jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Devang Khambhayta | Bhavin Kachhatiya | Yash Bhut | S.P. Gaur
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Advances in Nitinol Laser Cutting: Exploring Mixed Gas Applications and Process Optimization Nitinol, a shape memory alloy widely used in biomedical and aerospace industries, requires precise fabrication techniques such as laser cutting to maintain its unique properties. Mixed gas environments have demonstrated potential in enhancing the efficiency and quality of laser cutting processes, but their role remains insufficiently explored. This review investigates advancements in Nitinol laser cutting, focusing on mixed gas applications and process optimization through bibliometric analysis of studies till 2025. Key trends highlight the growing use of reactive gases for improved energy absorption and inert gases for superior surface quality, but challenges persist in standardizing methodologies and addressing scalability. Unanswered questions include the environmental impact of gas mixtures and their effects on long-term material performance. Recommendations emphasize integrating real-time monitoring, machine learning, and sustainable gas technologies for future research. Process (computing) ; Materials science ; Laser ; Laser cutting ; Computer science ; Process engineering ; Optoelectronics ; Engineering ; Optics ; Physics Shape Memory Alloy Transformations ; Laser Material Processing Techniques ; Metal and Thin Film Mechanics
Authors: Las Johansen B. Caluza | Arnel C. Fajardo
Source:  (article, 2025-05-27)
Field: Chemistry — Analytical Chemistry

Feature Engineering and Optimization of Convolutional Neural Network in Classifying Tikog Leaf for Banig Weaving Industry Innovative agricultural technology provides international coverage for practical applications and intelligent systems integrating advanced computing. Among those technologies introduced were artificial intelligence (AI) and machine learning. Thus, ongoing research focuses on improving CNN architectures and exploring their applications across various fields, including agriculture domain. However, in the agricultural domain, specifically for classifying Tikog leaves, a raw material used in the banig weaving industry utilizing feature engineering and optimization of Convolutional Neural Network (CNN) using featuring engineering approach remains unexplored. Therefore, this study investigated the application of CNNs in the agricultural domain, specifically for classifying Tikog, a raw material used in the banig weaving industry. This study utilized the KDD process, employing various machine learning algorithms such as color lab space representation, data augmentation, feature extraction, pooling technique, and fine-tuning of 500 Standard Quality and 500 Substandard Quality original data and augmented to 3000 images and 500 images for testing or validation purposes. Results revealed that there were 27 cases optimized models generated, and four (4) emerged as top-performing models, with Case 12 as the best-optimized model with accuracy (94.23%, 96.83%), precision (92.45%, 95.77%), recall (96.33%, 98.00%), F1-score (94.35%, 96.87%), ROC/AUC (98.18%, 99.40%) and SSE (173, 19). Case 12 optimized CNN model shows a non-overfitting model with computational efficiency and is highly effective, reinforcing its suitability as the best-performing model among the cases. Weaving ; Feature engineering ; Convolutional neural network ; Artificial neural network ; Pooling ; Feature (linguistics) ; Raw data ; Quality (philosophy) Spectroscopy and Chemometric Analyses
Authors: Ulzhalgas Nazarbek | P.A. Abdurazov | Galiya Kambarova | Yerkebulan Raiymbekov
Source: SERIES CHEMISTRY AND TECHNOLOGY (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

MACHINE LEARNING-BASED PREDICTION OF TEMPERATURE-DRIVEN SOLUBILITY CHANGES IN AQUEOUS SALT SOLUTIONS This work explores the predictive capabilities of machine learning techniques in modeling the solubility behavior of KCl, BaCl₂, and NiCl₂ as a function of temperature. Employing Random Forest and Gradient Boosting algorithms, the study compared predicted solubility values with experimental data to assess model performance. Random Forest exhibited the highest reliability, especially for BaCl₂ and KCl, due to its ensemble approach and resistance to data irregularities. Although Gradient Boosting effectively captured nonlinear patterns, its accuracy declined at lower temperatures for NiCl₂ and KCl, likely due to unmodeled chemical factors such as complexation and ionic strength fluctuations. The findings affirm the relevance of machine learning for forecasting chloride solubility, highlighting NiCl₂ as a system sensitive to multifactorial interactions. Future improvements may involve integrating additional descriptors such as solution pH, total ionic strength, and ligand availability. Solubility ; Random forest ; Gradient boosting ; Boosting (machine learning) ; Ionic strength ; Nonlinear system ; Aqueous solution ; Support vector machine Crystallization and Solubility Studies
Authors: Jacob C. Hickey | Arman M. Karimaghaei | Matt Flores | Son Minh Hoang | Roy Arrieta | Amit Kumar | Gonzalo Cuervo | Peter Zhu | Jakoah Brgoch
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Identifying inorganic solids for harsh environments <i>via</i> machine learning Developing multifunctional materials with superior mechanical properties, including high hardness and oxidation resistance, remains essential for aerospace, defense, and industrial applications. Machine learning offers a powerful, data-driven pathway for discovering new hard, oxidation-resistant materials for these uses, providing an efficient and scalable alternative to conventional materials discovery methods. Here, we present a pair of extreme gradient boosting (XGBoost) models, trained on compositional and structural descriptors. A Vickers hardness (<i>H</i> <sub>V</sub>) model was developed using a curated dataset of 1225 while a model for predicting the oxidation temperature (<i>T</i> <sub>p</sub>) was constructed using 348 compounds. The model was subsequently validated against a diverse dataset of 18 inorganic compounds, including borides, silicides, and intermetallics, with previously unmeasured oxidation temperatures. Integrating the updated structure-informed hardness model with the new oxidation model enabled the identification of multifunctional materials that simultaneously exhibit superior hardness and enhanced oxidation resistance. This work highlights the potential of machine learning to accelerate materials discovery and provides a robust framework for identifying compounds capable of withstanding extreme environments. Scalability ; Boosting (machine learning) ; Identification (biology) ; Gradient boosting ; Vickers hardness test ; Extreme learning machine Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Li-Qun Shen | Hao-Jin Wang | Mengzhao Sun | Yang Xiang | Xin-Ning Tian | Yue Chai | Yue Yang | Feng Ding | Xiao Kong | Marc‐Georg Willinger | Zhu‐Jun Wang
Source: ArXiv.org (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems. Faceting ; Surface reconstruction ; Maxima and minima ; Surface (topology) ; Lattice (music) ; Traverse ; Decoding methods ; Topology (electrical circuits) Graphene research and applications ; Topological Materials and Phenomena ; Surface and Thin Film Phenomena
Authors: Budi Purwanto
Source: Journal of Energy and Mechanical Engineering (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

PETA RISET MUTAKHIR TITANIUM ALLOY (KHUSUSNYA TI-6AL-4V) UNTUK APLIKASI TEKNIK: SINTESIS &amp; REKAYASA PERMUKAAN, PEMESINAN BERBASIS DATA, SERTA STRATEGI KENDALI KEAUSAN (LITERATUR REVIEW) Titanium alloys—especially Ti-6Al-4V—play a pivotal role in aerospace, biomedical, and energy applications due to their high strength-to-weight ratio and corrosion resistance. However, recurring key limitations remain: high wear/friction, “difficult-to-machine” behavior (localized heat, adhesion, rapid tool wear), and the sensitivity of surface integrity to process parameters. This review presents a comprehensive synthesis based on the provided literature (BUDI as the main review/extraction source and DATA 1 as additional recent abstracts), with emphasis on findings from 2024–2026. We map four major clusters: (1) materials and microstructure engineering (e.g., high-pressure sintering, multicomponent solid solutions), (2) surface engineering and coatings to enhance hardness/wear resistance (e.g., diffusion layers, micro-arc coatings), (3) machining and forming processes (turning/milling/drilling/thread rolling) including wear mechanisms, surface quality, and productivity–tool-life trade-offs, and (4) the use of machine learning (ML) and explainable/decision-support approaches for damage prediction, wear classification, and parameter optimization. The main contribution of this review is cross-cluster integration: linking physical mechanisms (tribology, thermal effects, deformation) with data-driven frameworks (explainable ML) as a bridge toward “manufacturing intelligence.” We also highlight research gaps, including limited data standardization (sensors, wear labels), insufficient model generalization across machines/materials, and a lack of sustainability assessments that are directly connected to operational decision-making. This review provides extensive summary tables to guide researchers and industry in designing more precise, reproducible, and readily translatable strategies to improve Ti-6Al-4V performance. Standardization ; Process (computing) ; Machining ; Bridge (graph theory) ; Sustainability Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Machine Learning in Materials Science
Authors: Silvio Paganucci | Jerome S. Lukas | Filippo Parisi | Tina Moor | Lukas Braun | Tobias Wolfer | Patric Eberle
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Comparative analysis of machine learning models for discrimination of cellulosic fibers using NIR hyperspectral imaging Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Spectral signature ; Artificial neural network ; Cellulosic ethanol ; Range (aeronautics) ; Counterfeit Spectroscopy and Chemometric Analyses ; Advanced Cellulose Research Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Anuj Kumar Ray | Sandeep Nagar | Girish Varma | U. Deva Priyakumar | Ankan Paul
Source: ChemRxiv (preprint, 2025-03-26)
Field: Materials Science — Materials Chemistry

Leveraging High-Spin DFT Features for Prediction of Spin State Gaps in 3d Transition Metal Complexes Determining spin state gap energetics (SSE) in 3d transition metal complexes (TMCs) is a major challenge in theoretical chemistry, as high-level quantum methods, though reliable, are time-intensive for large-scale studies. This work explores a machine learning (ML)-based approach to predict DFT adiabatic SSE gaps using descriptors derived from a single high-spin DFT calculation. This approach is adopted to eliminate the differential treatment of electronic correlation between high spin (HS) and low spin (LS) structures. Our descriptors try to inculcate the knowledge of crystal field theory into the ML model. It includes atomic energy levels of bare metal ions, natural charges and d-orbital molecular orbital eigenvalues derived from an HS calculation, ligand HOMO-LUMO gaps, and simple identity-based features. We train ML models on 1434 SSE values spanning 934 complexes and demonstrate their transferability to more complex bidentate π-bonding ligands despite being trained on simpler Werner-type monodentate complexes. This approach bypasses the need for multi-reference low-spin (LS) optimizations while retaining predictive accuracy, offering a cost-effective strategy for SSE estimation in transition metal chemistry. We hope the insights covered in this study will contribute to the development of further electronic structure-based descriptors for SSE predictions. Spin (aerodynamics) ; Transition metal ; Spin states ; Condensed matter physics ; Materials science ; State (computer science) ; Spin transition ; Chemical physics ; Physics ; Chemistry ; Computer science ; Thermodynamics Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Magnetism in coordination complexes
Authors: Frank Neese
Source: Pure and Applied Chemistry (article, 2025-09-25)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum chemistry and large systems – a personal perspective Abstract This perspective offers a personal reflection on the evolution, current status, and open challenges of quantum chemistry in the context of large molecular systems. Beginning with Dirac’s famous 1929 prophecy, I revisit the historical trajectory of our discipline, from the development of early conceptual models to the emergence of density functional theory and correlated wavefunction methods. While enormous progress has been made in algorithmic sophistication, enabling accurate calculations on systems with thousands of atoms, I argue that accurate energies alone do not solve the entirety of chemical problems. Real-world applications demand more than electronic structure: solvation, entropy, conformational complexity, and the sheer complexity of large molecules challenge our methods and our assumptions. Equally important are the conceptual and philosophical tensions that continue to shape the field, especially the difficult balance between prediction and understanding. I advocate for a more integrative approach that values models, insight, and falsifiability alongside numerical precision. Finally, I briefly consider the emerging role of machine learning and the implications of automation for the future of theory. This article is not a review but a dialogue – with the field, with its history, and with its practitioners. Falsifiability ; Perspective (graphical) ; Context (archaeology) ; Reflection (computer programming) ; Automation ; Quantum Various Chemistry Research Topics ; History and advancements in chemistry ; Quantum Mechanics and Applications
Authors: Mateus
B. P. Querne (22412417) | Marco A. M. T. Machado (22412420) | Ronaldo C. Prati (9628010) | Natan M. Regis (22412423) | Matheus P. Lima (5616509) | Juarez L. F. Da Silva (8114024)
Source: Figshare (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Bridging Text Miningand Quantum Simulations for theDesign of 2D Monochalcogenide Materials The formulation of a structured framework dedicated to the systematic review of the literature, identification of potential compounds, theoretical quantum chemistry characterization, and assessment of the significance of stability descriptors is essential for accelerating the discovery of two-dimensional materials for technological applications. In this work, we selected the two-dimensional monochalcogenides (MQ), which have attracted increased interest due to their potential applications in future devices, as prototype materials for the present investigation. Our framework started with a natural language processing analysis of more than 5400 articles, revealing a growing research interest in the two-dimensional MQ compounds, especially in electronics, energy, and fundamental studies. This led to the selection of 27 diverse MQ compounds across 13 distinct two-dimensional structural phases for density functional theory calculations, resulting in an extensive database of physicochemical properties. We evaluated formation enthalpies, uncovering clear stability trends (e.g., stability declines with heavier chalcogens, dynamic robustness of GeSe), and evaluated equilibrium lattice parameters, noting predictable expansions and significant anisotropies. Bader charge analysis offered insights into charge-transfer and ionicity trends. Our electronic structure analysis identified band gaps (direct or indirect, depending on the M element group and the weight of the chalcogen) and optical absorption anisotropies significantly influenced by crystal symmetry and spin orbit coupling. Importantly, band alignment calculations classified all possible heterojunctions as type I, II, or III, underscoring their extensive potential for future optoelectronic device development. Additionally, we incorporated machine learning, using a random forest approach, along with our density functional theory calculations to accurately forecast trends in energetic properties. This analysis identified the electronic charge as a highly significant stability descriptor among the 34 descriptors, underscoring its importance for future machine learning endeavors. Hence, this study offers a streamlined framework for the characterization and discovery of promising two-dimensional materials, highlighting the synergy between data-driven analysis and quantum-based simulations. Density functional theory ; Bridging (networking) ; Stability (learning theory) ; Heterojunction ; Lattice (music) ; Robustness (evolution) ; Electronic structure ; Phonon ; Band gap 2D Materials and Applications ; Machine Learning in Materials Science ; Iron-based superconductors research
Authors: Kh. Abd El‐Aziz | Ibrahim B. M. Taha | Д. Сабер
Source: Research Square (preprint, 2025-06-30)
Field: Materials Science — Materials Chemistry

Studying and Predicting the Wear Performance of Destabilized High-Cr WIs Using Machine Learning and Artificial Intelligence Techniques Materials science ; Carbide ; Abrasive ; Martensite ; Tempering ; Austenite ; Metallurgy ; Microstructure ; Wear resistance Metal Alloys Wear and Properties ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Jie Li | Bin Zou | Xingna Zheng | Yi Ru | Yongxin Li | Hui Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Novel Strategy for Accurately Distinguishing Chinese Tea by Simultaneously Sensing of Tea Infusions and Tea Aroma Based on Multi-metal Nanozymes Aroma ; Identification (biology) ; Chinese tea ; Focus (optics) ; Pattern recognition (psychology) ; Substrate (aquarium) Advanced Nanomaterials in Catalysis ; Tea Polyphenols and Effects ; Nanocluster Synthesis and Applications
Authors: Silvio Paganucci | Jerome S. Lukas | Filippo Parisi | Tina Moor | Lukas Braun | Tobias Wolfer | Patric Eberle
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Comparative analysis of machine learning models for discrimination of cellulosic fibers using NIR hyperspectral imaging Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Spectral signature ; Artificial neural network ; Cellulosic ethanol ; Range (aeronautics) ; Counterfeit Spectroscopy and Chemometric Analyses
Authors: Tongyu Liu (8147607) | Peng-Hu Du (13122103) | Qiang Sun (225630)
Source: Figshare (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Superhalogen-BasedLayered Hybrid Superlattices withHigh Ion Conductivity and Exceptional Mechanical Robustness Layered hybrid superlattices are of current interest for functional materials [<i>Nature</i> 635 (2024) 49]. To further expand their energy applications, we propose the first layered hybrid superlattice electrolyte, NaBH<sub>4</sub>-hBN, by uniquely integrating superhalogen [BH<sub>4</sub>]<sup>−</sup> clusters into a hexagonal boron nitride (hBN) framework. This design leverages synergistic mechanisms: the weakened interlayer van der Waals interactions liberate Na<sup>+</sup> confinement, hBN-mediated lattice flexibility dynamically adjusts ion pathways, and the rotational motion from [BH<sub>4</sub>]<sup>−</sup> rotation transiently lowers migration barriers. First-principles calculations, combined with machine learning-driven molecular dynamics, reveal a high room-temperature Na<sup>+</sup> conductivity of 16 mS/cm, surpassing that of state-of-the-art solid-state electrolytes (SSEs), such as NASICON and layered P2-type oxides. Moreover, this material exhibits an ultralow density of 1.60 g/cm<sup>3</sup>, significantly lighter than conventional SSEs, while achieving exceptional mechanical robustness (bulk modulus = 115 GPa) and moderate electrochemical stability (1.89 V), with low interfacial reactivity (Δ<i>E</i><sub>D,min,mutual</sub> = 73 meV/atom) toward layered oxide cathodes. By bridging cluster dynamics with structural coherence, this work establishes a promising strategy for engineering next-generation SSEs, offering merits for overcoming long-standing trade-offs in solid-state energy storage. Superlattice ; van der Waals force ; Molecular dynamics ; Graphene ; Conductivity ; Robustness (evolution) ; Boron nitride ; Fast ion conductor ; Density functional theory ; Lattice (music) 2D Materials and Applications ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials