jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongling Guo | Yuan Sh | Yinghua Feng | Yiting Guo | Xiuli Zhang | Ping Wang | Can Hu | Hongcheng Mei | Yajun Li | Jun Zhu
Source: Scientific Reports (article, 2026-04-03)
Field: Chemistry — Analytical Chemistry

A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic application Smokeless powder is the primary propellant in civilian and military ammunition, and in China, the use of propellants to make homemade ammunition and bombs is an emerging criminal practice. The identification and differentiation of the propellants used can provide forensic information about their sources. Depending upon the ammunition manufacturer and type, the recipe of propellants varies, and the characterization of smokeless powders in terms of their spectral components is useful for differentiating propellants. In this work, near-infrared spectroscopy (NIR) and chemometric modeling were used to explore the feasibility of differentiating and predicting smokeless powders from different sources. By comparison, the proposed neural network model in the study exhibited an average accuracy of over 80%. Furthermore, the potential for differentiating smokeless powders was well demonstrated via simple and rapid near-infrared spectroscopic analysis, and the employment of chemical agents and time-consuming chromatography and mass spectrometry could thereby be avoided. Propellant ; Ammunition ; Chemometrics ; Mass spectrometry ; Spectroscopy ; Characterization (materials science) Spectroscopy and Chemometric Analyses ; Mass Spectrometry Techniques and Applications ; Pharmaceutical Quality and Counterfeiting
Authors: O. P. Tulupova | G. N. Zholobova
Source: Digital Solutions and Artificial Intelligence Technologies (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression for Product Geometry Prediction in CAE Modeling Superplastic forming is an advanced technology used in the aerospace and automotive industries, as well as in the medical sector, for fabricating complex seamless components. However, its application is limited by high costs and the extended duration of the process. While finite element analysis in CAE systems such as ANSYS provides accurate results, it is computationally expensive. While finite element analysis performed in CAE systems such as ANSYS provides high-fidelity results, its computational expense creates a need for fast and accurate predictive models capable of supplementing or replacing this approach in multi-criteria analysis tasks. Despite the increasing adoption of machine learning across various disciplines, the development of reliable predictive models for specific geometric characteristics of superplastically formed components remains an understudied research area. The purpose of this study is to develop and verify a Gaussian process based model for predicting key geometric parameters of a hemisphere during the superplastic forming. An additional objective was to create an initial dataset using data generated from numerical simulations. The Latin Hypercube Sampling method was employed to design the experiment and generate the initial dataset, enabling efficient variation of material parameters K, m and pressure regime within ranges typical for aluminum alloys. Based on data from 50 numerical simulations, a predictive model for the hemisphere’s geometric characteristics was developed with Gaussian Process Regression with a composite kernel. Model hyperparameter optimization was performed using RandomizedSearchCV. The developed Gaussian Process Regression model demonstrated high accuracy, achieving a coefficient of determination greater than 0.90 on the validation set for all target variables: thickness at the pole, average height, and height difference. Analysis of the Mean Squared Error confirmed the models generalization capability and absence of overfitting. T his research is aimed at integrating the model into a digital twin system for real-time optimization of process parameters. The main challenge in scaling this approach is the computational cost associated with generating the required training data. Latin hypercube sampling ; Gaussian process ; Finite element method ; Kriging ; Regression analysis ; Process (computing) ; Hyperparameter ; Gaussian ; Linear regression Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Advanced Multi-Objective Optimization Algorithms
Authors: Mu-Chun Su | Jieh-Haur Chen | Wei-Jen Lin | Tzu-yangT Yu | Yu-Jen Chiang
Source: Journal of Global Governance and Sustainability (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Automation and Sustainability: Developing coating thickness detection algorithm for construction H-shaped steel components To enhance the longevity and sustainability of structural materials, H-shaped steel coated with specialized protective layers has emerged as a superior solution due to its high resistance to rust and fire. However, ensuring the optimal performance and lifecycle efficiency of such materials relies heavily on the precise, automated assessment of coating thickness—an area where traditional detection methods remain error-prone and inefficient. This study addresses the need for a sustainable, automated solution by developing an intelligent detection algorithm based on ResNet, a deep convolutional neural network architecture known for its high performance in visual recognition tasks. A comprehensive dataset of 23,900 annotated images was constructed under a statistically robust sampling strategy, ensuring a 95% confidence level with a 5% margin of error in a balanced 50–50 distribution. Of these, 19,100 images were used to train the CNN model, while 4,800 were reserved for rigorous validation and testing. The dataset was systematically categorized into three distinct coating conditions: uncoated, partially coated, and fully coated. Using a 5-fold cross-validation approach, the proposed model achieved a high accuracy rate of 93%, demonstrating its effectiveness in classifying coating levels. This advancement represents a critical step toward automated, scalable quality control in steel production and infrastructure maintenance. By reducing reliance on manual inspection and minimizing material waste or under-coating, the system contributes to sustainable construction practices and extends the service life of structural steel components. Convolutional neural network ; Automation ; Coating ; Scalability ; Margin (machine learning) ; Artificial neural network Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Thermography and Photoacoustic Techniques
Authors: Ryo Mashita | Liang Xiaouyu | Ryosuke Ueda | Yasumasa Bito | Kentaro Kajiwara | Yoshimasa Urushihara | Hiroyuki Kishimoto | Wataru Yashiro
Source: Japanese Journal of Applied Physics (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Development of an automated X-ray tomography measurement system with a temporal resolution of 10 ms Abstract Statistical methods, including machine learning, require large amounts of input data. When imaging data are used, minimizing measurement time becomes especially important. To address this, we developed an automated system that reduces the acquisition time of X-ray tomography with synchrotron radiation. Using this system, we successfully observed the fracture process of rubber materials at a temporal resolution of 10 ms. This work is expected to support the advancement of materials informatics, particularly for studying non-equilibrium states. Data acquisition ; Temporal resolution ; Tomography ; System of measurement ; Resolution (logic) ; Process (computing) ; High resolution Machine Learning in Materials Science ; Advanced X-ray Imaging Techniques ; Advanced X-ray and CT Imaging
Authors: Shucheng Bao | Wei Zhu | Lixing Liang | Man Zhou | Tian Qiu | Shijie Zhao | Yuan Deng
Source: Energy Conversion and Management (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Application-orientated design of micro thermoelectric coolers using a Machine learning-driven reverse framework Thermoelectric cooling ; Thermoelectric effect ; Optimal design ; Function (biology) ; Water cooling ; Design of experiments ; Artificial neural network Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials ; Thermal Expansion and Ionic Conductivity
Authors: Artem Chuprov | Egor E. Nuzhin | Alexey Tsukanov | Nikolai V. Brilliantov
Source: Computer Physics Communications (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Fast and accurate quasi-atom method for simultaneous atomistic and continuum simulation of solids • First demonstration of a hybrid multiscale approach for simulation collisions of micron-size particles’, while maintaining atomic-level accuracy in the contact region. • Elaboration of an efficient AI-based optimization scheme to determine parameters of interaction potentials between quasi-atoms, comprising composite medium, which mimics continuum medium with the same material constants. • Elaboration of Python-based package for LAMMPS for a seamless optimization of quasi-atom parameters and multiscale MD simulations. • Verification of Hertz and JKR theories for impacts of micron-size particles with pairwise and many-body inter-atomic potentials. We report a novel hybrid method of simultaneous atomistic simulation of solids in critical regions (contacts surfaces, cracks areas, etc.), along with continuum modeling of other parts. The continuum is treated in terms of quasi-atoms of different size, comprising composite medium. The parameters of interaction potential between the quasi-atoms are optimized to match elastic properties of the composite medium to those of the atomic one. The optimization method coincides conceptually with the online Machine Learning (ML) methods, making it computationally very efficient. Such an approach allows a straightforward application of standard software packages for molecular dynamics (MD), supplemented by the ML-based optimizer. The new method is applied to model systems with a simple, pairwise Lennard-Jones potential, as well with multi-body Tersoff potential, describing covalent bonds. Using LAMMPS software we simulate collision of particles of different size. Comparing simulation results, obtained by the novel method, with full-atomic simulations, we demonstrate its accuracy, validity and overwhelming superiority in computational speed. Furthermore, we compare our method with other hybrid methods, specifically, with the closest one – AtC (Atomic to Continuum) method. We demonstrate a significant superiority of our approach in computational speed and implementation convenience. Finally, we discuss a possible extension of the method for modeling other phenomena. Pairwise comparison ; Molecular dynamics ; Software ; Software package ; Composite number ; Dynamical simulation ; Material properties Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Composite Material Mechanics
Authors: Akram Hossain | A H M Kamruzzaman | Md. Halimuzzaman | Aloke Soumya Saborna | Md. Baki Billah Ripon
Source: American Journal of Geospatial Technology (article, 2026-02-09)
Field: Materials Science — Polymers and Plastics

Usages of AI in Waste Minimization and Recycling Strategies in Textile Manufacturing The purpose of this research was to assess the potential impact of Artificial Intelligence (AI) technology on the textile industry’s waste-reduction and recycling measures. Primary data were collected through focused survey on package performance, interviews of production managers, quality control engineers, and sustainability officers working in different textile industries. The qualitative interviews were analyzed by way of thematic content analysis. The results show that weaving mills are the sector with the greatest total waste (93±7 kg per 1000 m² of fabric) and cutting of fabric is the main source of it in all the sectors. However, mechanical recycling process, then the chemical recycling and finally the waste to energy process is the major processes in marketing. AI adoption is driven by data analytics for process optimization (65.4±4.8% for garment manufacturing) and machine learning for predictive maintenance (55.2±3.1%), whereas AI for recycling and sorting automation exhibits lower adoption (28.6±2.8%). After AI implementation, garment manufacturing produced the highest total waste percentage reduction (17.1±3.1%) and total cutting waste reduction (20.8±3.4%). The rewards are also seeing are also identified being less wastage of materials by 72%, better efficiency within the production process by 68.4% and less machine rework by 62.5%. The highest barriers to AI deployment are costs of implementation (65.3±4.7% in garment manufacturing) and technical expertise (58.6±3.5%) highly cited, with staff training (68.1±5.1%) and financial incentives (55.7±4.8%) recognized as important fore-runners. In summary, combined with targeted waste reduction and recycling, the practice of AI is a viable route toward sustainable, efficient, and environmentally benign textile manufacturing. Production (economics) ; Quality (philosophy) ; Sustainability ; Process (computing) ; Rework ; Incentive ; Overall equipment effectiveness ; Sorting Textile materials and evaluations ; Food Waste Reduction and Sustainability ; Sustainable Supply Chain Management
Authors: Flavio Giuliani | Francesco Guidarelli Mattioli | Yuhan Chen | Dario Baratella | Daniele Dragoni | Marco Bernasconi | John Russo | Lilia Boeri | Riccardo Mazzarello
Source: Proceedings of the National Academy of Sciences (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Liquid anomalies and fragility of supercooled antimony Phase-change materials (PCMs) based on group IV, V, and VI elements, such as Ge, Sb, and Te, exhibit distinctive liquid-state features, including thermodynamic anomalies and unusual dynamical properties, which are believed to play a key role in their fast and reversible crystallization behavior. Antimony (Sb), a monoatomic PCM with ultrafast switching capabilities, stands out as the only elemental member of this group for which the properties of the liquid and supercooled states have so far remained unknown. In this work, we use large-scale molecular dynamics simulations with a neural network potential trained on first-principles data to investigate the liquid, supercooled, and amorphous phases of Sb across a broad pressure-temperature range. We uncover clear signatures of anomalous behavior, including a density maximum and nonmonotonic thermodynamic response functions, which are described by a two-state model based on the structural evolution of the liquid. Moreover, extrapolation of the viscosity to the glass transition, based on configurational and excess entropies, indicates that Sb is a highly fragile material. Our results present a compelling case for the connection between the liquid-state properties of PCMs and their unique ability to combine high amorphous-phase stability with ultrafast crystallization. Supercooling ; Fragility ; Extrapolation ; Antimony ; Monatomic gas ; Molecular dynamics ; Amorphous solid ; Crystallization Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Phase Change Materials Research
Authors: Qi Dong | T.. Zhang | Jianhua Zhang | Yanze Du | Jinghong Ma | Rui Li | Bo Qin | Haijun Jiao
Source: Angewandte Chemie International Edition (article, 2026-02-03)
Field: Chemistry — Inorganic Chemistry

Encapsulated Non‐Exchangeable Na <sup>+</sup> Ions Determining the Upper Limit of Al Inclusion in FAU—A Multiscale Simulation Na<sup>+</sup> ion as charge-balance agent controlling the distribution of framework aluminum atoms in Y-zeolite from 1 to 14 Al atoms (Si/Al = 47-2.4) and the acid strength have been investigated based on DFT computations, descriptors from machine learning, AIMD simulations, and experimental analysis. Different from previous results with proton (H<sup>+</sup>) as charge-balancing agent preferring next-nearest-neighbor Al separation (3N-Al), the framework Al atoms, in the presence of Na<sup>+</sup> ions, prefer larger separations (4N-Al and 5N-Al) due to the balance between Na<sup>+</sup>/AlO<sub>4</sub> <sup>-</sup> attractive and AlO<sub>4</sub> <sup>-</sup>/AlO<sub>4</sub> <sup>-</sup> repulsive interaction and the preferential occupancy of Na<sup>+</sup> ions following the order of six-membered ring (6MR) > double six-membered ring (D6R) > four-membered ring (4MR). The computed sequential substitution enthalpy for 1-14 Al shows the thermodynamically favorable upper limit of rather low Si/Al ratio (≤ 3) and explains the difficult synthesis of Y-zeolite with higher Si/Al ratios. Y-zeolite with non-exchangeable Na<sup>+</sup> ions has stronger acid strength based on the adsorption energy of pyridine and ammonia and exhibits higher catalytic activity in propane cracking. Ion ; Adsorption ; Ring (chemistry) ; Enthalpy ; Pyridine ; Limit (mathematics) ; Propane Zeolite Catalysis and Synthesis ; Advancements in Battery Materials ; Thermal Expansion and Ionic Conductivity
Authors: Yuzhou Hao | Yujie Liu | Xuejie Li | Turab Lookman | Xiangdong Ding | Jun Sun | Zhibin Gao
Source: Applied Physics Letters (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

LLM-driven discovery for carbon allotropes with bond-network entropy The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using ab initio calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between sp, sp2, and sp3 hybridization states dictates material performance, we establish a closed-loop artificial intelligence (AI) framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report “yne-diamond C12” and “yne-hex-diamond C8,” which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed sp–sp3 hybridization. Furthermore, we discovered a complex sp–sp2–sp3 hybridized C12 phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond {96 GPa [R. A. Andrievski, Int. J. Refract. Met. Hard Mater. 19, 447–452 (2001)]}. Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with MLPs for the accelerated inverse design of functional materials. Anisotropy ; Ab initio ; Diamond ; Ab initio quantum chemistry methods ; Thermal conductivity ; Inverse ; Carbon fibers ; Configuration entropy ; Carbon nanotube Machine Learning in Materials Science ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Irem ONER ALP | Bilgehan Barıs ONER | Ecem HELVACI | Cevat ACIKEL
Source: Springer Link (Chiba Institute of Technology) (article, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Negative-refractive-index behavior in a periodic photonic metamaterial: unit-cell topology tailoring for broadband response This paper examines how systematic control of unit-cell topology governs dispersion characteristics and bandwidth stability in configured periodic structures with numerical simulations. Distinct from the existing literature, the study targets a negative refractive index over a broad frequency range, while neural network–based optimization is used as a supporting tool to efficiently guide for bandwidth maximization. Comprehensive analysis is employed to optimize bandwidth effectively and validate the applicability of the resulting low-dispersion periodic structures. For the optimized unit cell, the third photonic band spans a wavelength range 1388–1631 nm (after model enhancement 1429–1578 nm) covering the S, C, and L optical communication bands, demonstrating broadband and stable dispersive behavior beyond a narrow resonance regime. The obtained results are thus expected to enhance the performance of negative-index based photonic components used in optical and photonic communication systems, compact antenna architectures, and advanced material engineering. Broadband ; Photonics ; Bandwidth (computing) ; Topology (electrical circuits) ; Optical communication ; Dispersion (optics) ; Network topology ; Refractive index ; Wavelength Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Kan Tang | Shengxian Liu | Jian Zhou | Zhimei Sun
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Highly Efficient 2D Photocatalytic Materials for Acid Water Splitting Exciton ; Absorption (acoustics) ; Band gap ; Photocatalysis ; Water splitting ; Binding energy ; Recombination ; Work (physics) ; Electronic structure 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Yang Zhong | Xiwen Li | Xingao Gong | HongJun XIANG
Source: ArXiv.org (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition of the electrostatic energy, deriving a variationally consistent mapping from the electron density matrix to effective atomic charges. We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation. Benchmarks demonstrate that physics-based long-range corrections are essential: purely data-driven attention mechanisms fail to capture macroscopic electrostatic potentials. Benchmarks on polar ZnO slabs, CdSe/ZnS heterostructures, and GaN/AlN superlattices show two- to threefold error reductions and robust transferability to systems far beyond training sizes, eliminating the characteristic staircase artifacts that plague short-range models in the presence of built-in electric fields. Coulomb ; Hamiltonian (control theory) ; Polar ; Electrostatics ; Matrix (chemical analysis) ; Electric field ; Matrix multiplication ; Basis (linear algebra) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks