: Computational Materials Science (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Graph embedding oversampling for crystal material properties in imbalanced regression Regression ; Embedding ; Graph ; Artificial neural network ; Linear regression ; Oversampling ; Pattern recognition (psychology) ; Regression analysis ; Material properties Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mineral Processing and Grinding
Authors: Xia Liang
Source: Spiral (Imperial College London) (article, 2025-03-28)
Field: Materials Science — Materials Chemistry

Structural dynamics of hybrid lead halide perovskites with machine learning In this thesis, I present a unified computational strategy for investigating the structure and dynamics of lead halide perovskites, which remain at the forefront of research for next-generation solar cells and optoelectronic devices. The methods combine on-the-fly force field training connected to machine learning (ML) architectures such as the Gaussian Approximation Potential, Allegro, and MACE, with an automated Python workflow. My code PDynA was built to efficiently quantify perovskite structural descriptors (e.g., octahedral tilting, distortion modes, and molecular orientations). This integrated approach is applied to both simple, pure compositions and more intricate mixed perovskites, where varying A-site cations and halide ratios drive multiple phase transformations and dynamic effects. Halide ; Perovskite (structure) ; Python (programming language) ; Lead (geology) ; Molecular dynamics ; Support vector machine Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: V Revathi | K Aravinda | Vijilius Helena Raj | P. Rajesh
Source: International Journal of Computational Methods (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Evaluating Microstructural Characteristics of Aluminum–Silicon Carbide Nanomixtures: Application of Spatio-Temporal Graph Convolutional Neural Networks for Enhanced Analysis Nanomixes of aluminum and silicon carbide (Al–SiC) are sophisticated composite materials with better mechanical qualities, such as increased stiffness and strength. The microstructural properties of an aluminum matrix, such as grain size, phase distribution, and interfacial bonding, can be greatly influenced by the addition of silicon carbide nanoparticles. Achieving homogeneous dispersion of nanoparticles and preserving robust interface bonding while striking a balance between mechanical strength and ductility present challenges. This paper proposes a hybrid approach for evaluating the microstructural characteristics of Aluminum–Silicon Carbide Nanomixtures. The proposed hybrid method combines a Spatio-Temporal Graph Convolutional Neural Network (STGCNN) and Ali Baba and Forty Thieves Optimization (AFTO) and is usually referred to as the AFTO-STGCNN method. The main objective of the proposed method indicates the smallest aluminum matrix crystallite size and the maximum lattice strain can be achieved by modifying the process parameters. The AFTO approach is utilized to optimize the process parameters producing the minimum crystallite size and the maximum lattice strain of the Al matrix. The STGCNN technique predicts the characteristics of the Al/SiC nanocomposite. The proposed AFTO-STGCNN technique runs in the MATLAB platform and is compared to perform with the various existing methods. By this, the proposed technique achieves an error of 0.1%. But, the existing techniques, like Artificial Neural Network (ANN), Growth Optimizer Algorithm (GOA), and Multi-objective Grasshopper Optimization Algorithm (MOGOA) attain the error of 0.3%, 0.2%, and 0.6%, respectively. Finally, this work demonstrates that the proposed strategy for minimizing error and enhancing performance in evaluating the microstructural properties of aluminum–silicon carbide nanomixtures is feasible. Silicon carbide ; Materials science ; Convolutional neural network ; Nano- ; Aluminium ; Graph ; Silicon ; Computer science ; Metallurgy ; Composite material ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science
Authors: Zhihan Jin | Tianhong Chen | Hao Liu | Jie Yan | Ting C. Huang | Chee Leong Tan | Xiang Wan | Huabin Sun | Yi Shi | Shancheng Yan
Source: Optics Express (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Visual information enhancement encoder based on exciton regulation The strong Coulomb response in two-dimensional transition metal dichalcogenides (TMDCs) leads to the emergence of firmly bound excitons and charged excitons, and their transformation is the key to studying and utilizing the optical and photoelectric properties of two-dimensional materials. Especially in the study of optoelectronic applications, they are beneficial in many valley electron and energy transfer applications. However, their potential for new information processing, such as the combination of visual information and neural networks, remains largely untapped. Here, we study the photoluminescence (PL) properties and exciton behavior of monolayer WS<sub>2</sub>. We achieve a wide range of exciton-trion modulation by changing the substrate and pump intensity, extending the dynamic range of visual information processing. At room temperature, the I(X<sup>-</sup>)/I(X<sup>0</sup>) ratio of CaF<sub>2</sub>/WS<sub>2</sub> samples pumped at high intensity is improved by an order of magnitude, and the three-particle conversion rate can reach 90%. Then, we combined the non-linear response of the WS<sub>2</sub>/CaF<sub>2</sub> device and the linear response of the WS<sub>2</sub>/SiO<sub>2</sub> device to re-encode the data source through the multilayer neural network algorithm, and the recognition accuracy reached 98.6%, demonstrating the potential of the device in advanced vision technology. Our work provides a new path for future 2D exciton device design. Optics ; Encoder ; Exciton ; Physics ; Materials science ; Computer science 2D Materials and Applications ; Perovskite Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Ziyang Wang | Bingqi Linghu | Donghao Li | Jie Zhu | Dawei Tang
Source: International Communications in Heat and Mass Transfer (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Machine learning-driven data analysis for three-layer time-domain thermoreflectance measurements Interpretability ; Thermal conductivity ; Microscale chemistry ; Characterization (materials science) ; Thermal ; Thermal resistance ; Visualization ; SIGNAL (programming language) Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Machine Learning in Materials Science
Authors: Mohamad Hossein Nargesi | Kamran Kheiralipour | jafar amiriparian
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Wheat, Chickpea, and Sea Foam in Black Pepper Using Hyperspectral Imaging Technique Pepper ; Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Wheat flour ; Artificial intelligence ; Mathematics ; Computer science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Jefferson Tales Oliva | Vinicius Herique Kieling | Felipe Augusto Bueno Rossi | Érick Oliveira Rodrigues | Giovanni A. Guarneri | Larissa Macedo dos Santos
Source: Journal of Chemometrics (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Comparison Between Portable and Bench‐Top Near‐Infrared Spectroscopy for Corn Silage Characterization Using Partial Least Square and Support Vector Regression Methods ABSTRACT In this work, bench‐top and portable near‐infrared (NIR) spectroscopy equipment are compared in the context of generating predictive models for the quantification of phosphorus (P), potassium (K), and nitrogen (N) components from corn silage samples. For this, 200 spectral samples resulting from bench‐top and portable NIR are preprocessed by the following sequence of approaches: mean centering application for removing the spectral bias/offset, Savitzky–Golay filter for highlighting signal energy absorption in relation to spectral noise, interval partial least square (iPLS) for selection of spectral region, and Monte Carlo method for outlier detection and removal. Then, from the preprocessed spectra, predictive models were built using the partial least squares (PLS) and support vector regression (SVR) methods for each chemical component and NIR equipment. In this sense, six models are generated, three for each NIR spectroscopy (or two for each element). As a result, considering all components and machine learning (ML) methods, bench‐top models achieved R 2 values between 0.66 (quantification of P using PLS or SVR) and 0.81 (prediction of K and N using SVR regressors) during the validation, whereas portable ones achieved values between 0.50 (prediction of K using SVR) and 0.67 (quantification of N using PLS). Our results can be considered competitive, as robust and accurate predictors are also generated. Partial least squares regression ; Context (archaeology) ; Support vector machine ; Spectroscopy ; Mean squared error ; Analytical Chemistry (journal) ; Monte Carlo method ; Filter (signal processing) ; Chemometrics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Soil Geostatistics and Mapping
Authors: Varughese, Bilvin | Loeffler, Troy D. | Banik, Suvo | Koneru, Aditya | Manna, Sukriti | Balasubramanian, Karthik | Batra, Rohit | Cherukara, Mathew J. | Yildiz, Orcun | Peterka, Tom | Sumpter, Bobby G. | Sankaranarayanan, Subramanian K. R. S.
Source: ArXiv.org (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton-Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties - lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales. Symbolic regression ; Artificial neural network ; Monte Carlo method ; Interatomic potential ; Transformation (genetics) ; Lattice (music) ; Regression ; Function (biology) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Boyuan Zhang | Zhenliang He | Meina Kan | Shiguang Shan
Source:  (article, 2025-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Precise Integral in NeRFs: Overcoming the Approximation Errors of Numerical Quadrature Neural Radiance Fields (NeRFs) use neural networks to translate spatial coordinates to corresponding volume density and directional radiance, enabling realistic novel view synthesis through volume rendering. Rendering new viewpoints involves computing volume rendering integrals along rays, usually approximated by numerical quadrature because of lacking closed-form solutions. In this paper, utilizing Taylor expansion, we demonstrate that numerical quadrature causes inevitable approximation error in NeRF integrals due to ignoring the parameter associated with the Lagrange remainder. To mitigate the approximation error, we propose a novel neural field with segment representation as input to implicitly model the remainder parameter. In theory, our proposed method is proven to possess the potential to achieve fully precise rendering integral, as demonstrated by comprehensive experiments on several commonly used datasets with state-of-the-art results. Quadrature (astronomy) ; Numerical integration ; Applied mathematics ; Mathematics ; Mathematical analysis ; Physics ; Optics Magnetic Properties and Applications ; Electromagnetic Scattering and Analysis ; Non-Destructive Testing Techniques
Authors: Wiebke Zielasko | R. B. Oliveira | Edgar Dutra Zanotto | Michael Ryan Hansen | Hellmut Eckert | Henrik Bradtmüller
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Contrasting Structural Roles of Nb2O5 vs. La2O in Silicate Glasses: Structural Studies by Solid-State NMR and Raman Spectroscopy Raman spectroscopy ; Solid-state nuclear magnetic resonance ; Silicate ; Silicate glass ; Materials science ; Solid-state ; Nuclear magnetic resonance spectroscopy ; Spectroscopy ; Nuclear magnetic resonance ; Chemistry ; Physical chemistry ; Physics ; Optics ; Organic chemistry ; Composite material Glass properties and applications ; Zeolite Catalysis and Synthesis ; Luminescence Properties of Advanced Materials