jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hajar Lamouadene | Majid EL Kassaoui | M. El Yadari | A. El Kenz | A. Benyoussef
Source: Chemical Physics (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Exploring modeling techniques for predicting band gaps of Doped-ZnO: A Machine learning approach Chemistry ; Doping ; Band gap ; Nanotechnology ; Machine learning ; Optoelectronics ; Computer science ; Physics Machine Learning in Materials Science ; Machine Learning and ELM ; ZnO doping and properties
Authors: Yuanbin Liu | Joe D. Morrow | Christina Ertural | Natascia L. Fragapane | John L. A. Gardner | Aakash Ashok Naik | Yuxing Zhou | Janine George | Volker L. Deringer
Source: Nature Communications (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

An automated framework for exploring and learning potential-energy surfaces Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex ('automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO<sub>2</sub>, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning in computational materials science. Computer science ; Data science ; Human–computer interaction Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electron and X-Ray Spectroscopy Techniques
Authors: Samar H. Elagamy | Khanda F.M. Amin | Reem H. Obaydo | Hayam M. Lotfy
Source: Talanta Open (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Review of green synthesized carbon dots: Applications in pharmaceutical drug sensing • This review provides a comprehensive overview of GSCD synthesis from renewable, biomass-derived precursors. • This review presents the applications of GSCDs in pharmaceutical sensing. • This review introduces the integration of machine learning (ML) strategies to optimize synthesis parameters and address precursor variability. • This review critically examines current challenges and future directions in GSCD synthesis. Green-synthesized carbon dots GSCDs which are derived from natural, renewable sources such as biomass and bio-derived chemicals, have emerged as a promising class of eco-friendly nanomaterials. Owing to their unique physicochemical properties including strong fluorescence, high water solubility, low toxicity, biocompatibility, and excellent photostability. GSCDs offer significant advantages in biomedical and pharmaceutical applications. Their potential for sensitive and selective drug sensing in dosage forms, biological fluids, and environmental samples has gained increasing attention. Green synthesis approaches, such as hydrothermal treatment, microwave irradiation, ultrasonic methods, and chemical oxidation, provide a sustainable alternative to conventional synthetic routes. This review highlights various natural precursors and green synthetic methods used to produce GSCDs, and discusses their advantages in pharmaceutical drug detection. Additionally, it explores the role of machine learning (ML) in optimizing the synthesis of GSCDs. Furthermore, this review identifies current research gaps and proposes future directions to advance the application of GSCDs in pharmaceutical analysis. Pharmaceutical industry ; Drug delivery ; Renewable energy ; Green chemistry ; Carbon fibers ; Drug Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications
Authors: Shuhao Zhang | R.I. Zubatyuk | Yinuo Yang | Adrián E. Roitberg | Olexandr Isayev
Source: Journal of Chemical Theory and Computation (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions under diverse conditions and the prediction of reaction properties, such as barrier heights. This work introduces ANI-1xBB, a novel ANI-based reactive ML potential trained on off-equilibrium molecular conformers generated through an automated bond-breaking workflow. ANI-1xBB significantly enhances the prediction of reaction energetics, barrier heights, and bond dissociation energies, surpassing those of conventional ANI models. Our results show that ANI-1xBB improves transition state modeling and reaction pathway prediction while generalizing effectively to pericyclic reactions and radical-driven processes. Furthermore, the automated data generation strategy supports the efficient construction of large-scale, high-quality reactive data sets, reducing reliance on expensive QM calculations. This work highlights ANI-1xBB as a practical model for accelerating the development of reactive machine learning potentials, offering new opportunities for modeling reaction phenomena. Organic molecules ; Computer science ; Data science ; Chemistry ; Nanotechnology ; Molecule ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: A. S. Abouhaswa | U. Perişanoğlu | Sevda Saltık | Neslihan Ekinci | M. H. Nasr | Sedanur Kalecik | Esra Kavaz Perişanoğlu
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Er2O3-Doped Lead Borate Glasses: Advanced Optical and Radiation Shielding Performance Abstract A new glass system with the composition 60B 2 O 3 + 30PbF 2 + (10−x)K 2 O + x Er 2 O 3 (x = 0 to 3 mol%) were synthesized using the melt-quenching technique and comprehensively analyzed to evaluate their structural, optical, mechanical, and radiation shielding properties. Increasing Er 2 O 3 concentration enhanced the density (from 4.260 to 4.89 g/cm 3 ) and reduced the molar volume (from 29.28 to 28.98 cm 3 /mol), indicating a denser and more compact glass matrix. Optical studies revealed increased UV absorbance, a red shift in the cutoff wavelength, and a reduction in the optical energy gap from 3.487 to 3.335 eV (direct transitions). Urbach energy values increased from 0.722 to 1.083 eV, signifying heightened structural disorder. Enhanced refractive index and extinction coefficients further underscored the glasses’ potential for optical applications. Mechanical analyses demonstrated a significant increase in all elastic moduli, including Young’s, bulk, and shear moduli, with Er 2 O 3 incorporation, indicating improved rigidity and mechanical stability. The radiation shielding performance of the glasses was assessed across photon energies of 0.015–15 MeV, incorporating both experimental data and machine learning (ML)-based predictions of mass attenuation coefficients (MAC). The ML model, developed using a neural network architecture, successfully predicted MAC values with high accuracy, demonstrating excellent agreement with XCOM-calculated results. Key shielding parameters, including half-value layer (HVL), effective atomic number (Z eff ), and buildup factors (EABF and EBF), improved significantly with higher Er 2 O 3 content. BPKE3 glass, with the highest Er 2 O 3 concentration, exhibited the best shielding efficiency, outperforming conventional shielding materials in terms of lower HVL and buildup factors, coupled with higher MAC and Z eff values. This study highlights the dual role of Er 2 O 3 -doped lead borate glasses as efficient optical and radiation shielding materials. Machine learning effectively predicts shielding parameters, aiding material optimization for applications in nuclear, medical, and industrial fields. Boron ; Radiation shielding ; Doping ; Materials science ; Electromagnetic shielding ; Lead (geology) ; Radiation ; Nuclear radiation ; Borate glass ; Optoelectronics ; Radiochemistry ; Optics ; Chemistry ; Composite material ; Nuclear physics ; Physics Radiation Shielding Materials Analysis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Nam Khanh Nguyen | Dae-Young Kim | Van Quan Phan | Minji Kim | Pangun Park | Junghyo Nah
Source: Carbon (article, 2025-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspectives on absorption-dominant electromagnetic interference shielding materials with MXene and carbon-based polymer composites Electromagnetic shielding ; Composite material ; Materials science ; Electromagnetic interference ; Carbon fibers ; Absorption (acoustics) ; Interference (communication) ; Polymer ; Composite number ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Andrea Angeletti | Luca Leoni | Dario Massa | Luca Pasquini | Stefanos Papanikolaou | Cesare Franchini
Source: npj Computational Materials (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion in magnesium using machine learning potentials: a comparative study Abstract Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab-initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning. By considering different temperatures and concentration regimes, we obtain hydrogen diffusion coefficients and activation energy values which align remarkably well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force-field allows the retrieval of DFT-level accuracy at a fraction of the computational cost. Magnesium ; Diffusion ; Hydrogen ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Thermodynamics ; Physics ; Metallurgy ; Organic chemistry Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Wantae Kim | Weon‐Gyu Lee | Hong-Eun An | Hiroyasu Furukawa | WooSeok Jeong | Sung‐Chul Kim | Jeffrey R. Long | Sohee Jeong | Jung‐Hoon Lee
Source: Chemical Engineering Journal (article, 2025-02-17)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted design of metal–organic frameworks for hydrogen storage: A high-throughput screening and experimental approach • MOF databases and ML techniques identify synthesizable MOF structures. • High-throughput screening finds promising MOF structures for hydrogen storage. • New vanadium-based MOF (V 3 (PET)) shows the high H 2 storage performance. • V 3 (PET) remains stable over 100 adsorption cycles. • ML accelerates discovery of efficient hydrogen storage materials. Various theoretical approaches, including big data and high-throughput screening techniques, have been explored in developing new materials due to their significant potential time-saving advantages. However, it remains a significant challenge to experimentally realize new materials that are predicted. In this study, we propose a novel materials design strategy that utilizes machine-learning (ML) techniques to predict new porous materials that show promise for hydrogen storage and are likely to be feasible to synthesize. By leveraging ML techniques and metal–organic framework (MOF) databases, we are able to predict the synthesizability of MOF structures. This is evidenced by the successful synthesis of a new vanadium-based MOF that exhibits excellent performance for cryogenic H 2 storage. Notably, the total gravimetric and volumetric H 2 uptakes are as high as 9.0 wt% and 50.0 g/L at 77 K and 150 bar. This ML-assisted materials design offers an efficient and promising approach for developing hydrogen storage materials. Throughput ; Hydrogen storage ; Process engineering ; High-throughput screening ; Metal-organic framework ; Computer science ; Biochemical engineering ; Materials science ; Nanotechnology ; Hydrogen ; Chemistry ; Engineering ; Organic chemistry ; Telecommunications ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Jiahui Xu | Yalong Song | Rui Zhao | Zhao Zhang | Can Hu | Long Wang | Wentao Li | Jianfei Xing | Xufeng Wang
Source: Frontiers in Sustainable Food Systems (article, 2025-02-27)
Field: Chemistry — Analytical Chemistry

Trend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024) Multispectral imaging technology uses sensors capable of detecting spectral information across various wavelength ranges to acquire multi-channel target data. This enables researchers to collect comprehensive biological information about the observed objects or areas, including their physical and chemical characteristics. Spectral technology is widely applied in agriculture for collecting crop information and predicting yield. Over the past decade, multispectral image acquisition and information extraction from plants have provided rich data resources for scientific research, facilitating a deeper understanding of plant growth mechanisms and ecosystem function. This article presents a bibliometric analysis of the relationship between multispectral imaging and crop yield prediction, reviewing past studies and forecasting future research trends. Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. Cluster and co-citation analyses revealed the developmental trajectory of multispectral yield estimation. Our bibliometric approach offers a novel perspective to understand the current status of multispectral technology in agricultural applications. This methodology helps new researchers quickly familiarize themselves with the field’s knowledge and gain a more precise understanding of development trends and research hotspots in the domain of multispectral technology for agricultural yield estimation. Multispectral image ; Visualization ; Yield (engineering) ; Computer science ; Remote sensing ; Data science ; Data mining ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Tao Chen | Peng Liu | Ge Yu | Xin Lei | Zhenhong Fan | Xiaoyu Zhu | Yangping Wen | Hassan Karimi-Maleh | Xiaoqiang Wang
Source: Chemical Engineering Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

XGBoost algorithm-guided synthesis of violet phosphorus/MoAlB-MBene nanozyme for portable wireless intelligent sensing of sesamol in different varieties of sesame seeds Sesamol ; Wireless ; Phosphorus ; Algorithm ; Computer science ; Telecommunications ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Organizational and Employee Performance ; Machine Learning and ELM
Authors: Chengbing Chen | Yutong Li | Rui Zhao | Zhoulin Liu | Zheyong Fan | Gang Tang | Zhiyong Wang
Source: Computer Physics Communications (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials Python (programming language) ; Workflow ; Bottleneck ; Visualization ; Graphical user interface ; Software ; Initialization ; Process (computing) ; Neuroevolution Machine Learning in Materials Science
Authors: Bin Han | Kuang Yu
Source: Nature Communications (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Refining potential energy surface through dynamical properties via differentiable molecular simulation Recently, machine learning potential (MLP) largely enhances the reliability of molecular dynamics, but its accuracy is limited by the underlying ab initio methods. A viable approach to overcome this limitation is to refine the potential by learning from experimental data, which now can be done efficiently using modern automatic differentiation technique. However, potential refinement is mostly performed using thermodynamic properties, leaving the most accessible and informative dynamical data (like spectroscopy) unexploited. In this work, through a comprehensive application of adjoint and gradient truncation methods, we show that both memory and gradient explosion issues can be circumvented in many situations, so the dynamical property differentiation is well-behaved. Consequently, both transport coefficients and spectroscopic data can be used to improve the density functional theory based MLP towards higher accuracy. Essentially, this work contributes to the solution of the inverse problem of spectroscopy by extracting microscopic interactions from vibrational spectroscopic data. Refining (metallurgy) ; Differentiable function ; Surface (topology) ; Computer science ; Potential energy surface ; Molecular dynamics ; Energy (signal processing) ; Statistical physics ; Biological system ; Physics ; Chemistry ; Computational chemistry ; Molecule ; Mathematics ; Biology ; Physical chemistry ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum, superfluid, helium dynamics
Authors: Ye Cheng | Takuma Araki | Naofumi Kamimura | Eiji Masai | Masaya Nakamura | Sergei Manzhos | Tsuyoshi Michinobu
Source: ACS Applied Materials & Interfaces (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning 2-Pyrone-4,6-dicarboxylic acid (PDC) can be produced on a large scale from lignin by transformed bacteria, and its use as a bifunctional monomer to synthesize biomass-based polymers has been reported. Recently, excellent adhesive properties of the resulting biomass-based polymers were also reported, but their performance has not yet been optimized. In this study, we focus on improving the adhesive properties of PDC-based polyurethanes (PUs) by combining experiments and machine learning (ML). We synthesized an initial data set of 25 adhesive samples from different polyols and isocyanates with different isocyanate-to-hydroxyl ratios (<i>r</i>). Adhesive strengths were measured after hot-pressing at varying temperatures (<i>T</i><sub>heat</sub>, °C) and durations (<i>t</i><sub>heat</sub>, h), following a Taguchi L25 orthogonal design. Gaussian process-based Bayesian optimization (BO) was employed to identify an optimal PDC-based PU adhesive as a function of polyol type, isocyanate type, <i>r</i> ratio, heating temperature, and time with an improved adhesive strength of 10.04 ± 1.26 MPa after only five iterations. This approach highlights the effectiveness of BO in guiding experimental conditions for an enhanced performance. Random Forest regression was also used as an alternative ML approach and supported the conclusions. Overall, this study demonstrates the potential of the BO in accelerating the development and optimization of novel adhesive materials. Materials science ; Polyurethane ; Adhesive ; Biomass (ecology) ; Composite material ; Polymer science ; Nanotechnology ; Process engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Michael E. Miszczak | Kabiruddin Khan | Pernille Høgh Danielsen | Keld Alstrup Jensen | Ulla Vogel | Roland C. Grafström | Agnieszka Gajewicz
Source: Journal of Nanobiotechnology (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Dynamic QSAR modeling for predicting in vivo genotoxicity and inflammation induced by nanoparticles and advanced materials: a time-dose-property/response approach Predicting the health risks of nanoparticles (NPs) and advanced materials (AdMa) is a critical challenge. Due to the complexity and time-consuming nature of experimental testing, there is a reliance on in silico methods such as quantitative structure-activity relationship (QSAR), which, while effective, often fail to capture the dynamic nature of material activity over time-essential for reliable risk assessment. This study develops dynamic QSAR models using machine learning to predict toxicological responses, such as inflammation and genotoxicity, following pulmonary exposure to 39 AdMa across various post-exposure time points and dose levels. By incorporating exposure time, administered dose, and material properties as independent variables, we successfully developed time-dose-property/response models capable of predicting AdMa-induced in vivo genotoxicity in bronchoalveolar lavage fluid cells, lung and liver tissue, and inflammation in terms of neutrophil influx into the lungs of mice. Key factors driving AdMa-induced toxicity were identified, including exposure dose, post-exposure duration time, aspect ratio, surface area, reactive oxygen species generation, and metal ion release. The time-dose-property/response modeling paradigm presented here provides a practical and robust approach for predicting in vivo genotoxicity and inflammation and supports the comprehensive risk assessment of morphologically diverse AdMa. Quantitative structure–activity relationship ; Genotoxicity ; In vivo ; Inflammation ; Chemistry ; Nanoparticle ; Pharmacology ; Nanotechnology ; Materials science ; Stereochemistry ; Medicine ; Organic chemistry ; Toxicity ; Biology ; Immunology ; Biotechnology Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Microplastics and Plastic Pollution
Authors: Nokubonga P. Makhanya | Michael Kumi | Charles Mbohwa | Bilainu Oboirien
Source: Journal of Energy Storage (review, 2025-01-13)
Field: Chemistry — Inorganic Chemistry

Application of machine learning in adsorption energy storage using metal organic frameworks: A review Adsorption ; Metal-organic framework ; Energy storage ; Process engineering ; Computer science ; Materials science ; Chemistry ; Engineering ; Physics ; Thermodynamics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials ; Covalent Organic Framework Applications