jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xusheng Zhang
Source: Academic Journal of Science and Technology (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Lightweight Automotive Materials And Structural Design and Performance Prediction Based on Fundamental Algorithms In the context of global energy conservation, emission reduction, and sustainable development, automotive lightweight technology has become a crucial direction for the transformation and upgrading of the automotive industry. By reducing vehicle weight, it is possible to significantly improve fuel economy, extend the driving range of new energy vehicles, and decrease overall greenhouse gas emissions, thereby supporting both environmental and economic goals. This paper centers on the design and performance prediction of lightweight automotive materials and structures, with a particular emphasis on the integration of advanced algorithms into engineering practice. It first reviews the industrial background and strategic significance of lightweighting, followed by a summary of the mechanical, thermal, and economic properties of common lightweight materials such as aluminum alloys, magnesium alloys, high-strength steels, and composite materials. Furthermore, the paper introduces collaborative design concepts that couple material development with structural innovation, and discusses multiple lightweight design strategies. Special attention is given to the role of machine learning, topology optimization, and multi-objective optimization in predicting material performance, guiding structural design, and balancing trade-offs among cost, safety, and sustainability. Finally, through case studies of typical manufacturers, the paper analyzes practical applications and summarizes future trends, aiming to provide a comprehensive theoretical reference and methodological guidance for research and practice in automotive lightweight technology. Automotive industry ; Context (archaeology) ; Range (aeronautics) ; Fuel efficiency ; Engineering design process ; Process (computing) ; Efficient energy use ; Greenhouse gas Machine Learning in Materials Science ; Material Selection and Properties ; Mechanical Engineering and Vibrations Research
Authors: Umberto Raucci (433995)
Source: Figshare (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Capturing ExcitedState Proton Transfer Dynamics withReactive Machine Learning Potentials Excited state proton transfer is a fundamental process in photochemistry, playing a crucial role in fluorescence sensing, bioimaging, and optoelectronic applications. However, fully resolving its dynamics remains challenging due to the prohibitive computational cost of <i>ab initio</i> simulations and the need for ultrafast experimental techniques with high temporal resolution. Here, we tackle this challenge by using machine learning-driven excited state molecular dynamics simulations. We propose an active learning framework powered by enhanced sampling techniques for constructing a high-quality training set for excited state machine learning potentials, which we then use to map the reaction free energy landscape and capture the photorelaxation dynamics. Using 10-hydroxybenzo[<i>h</i>]quinoline as a test case, our simulations reveal a barrierless excited state proton transfer occurring within ∼50 fs, accompanied by a significant red shift in the emission energy (∼1 eV), in agreement with experimental findings. Furthermore, our results highlight a strong coupling between proton transfer and charge redistribution, which facilitates the rapid tautomerization process. These findings showcase the power of machine learning-driven molecular dynamics in accurately capturing photochemical dynamics while enabling large-scale statistical sampling. Excited state ; Proton ; Molecular dynamics ; QM/MM ; Dynamics (music) ; Coupling (piping) ; Energy (signal processing) ; Potential energy ; Reaction dynamics Machine Learning in Materials Science ; Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies
Authors: Zhiyu An | Jingjie Yeo
Source: International Journal of AI for Materials and Design (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

Machine-learned molecular modeling of ruthenium: A Kolmogorov-Arnold Network approach Developing refractory high-entropy superalloys (RSAs) with performance advantages over nickel-based alloys is a critical frontier in materials science. Body-centered cubic (bcc)-based RSAs have attracted significant attention, with ruthenium (Ru) playing a key role in forming two-phase regions of A2 (disordered bcc) + B2 (ordered bcc), which could lead to superalloy-like microstructures. This study introduces the application of the Kolmogorov-Arnold Network (KAN) model to predict the mechanical and thermodynamic properties of Ru while comparing its performance against other commonly used machine-learned models. Utilizing density functional theory calculations as training data, the KAN model demonstrates superior accuracy and computational efficiency compared to conventional methods, while reducing descriptor complexity. The model accurately predicts a range of properties, including elastic constants, thermal expansion coefficients, and various moduli, with discrepancies within 6% of experimental reference data. Molecular dynamics simulations further validate the model&amp;rsquo;s efficacy, accurately capturing Ru&amp;rsquo;s phase transitions from hexagonal close-packed (hcp) to face-centered cubic structure and the melting point. This work presents the first application of KAN in materials science, demonstrating how its balanced performance and efficiency provide a new pathway for designing advanced materials, with unique advantages over conventional machine learning approaches in predicting material properties. Ruthenium ; Computer science ; Artificial intelligence ; Cognitive science ; Statistical physics ; Chemistry ; Psychology ; Physics ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Hayato Wakai | Atsuto Seko | Isao Tanaka
Source: ArXiv.org (preprint, 2025-03-28)
Field: Materials Science — Materials Chemistry

Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust methodology founded on polynomial MLPs to comprehensively enumerate crystal structures under high-pressure conditions and to evaluate their phase stability at finite temperatures. The proposed approach involves constructing polynomial MLPs with high predictive accuracy across a broad range of pressures, conducting reliable global structure searches, and performing exhaustive self-consistent phonon calculations. We demonstrate the effectiveness of this approach by examining elemental silicon at pressures up to 100 GPa and temperatures up to 1000 K, revealing stable phases across these conditions. The framework established in this study offers a powerful strategy for predicting crystal structures and phase stability under high-pressure and finite-temperature conditions. Polynomial ; Stability (learning theory) ; Phase diagram ; Phase (matter) ; Crystal structure prediction ; Crystal structure ; Phonon Machine Learning in Materials Science ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research
Authors: Kaoutar El Haloui | Nicolas Thome | Nicolas Sisourat
Source: Atoms (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Combining Physics and Machine Learning: Hybrid Models for Predicting Interatomic Potentials Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, they are computationally prohibitive for extensive applications. Alternatively, analytical physics-based models such as the Morse potential offer efficient solutions but are limited by their rigidity and poor generalization to excited states. In recent years, neural networks have emerged as powerful tools for determining PES, due to their universal function approximation capabilities, but they require large training datasets. In this work, we investigate hybrid-residual modeling approaches that combine physics-based potentials with neural network corrections, aiming to leverage both physical priors and data adaptability. Specifically, we compare three hybrid models—APHYNITY, Sequential Phy-ML, and PhysiNet—in their ability to reconstruct the potential energy curve of the ground and first excited states of the hydrogen molecule. Each model integrates a simplified physical representation with a neural component that learns the discrepancies from accurate reference data. Our findings reveal that hybrid models significantly outperform both standalone neural networks and pure physics-based models, especially in low-data regimes. Notably, APHYNITY and Sequential Phy-ML exhibit better generalization and maintain accurate estimation of physical parameters, underscoring the benefits of explicit physics incorporation. Artificial neural network ; Leverage (statistics) ; Generalization ; Representation (politics) ; Ansatz ; Potential energy ; Rigidity (electromagnetism) ; Excited state Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: S. Jenefar
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-04-15)
Field: Chemistry — Analytical Chemistry

Smart System Approach for Prediction of Microbial Quality of Milk Using Machine Learning Developing a real-time system to assess milk quality using Resazurin dye and machine learning. The system combines an Arduino Uno, color sensor, and LCD display to detect color changes in milk samples. These changes indicate microbial activity and quality. By integrating Python and machine learning algorithms, the system processes and analyzes data, providing accurate and immediate milk quality assessments. This automated solution enhances efficiency and reliability, reducing the need for manual inspections and improving overall dairy quality control.The system's ability to deliver accurate, real-time information underscores its potential for impactful use in scientific and industrial settings, highlighting its significance in the field of chemical analysis. Quality (philosophy) ; Computer science ; Artificial intelligence ; Food science ; Machine learning ; Chemistry ; Philosophy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Eunho Lee | Junho Sung | Sein Chung | Byeongjun Jeon | Donghwa Lee | M. An | Jaeeun Shin | Yong Ju Jang | Bumjoon Seo
Source: Research Square (preprint, 2025-08-26)
Field: Materials Science — Polymers and Plastics

Conjugated Backbone-Directed Side Chain-Electrolyte Coupling toward Nonvolatile Artificial Synapse Side chain ; Synapse ; Coupling (piping) ; Electrolyte ; Conjugated system ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Neuroscience ; Polymer ; Biology ; Organic chemistry ; Electrode Conducting polymers and applications ; Electrochemical Analysis and Applications ; Molecular Junctions and Nanostructures
Authors: Claudia Arias | Andrés Felipe Rocha Parra | Jesica Liberati | Diego  Rocha Parra | Pablo  Daniel Ribotta | Orlando  Ruiz Villadiego
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Application of NIR spectroscopy and chemometric methods for the discrimination and quantification of dietary fiber fractions in apple pomace flours from two different industrial processes Pomace ; Calibration ; Fiber ; Pressing ; Partial least squares regression ; Extraction (chemistry) Spectroscopy and Chemometric Analyses ; Food composition and properties ; Advanced Cellulose Research Studies
Authors: Gajanan M. Hingangavkar
Source: Next research. (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Applied mathematical and computational frameworks in materials science: a comprehensive review Bridging (networking) ; Merge (version control) ; Field (mathematics) ; Mathematical structure ; Statistical mechanics ; Computational mechanics ; Mathematical model ; Multiscale modeling Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topology Optimization in Engineering
Authors: Hilhorst, Antoine | Trinquet, Victor | Rignanese, Gian-Marco | Jacques, Pascal | ICSMA20
Source: Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhancing the mechanical properties of TWIP alloys by generalized stacking fault energy engineering Modern structural parts must meet increasingly stringent requirements, encompassing not only mechanical performance but also considerations such as life cycle, processability, and ethical sourcing of raw materials. The twinning induced plasticity (TWIP) mechanism can enhance the strain hardening of metallic materials, which helps to improve both strength and ductility, overcoming the typical trade-off between these two properties. While the benefits of deformation twinning are acknowledged, the limited understanding of some aspects of the TWIP effect hinders the development of new alloys with enhanced combinations of strength and ductility. To address these knowledge gaps, the present work focusses on the generalized stacking fault energy (GSFE), a critical material parameter that may impact the performance of TWIP alloys. On the one hand, this work highlights the complex relationship between alloy composition, intrinsic and extrinsic SFEs, and their correlation with mechanical properties by a database analysis approach. The database contains more than a thousand entries and gathers all SFE values measured by node as well as partial dislocation separation methods using transmission electron microscopy (TEM) as well as SFE values measured by diffraction. On the other hand, we propose a novel high-throughput approach to compute the GSFE (i.e. the unstable and stable intrinsic SFEs, the unstable and stable extrinsic SFEs, as well as the energies associated to HCP nucleation) based on state-of-the-art machine learning (ML) potentials. This new approach is also compared to a density functional theory (DFT) based approach. The combination of the database and GSFE computations offers a comprehensive methodology in order to offer guidelines for the discovery of novel strong and ductile TWIP alloys. Twip ; Stacking-fault energy ; Crystal twinning ; Plasticity ; Deformation mechanism ; Deformation (meteorology) ; Ultimate tensile strength ; Stacking fault ; Work (physics) Microstructure and mechanical properties ; High Entropy Alloys Studies ; High Temperature Alloys and Creep
Authors: Cuadros-Rodriguez Luis
Source: Analytical Chemistry (article, 2025-12-30)
Field: Chemistry — Analytical Chemistry

Basic Process Equation for Analytical Chemistry – An Inclusive and Conciliatory Approach The proposal outlined in this article aims to use information flow as a benchmark for analytical chemistry. It starts with raw information, mostly implicit and nonobvious (latent information), enclosed in the analytical signal, which is transformed into explicit and interpretable target chemical information (patent information). A basic process equation is proposed, derived from the currently accepted definition of analytical chemistry, which reflects the paradigm change from measurement to information. The main innovation lies in the inclusion of a term called the 'analytical operator,' which is responsible for transforming the information. The terms of this equation are interpreted and matched with different analytical methods, as well as with the R&D activities involved in current analytical chemistry. The proposal provides, for the first time, an inclusive approach that reconciles traditional analytical methods, relying on univariate data, with analytical methods derived from the application of chemometrics (or, in the same way, data mining, machine/deep learning, artificial intelligence, etc.) based on multivariate data. For better understanding, the determination of the iodine value in vegetable oils has been considered as a guiding example demonstrating how the proposed process equation is valid throughout the different periods of analytical chemistry, regardless of the analytical method applied. Chemometrics ; Univariate ; Process (computing) ; Benchmark (surveying) ; Structural equation modeling ; Raw data ; Calculus (dental) ; Term (time) Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Fault Detection and Control Systems
Authors: Pieter Dobbelaere (21759927) | Sander Vandenhaute (11340198) | Veronique Van Speybroeck (7359878)
Source: Figshare (article, 2025-07-22)
Field: Chemistry — Inorganic Chemistry

Cluster-BasedMachine Learning Potentials to DescribeDisordered Metal–Organic Frameworks up to the Mesoscale Metal–organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model’s training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over 20 thousand atoms. Afterward, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behavior. Our analysis includes large supercell structures, demonstrating that (near-) <i>ab initio</i> accuracy is within reach at the mesoscale. Mesoscopic physics ; Supercell ; Mesoscale meteorology ; Complex system ; Training set Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Muhammad Munsif | Altaf Hussain | Zulfiqar Ahmad Khan | Min Je Kim | Sung Wook Baik
Source: Advanced Engineering Informatics (article, 2025-05-30)
Field: Materials Science — Materials Chemistry

Hierarchical attention-based framework for enhanced prediction and optimization of organic and inorganic material synthesis Computer science ; Artificial intelligence ; Biochemical engineering ; Process engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Yanhao Deng | T. Jiang | Zeyu Deng | Yanming Wang
Source: Journal of Chemical Theory and Computation (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

luMOD: A Lightweight Universal Midrange Atomic Orientational Descriptor Extracting structural information in atomistic simulations is crucial for applying machine learning to understand structure-property relationships and accelerate materials exploration. Existing descriptors can encode beyond-local environments and address multispecies complexities, but often suffer from high dimensionality and limited flexibility. In this work, we develop luMOD, a lightweight universal Midrange atomic Orientational Descriptor composed of only 24 features. Despite its compact size, it integrates direct and convoluted bond orientational order parameters with neighbor species distributions, enabling efficient description of heterogeneous atomic environments in several nearest-neighbor layers. luMOD demonstrates strong versatility across both single- and multispecies systems, including complex crystal structure interfaces such as perovskites and olivines. In addition to the well-maintained accuracy and applicability of luMOD, its fixed short length generally helps reduce the training time of downstream machine learning models, particularly in multispecies systems. We believe this development offers a promising step toward more efficient and scalable structural descriptors, helping to accelerate data-driven materials discovery and design. Curse of dimensionality ; ENCODE ; Scalability ; Order (exchange) ; Encoding (memory) ; Dimensionality reduction Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Neha P. Lanke | Manoj B. Chandak
Source: Journal of Optics (article, 2025-08-16)
Field: Chemistry — Analytical Chemistry

Recent trends in deep learning and hyperspectral imaging for fruit quality analysis: an overview Hyperspectral imaging ; Remote sensing ; Quality (philosophy) ; Optics ; Image quality ; Computer science ; Artificial intelligence ; Geology ; Image (mathematics) ; Physics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Water Quality Monitoring and Analysis
Authors: Ali Ramlaoui | Martin Siron | Inel Djafar | Joseph Musielewicz | Armando Rossello | Victor Schmidt | Alexandre Duval
Source: ArXiv.org (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling The development of accurate machine learning interatomic potentials (MLIPs) is limited by the fragmented availability and inconsistent formatting of quantum mechanical trajectory datasets derived from Density Functional Theory (DFT). These datasets are expensive to generate yet difficult to combine due to variations in format, metadata, and accessibility. To address this, we introduce LeMat-Traj, a curated dataset comprising over 120 million atomic configurations aggregated from large-scale repositories, including the Materials Project, Alexandria, and OQMD. LeMat-Traj standardizes data representation, harmonizes results and filters for high-quality configurations across widely used DFT functionals (PBE, PBESol, SCAN, r2SCAN). It significantly lowers the barrier for training transferrable and accurate MLIPs. LeMat-Traj spans both relaxed low-energy states and high-energy, high-force structures, complementing molecular dynamics and active learning datasets. By fine-tuning models pre-trained on high-force data with LeMat-Traj, we achieve a significant reduction in force prediction errors on relaxation tasks. We also present LeMaterial-Fetcher, a modular and extensible open-source library developed for this work, designed to provide a reproducible framework for the community to easily incorporate new data sources and ensure the continued evolution of large-scale materials datasets. LeMat-Traj and LeMaterial-Fetcher are publicly available at https://huggingface.co/datasets/LeMaterial/LeMat-Traj and https://github.com/LeMaterial/lematerial-fetcher. Scalability ; Trajectory ; Modular design ; Disk formatting ; Extensibility ; Relaxation (psychology) ; Reduction (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography