jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zenisha Shrestha | Bijendra Shrestha | Panmanas Sirisomboon | Umed Kumar Pun | Tri Ratna Bajracharya | Bim Prasad Shrestha | Pimpen Pornchaloempong
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-09-18)
Field: Chemistry — Analytical Chemistry

Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning Accurate determination of moisture content is essential for energy efficiency and biomass management for fuel materials such as durian husk. Traditional methods of determining biomass moisture content are time-consuming and require specialized expertise, posing challenges for continuous monitoring. To address this limitation, this study applies Near Infrared Spectroscopy (NIRS) combined with machine learning models to rapidly and accurately assess moisture content. Both linear Partial Least Squares Regression (PLSR) and non-linear approaches were used, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGB). The application of preprocessing techniques, notably the Savitzky-Golay second derivative (SD) and Standard Normal Variate (SNV), significantly augmented the predictive performance, highlighting the importance of data preprocessing in spectral analysis. Synthetic spectral augmentation using Gaussian noise revealed that while SVM and ANN exhibited near-perfect performance, SVM demonstrated quantifiable reliability. This study also demonstrates SVM as the most sensitive and reliable method for detecting and quantifying moisture content in durian husk. This research contributes novel insights to biomass analysis, highlighting the benefits of integrating NIRS and feasibility of explainable machine learning techniques to identify water related spectral parameters to advance aquaphotomics, thereby advancing rapid and accurate biomass characterization. • NIRS coupled with SVM demonstrates potential for quick moisture content analysis in durian husk biomass. • Explainable machine learning can be used to identify and characterize the water spectral pattern. • Traditional random sampling outperforms KS and SPXY methods when calibration data is limited. • SHAP provides clear explanations for model differences, enhancing the selection and transparency of SVM, ANN, XGB models. Support vector machine ; Water content ; Preprocessor ; Husk ; Least squares support vector machine ; Near-infrared spectroscopy ; Biomass (ecology) ; Partial least squares regression ; Boosting (machine learning) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jiaqi Liang | Jiaxi Liu | Yifan Jing | Huicheng Yang | Shengming Zhou | Yongpeng Xia | Fen Xu | Lixian Sun | Pengru Huang
Source: Materials & Design (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Nonequilibrium molecular dynamics study on thermal conductivity of spinel AB2O4 • The development of a deep learning potential with high adaptability and accuracy for hexagonal spinel oxides. • A study of the effects of elements, temperature, grain boundaries, and antisite defects on thermal conductivity. • The finding offers valuable theoretical guidance for the development of advanced thermal barrier coating materials. Spinel oxides with the formula AB 2 O 4 are widely used in the design of materials for thermal barrier coatings, due to their low thermal expansion, thermal conductivity, and hydrogen diffusion. This study aims to identify spinel oxide ceramics that exhibit both low thermal conductivity and low thermal expansion by developing a machine learning potential for spinel oxides containing cation elements such as Cu, Al, Cr, Co, Ni, and Fe. Employ machine learning to fit DFT calculation data to obtain interatomic potential functions for molecular dynamics simulations. Non-equilibrium molecular dynamics simulations were conducted to investigate the thermal conductivity and thermal expansion of these materials, focusing on the influence of temperature, elemental composition, grain boundaries, and antisite defects. The results reveal that the high-entropy oxide (CuNiAlCrCoFe) 0.5 O 4 demonstrates a lower thermal conductivity and thermal expansion coefficient compared to other spinel oxides with different compositions, making it a promising candidate for thermal barrier coating applications. Moreover, in practical applications, the thermal conductivity of spinel oxides generally decreases as the temperature increases. For spinel oxides with the same composition, the thermal conductivity can be further reduced by the formation of grain boundaries and antisite defects during the material preparation process. Materials science ; Spinel ; Non-equilibrium thermodynamics ; Thermal conductivity ; Molecular dynamics ; Dynamics (music) ; Thermal ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Engineering physics ; Metallurgy ; Composite material ; Computational chemistry ; Physics ; Chemistry Thermal and Kinetic Analysis ; Nuclear materials and radiation effects ; X-ray Diffraction in Crystallography
Authors: Jingfeng Wang | Zihe Li | Mengke Li | Wenyan Jiao | Yufeng Luo | Huijun Liu | Ying Fang
Source: Materials Today Physics (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Accurate prediction of band gap of two-dimensional monolayer materials via transfer learning Materials science ; Monolayer ; Transfer of learning ; Nanotechnology ; Artificial intelligence ; Computer science 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Muhammad Aasım | Amjad Ali | Furkan Ulaş | Muhammed Tatar | Waqas Liaqat | Muhammad Tanveer Altaf | Mehdi Rahimi
Source: Beni-Suef University Journal of Basic and Applied Sciences (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

Myconanoparticles: a sustainable strategy for agricultural applications and phytopathogen management Nanotechnology presents a sustainable paradigm for modern agriculture, utilizing nanoscale materials to combat plant diseases, enhance crop growth, and minimize environmental impact. Among the various synthesis methods, fungi-based nanoparticles (NPs) have garnered significant attention due to their cost-effectiveness, eco-friendliness, and versatility compared to conventional physicochemical approaches. Fungi possess unique metabolic pathways that facilitate the biosynthesis of NPs with distinct physicochemical properties, making them highly applicable to agriculture. These fungi-derived NPs exhibit strong and broad-spectrum antimicrobial activity against major phytopathogens, including Fusarium oxysporum, Rhizoctonia solani, and Sclerotinia sclerotiorum. This review comprehensively details the biological mechanisms and agricultural applications of these NPs. Furthermore, it explores the integration of machine learning (ML) to advance the field, demonstrating how ML models optimize synthesis parameters, predict NP stability, and enhance antimicrobial efficacy, thereby paving the way for scalable and standardized production. An advanced bibliometric analysis (2000–2024) underscores the rapid expansion of this research domain, with India emerging as a leading contributor, reflecting a global shift toward sustainable nanotechnology for plant disease management. By synthesizing insights from fungal nanobiotechnology, AI-driven optimization, and global research trends, this review provides a forward-looking perspective on developing and implementing advanced, eco-friendly strategies for sustainable agriculture. Agriculture ; Applications of nanotechnology ; Sustainability ; Sustainable agriculture ; Sustainable development ; Scalability Nanoparticles: synthesis and applications ; Plant-Microbe Interactions and Immunity ; Microbial Natural Products and Biosynthesis
Authors: Yanjing He | Rongmei Han | Min Wang | Zhengqing Zhang | Chongli Zhong
Source: Journal of Membrane Science (article, 2025-05-09)
Field: Chemistry — Inorganic Chemistry

Machine learning-guided design of metal-organic frameworks for efficient H2S/CH4 separation Separation (statistics) ; Metal-organic framework ; Membrane ; Gas separation ; Computer science ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Machine learning ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Gas Sensing Nanomaterials and Sensors
Authors: Syed Abdul Moiz Hashmi | Waqar Hussain | Hina Bhanbhro | Haider I. Rasool
Source: Comments on Inorganic Chemistry (article, 2025-10-14)
Field: Chemistry — Inorganic Chemistry

Engineering Hierarchical Zeolites: Mechanistic Insights, Predictive Framework, and Deployment Pathways Hierarchical zeolites, composed of microporous frameworks integrated with secondary mesoporosity that mitigate diffusion limitations and improve accessibility to active catalytic sites. The presence of mesopores enhances diffusion, facilitates reactant access, and improves resistance to deactivation, leading to superior performance in CO₂ capture, hydrocarbon separation, catalytic cracking, and biomass conversion. Numerous synthesis strategies have been developed, including hard and soft templating, desilication, one-pot crystallization, and 3D printing. Despite these advances, a clear framework connecting structural features to functional outcomes remains underdeveloped. This review examines diffusion mechanisms in porous media and evaluates how key structural descriptors such as mesopore size, pore connectivity, micropore volume, and external surface area influence adsorption, catalytic activity, and long-term material stability. Based on this analysis, we propose a structure-function framework that enables reproducible, semi-quantitative comparisons across studies. This review also discusses emerging research directions such as machine learning-guided synthesis, operando monitoring techniques, and modular integration approaches. Together, these insights pave the way for the development of scalable, adaptable chemical systems and support the rational design of next-generation hierarchical zeolites for industrial applications. Software deployment ; Key (lock) ; Mechanism (biology) ; Context (archaeology) ; Variety (cybernetics) Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Hydrodesulfurization Studies
Authors: Xiaoqian Gao | Li Chen | Rui Liu | Jing Wan | Huasong Qin | Yilun Liu
Source: Extreme Mechanics Letters (review, 2025-07-24)
Field: Materials Science — Ceramics and Composites

Multiscale structure-mechanical property relationship and data-driven design of ultra-high temperature ceramics: A review Materials science ; Ceramic ; Property (philosophy) ; Mechanical engineering ; Nanotechnology ; Composite material ; Engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Injection Molding Process and Properties
Authors: Chao Wang | Fangbao Jiao | Shuhai Zhang | Ruijun Gou | Hongzhen Li | Yu Liu | Xin Zhou
Source: Separation and Purification Technology (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced morphology and size control of 1,3,5-trinitro-1,3,5-triazinane (RDX) in Nanofiltration-assisted cooling crystallization Nanofiltration ; Crystallization ; Chemical engineering ; Morphology (biology) ; Materials science ; Chemistry ; Membrane ; Engineering ; Geology Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Energetic Materials and Combustion
Authors: Lorena Alzate-Vargas | Kashi N. Subedi | Nicholas Lubbers | M. Cooper | Roxanne Tutchton | Tammie Gibson | Richard A. Messerly
Source: Machine Learning Science and Technology (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Toward machine learning interatomic potentials for modeling uranium mononitride Abstract Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage. Uranium ; Interatomic potential ; Materials science ; Computer science ; Chemistry ; Computational chemistry ; Metallurgy ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Cong Nga Ha | Phuong Viet Pham | Truong Son Hy
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-15)
Field: Materials Science — Materials Chemistry

LANTERN: Leveraging Large Language Models and Transformers for Enhanced Molecular Interactions Abstract Understanding molecular interactions such as Drug-Target Interaction (DTI), Protein-Protein Interaction (PPI), and Drug-Drug Interaction (DDI) is critical for advancing drug discovery and systems biology. However, existing methods often struggle with scalability due to the vast chemical and biological space and suffer from limited accuracy when capturing intricate biochemical relationships. To address these challenges, we introduce LANTERN (Leveraging Large LAN guage Models and T ransformers for E nhanced molecula R interactio N s), a novel deep learning framework that integrates Large Language Models (LLMs) with Transformer-based architectures to model molecular interactions more effectively. LANTERN generates high-quality, context-aware embeddings for drug and protein sequences, enabling richer feature representations and improving predictive accuracy. By leveraging a Transformer-based fusion mechanism, our framework enhances scalability by efficiently integrating diverse interaction data while maintaining computational feasibility. Experimental results demonstrate that LANTERN achieves state-of-the-art performance on multiple DTI and DDI benchmarks, significantly outperforming traditional deep learning approaches. Additionally, LANTERN exhibits competitive performance on challenging PPI tasks, underscoring its versatility across diverse molecular interaction domains. The proposed framework offers a robust and adaptable solution for modeling molecular interactions, efficiently handling a diverse range of molecular entities without the need for 3D structural data and making it a promising framework for foundation models in molecular interaction. Our findings highlight the transformative potential of combining LLM-based embeddings with Transformer architectures, setting a new standard for molecular interaction prediction. The source code and relevant documentation are available at: https://github.com/HySonLab/LANTERN . Lantern ; Transformer ; Computer science ; Engineering ; Electrical engineering ; Chemistry ; Voltage Machine Learning in Materials Science ; Topic Modeling
Authors: Elizaveta I. Yakovenko | Iurii M. Nevolin | Anatoliy A. Chasovskikh | Artem Mitrofanov | Vadim Korolev
Source: Journal of Chemical Information and Modeling (article, 2025-02-10)
Field: Chemistry — Inorganic Chemistry

Data-Driven Prediction of Structures of Metal–Organic Frameworks Crystal structure prediction (CSP) has proven to be an effective route for the discovery of new materials. Nonetheless, the <i>ab initio</i> techniques employed for the CSP of metal-organic frameworks (MOFs) cannot be scaled to a high-throughput mode. Here, we propose a data-driven method for addressing the current needs of computational MOF discovery. Specifically, coarse-grained neural networks were implemented to predict the underlying net topology. The models showed satisfactory performance, which was next enhanced via the limitation of the applicability domain. Computer science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Haotian Chen | Enno Kätelhön | Yuanyuan Lu | Jun Cheng | Zhong-Qun Tian | Richard G. Compton
Source: eScience (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

30 years of AI for electrocatalysis: Where we are and what’s next? Artificial Intelligence (AI) has evolved over the past three decades from the initial pioneering stage to become a transformative force in electrocatalytic research yet is far from realizing its full potential. This review traces foundational applications of AI to electrocatalysis in the 1990s to highlight the integration of AI into the full catalyst development workflow in the last five years, from material design and synthesis to characterization and performance evaluation, and ultimately to knowledge extraction. Emphasis is placed on critical but often partially recognized or neglected bottlenecks: the scale gap between atomistic simulations and macroscopic performance, inverse electrocatalyst design, physical consistency and interpretability of machine learning models, automated experiments, and the scarcity of high-quality, well validated experimental data. Cutting edge solutions such as exascale computing, machine learning interatomic potentials (MLIPs), physics-informed machine learning (PIML), generative models (variational autoencoders, diffusion models, and large language models), and FAIR-compliant data are discussed. This review highlights that the progress of AI for electrocatalysis is inherently data-centric, driven by advances in data-quality, FAIR-compliant infrastructure, and data-driven workflows that connect experiment, simulations, and machine learning. Beyond technical perspectives, this review also emphasizes the importance of interdisciplinary collaboration, industrial relevance, and cautions in respect of hyping. By identifying challenges and highlighting emerging breakthroughs, this work offers a roadmap for advancing AI-driven electrocatalysis towards more predictive, interpretable, and scalable discovery. • Traces 30 years of AI development and integration in electrocatalysis. • Identify five bottlenecks limiting AI-enabled catalyst discovery. • Discusses MLIPs, PIML, and generative AI for design and mechanistic insights. • Highlights data-centric progress driven by accessible and consistent data. • Calls for interdisciplinary, industrial, and responsible AI integration. Workflow ; Electrocatalyst ; Generative grammar ; Interpretability ; Transformative learning ; Consistency (knowledge bases) ; Petabyte Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Jorge Suárez-Recio | Pablo M. Piaggi | F. J. Domínguez-Gutiérrez | R. González-Arrabal | R. Iglesias
Source: Communications Materials (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Early stages of self-healing at tungsten grain boundaries from ab initio machine learning simulations Nanostructured tungsten has been reported as a possible alternative plasma–facing material due to its potential ability to self–heal radiation-induced defects, a property that is attributed to its high density of grain boundaries (GB). Here, we study the initial stages of self-healing at tungsten interfaces with molecular dynamics simulations driven by a machine-learning interatomic potential tailored to one of the most common GBs found in experiments. Our model accurately reproduces the ab initio potential energy surface derived from density functional theory (DFT) calculations and outperforms previously reported empirical and machine learning interatomic potentials in predicting defect energetics. The simulations reveal low-temperature defect migration to GBs driven by rapid dumbbell-like ordering and subsequent accommodation along GB grooves. In contrast to empirical potentials, which predict unexpected GB degradation at high temperatures after defect migration, our model maintains stable GB motifs over the investigated temperature range. The temperature-dependent defect counts, evaluated using an Arrhenius-like fit, yield an average interstitial migration energy of 0.048 eV, in agreement with experiment. This work underscores the capabilities of ab initio machine learning simulations in accurately modeling defect-GB interactions and highlights their potential to contribute to the development of radiation tolerant materials. Nanostructured tungsten is being investigated for use as a plasma-facing material in the nuclear industry, due to the possibility of self-healing of radiation-induced defects by the high grain boundary content. Here, a machine-learning interatomic potential is used to investigate the early stages of this process. Interatomic potential ; Ab initio ; Grain boundary ; Tungsten ; Molecular dynamics ; Ab initio quantum chemistry methods ; Density functional theory Fusion materials and technologies ; Nuclear materials and radiation effects ; Nuclear Materials and Properties
Authors: Yizhou Lu | Blas P. Uberuaga | Samrat Choudhury
Source: Molecules (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y<sub>2</sub>O<sub>3</sub> interface-an important model interface for cladding applications in nuclear fission reactors-by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (ESeg) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining ESeg. Moreover, the segregation behavior at a specific Fe/Y<sub>2</sub>O<sub>3</sub> interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y<sub>2</sub>O<sub>3</sub> interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations. Density functional theory ; Oxide ; Interface (matter) ; Materials science ; Metal ; Orientation (vector space) ; Cladding (metalworking) ; Chemical physics ; Computer science ; Computational chemistry ; Chemistry ; Geometry ; Metallurgy ; Mathematics Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Advanced Memory and Neural Computing