jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xingze Geng | Jianing Gu | Gaowu Qin | Lin‐Wang Wang | Xiangying Meng
Source: The Journal of Chemical Physics (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields Machine Learning Force Fields (MLFFs) require ongoing improvement and innovation to effectively address challenges across various domains. Developing MLFF models typically involves extensive screening, tuning, and iterative testing. However, existing packages based on a single mature descriptor or model are unsuitable for this process. Therefore, we developed a package named ABFML, based on PyTorch, which aims to promote MLFF innovation by providing developers with a rapid, efficient, and user-friendly tool for constructing, screening, and validating new force field models. Moreover, by leveraging standardized module operations and cutting-edge machine learning frameworks, developers can swiftly establish models. In addition, the platform can seamlessly transition to the graphics processing unit environments, enabling accelerated calculations and large-scale parallel simulations of molecular dynamics. In contrast to traditional from-scratch approaches for MLFF development, ABFML significantly lowers the barriers to developing force field models, thereby expediting innovation and application within the MLFF development domains. Computer science ; Expediting ; Scratch ; Field (mathematics) ; Graphics ; Process (computing) ; Graphics processing unit ; Enhanced Data Rates for GSM Evolution ; Iterative and incremental development ; Artificial intelligence ; Machine learning ; Human–computer interaction ; Software engineering ; Systems engineering ; Engineering ; Parallel computing ; Operating system Machine Learning in Materials Science ; Software Engineering Research ; Fuel Cells and Related Materials
Authors: Ruiqing Sun | Yujuan Xie | Xinpeng Zhou | Qiwen Zhan | Haoran Fan | Xiaorong Sun | Dongsheng Bai | Yangyang Wen | Hongyan Li | Jing Wang | Baoguo Sun
Source: ACS Applied Materials & Interfaces (article, 2025-12-02)
Field: Chemistry — Spectroscopy

Machine Learning-Assisted Ratiometric Fluorescence Electrospun Nanofiber Films for Portable and Intelligent Monitoring of Multiple Alkylresorcinol Homologues in Whole Wheat Foods The intelligent authentication of whole wheat products remains a significant challenge due to the difficulty in simultaneously monitoring multiple alkylresorcinol (AR) homologues within complex food matrices. To address this, we have developed a novel sensing platform integrating machine learning (ML) algorithms with advanced ratiometric fluorescence (FL) materials. The core component is a dual-emission g-C<sub>3</sub>N<sub>4</sub>/Ru, leveraging the blue fluorescence from g-C<sub>3</sub>N<sub>4</sub> nanosheets as the analytical signal and the red fluorescence from [Ru(bpy)<sub>3</sub>]<sup>2+</sup> as an internal reference. Upon interaction with AR homologues, a visible color change from blue-violet to pink occured due to multiple synergistic effects of IFE, a-PET, electrostatic attraction, and π-π interactions. The system exhibited exceptional sensitivity for quantitative detection of five AR homologues (C17:0, C19:0, C21:0, C23:0, C25:0) within the concentration range of 0-60 μg·mL<sup>-1</sup>, achieving ultralow limit of detections (LODs) ranging from 2.1 to 9.9 ng·mL<sup>-1</sup>. For precise and portable quantitative analysis, a random forest-back-propagation neural network (RF-BPNN) algorithm-assisted FL electrospun film (RuCN PAN NFs) was employed, enabling reliable on-site monitoring. RGB features were extracted using the OpenCV library, and 252 samples were evenly divided through stratified sampling to maintain data balance, yielding exceptional prediction accuracy (<i>R</i><sup>2</sup> = 0.9822) and robust performance by training with the RF-BPNN. Application to commercial wheat samples confirms the system's utility for AR detection and whole wheat authentication. This integrated approach overcomes traditional analytical limitations by combining ML algorithms with advanced materials, enabling intelligent, rapid, and on-site detection of AR homologues. It provides a promising tool for verifying the authenticity of whole wheat products, offering significant potential for food safety monitoring and quality control applications. RGB color model ; Fluorescence ; Detection limit ; SIGNAL (programming language) ; Nanofiber ; Sampling (signal processing) ; Range (aeronautics) Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications ; Spectroscopy and Chemometric Analyses
Authors: Jian-Kuen Wu | Peijia Lin | Zheshuai Lin | Bing Zhang | Yanchuan Guo
Source: Chemical Engineering Journal (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

d-band center-guided high-fidelity generative model for inverse materials design Adsorption ; Lattice (music) ; Center (category theory) ; Group (periodic table) ; Artificial neural network ; Constraint (computer-aided design) ; Inverse Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Heusler alloys: electronic and magnetic properties
Authors: Huan-huan Fan | Xiangling Wang | Jie Feng | Wenying Li
Source: Separations (article, 2025-01-24)
Field: Chemistry — Spectroscopy

Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography A comprehensive understanding of the compositions and physicochemical properties of coal-based liquids is conducive to the rapid development of multipurpose, high-performance, and high-value functional chemicals. However, because of their complex compositions, coal-based liquids generate two-dimensional gas chromatography (GC × GC) chromatograms that are very complex and very time consuming to analyze. Therefore, the development of a method for accurately and rapidly analyzing chromatograms is crucial for understanding the chemical compositions and structures of coal-based liquids, such as direct coal liquefaction (DCL) oils and coal tar. In this study, DCL oils were distilled and qualitatively analyzed using GC × GC chromatograms. A deep-learning (DL) model was used to identify spectral features in GC × GC chromatograms and predominantly categorize the corresponding DCL oils as aliphatic alkanes, cycloalkanes, mono-, bi-, tri-, and tetracyclic aromatics. Regional labels associated with areas in the GC × GC chromatograms were fed into the mask-region-based convolutional neural network’s (Mask R-CNN’s) algorithm. The Mask R-CNN accurately and rapidly segmented the GC × GC chromatograms into regions representing different compounds, thereby automatically qualitatively classifying the compounds according to their spots in the chromatograms. Results show that the Mask R-CNN model’s accuracy, precision, recall, F1 value, and Intersection over Union (IoU) value were 93.71%, 96.99%, 96.27%, 0.95, and 0.93, respectively. DL is effective for visually comparing GC × GC chromatograms to analyze the compositions of chemical mixtures, accelerating GC × GC chromatogram interpretation and compound characterization and facilitating comparisons of the chemical compositions of multiple coal-based liquids produced in the coal and petroleum industry. Applying DL to analyze chromatograms improves analysis efficiency and provides a new method for analyzing GC × GC chromatograms, which is important for fast and accurate analysis. Coal ; Gas chromatography ; Chromatography ; Chemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Mineral Processing and Grinding
Authors: Guanjian Cheng | Wan‐Jian Yin
Source: The Journal of Physical Chemistry Letters (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

De Novo Inverse Design Superhard C–N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective Optimization Algorithm A major challenge in the field of superhard materials is the identification of compounds with a hardness exceeding that of diamond. In this study, we developed a variable-composition inverse material design (VC-IMD) approach for designing C-N superhard materials. In this approach, an improved multiobjective optimization algorithm is introduced, utilizing structure similarity constraint to prevent convergence toward local minima. Combined with active learning, it trains global machine learning interatomic potentials (<i>g</i>-MLIPs) while exploring target materials. By comparing several <i>g</i>-MLIPs and selecting the best, the resulting <i>g</i>-MLIPs achieved reasonable precision within three iterations. Through multiple searches, 38 novel and stable C-N superhard materials not present in major computational materials databases were identified. Notably, the material C<sub>3</sub>(<i>P</i>6<sub>4</sub>22) with a hardness of 97.4 GPa was discovered, potentially exceeding that of diamond (94.0 GPa). This approach provided a new pathway for materials design with target properties. Inverse ; Optimization algorithm ; Algorithm ; Computer science ; Interatomic potential ; Multi-objective optimization ; Materials science ; Mathematical optimization ; Computational chemistry ; Chemistry ; Mathematics ; Machine learning ; Molecular dynamics ; Geometry Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced materials and composites
Authors: Sima Rezvantalab | Sara Mihandoost | Roger M. Pallares | Fabian Kießling
Source: ACS Omega (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Sequential EXtreme Gradient Boosting-Based Descriptor Reduction for Size Prediction of Zwitterionic Polymer-Based Nanoparticles An approach based on machine learning (ML) is applied to investigate how structural descriptors affect the size of zwitterionic polymers (ZP) used in drug delivery systems (DDSs). This study examined the structural characteristics of components of ZP-based DDSs to determine how they contribute to the self-assembly phenomenon and, consequently, the final size of such DDSs. A new data set was curated through an extensive literature review, incorporating a wide range of descriptors related to molecular structures and pH. With each molecule initially characterized by 312 descriptors, reducing this extensive set to a manageable number emerged as a crucial step in the research process. To tackle this challenge, we introduced an innovative descriptor reduction strategy based on eXtreme Gradient Boosting (XGB), which we termed Sequential XGB (SXGB). This method was designed to systematically and efficiently identify the most significant descriptors while minimizing information loss. After applying the SXGB reduction process, the data set was streamlined to just 11 key descriptors, which were then used for further analysis. Among these, pH stood out as the most influential parameter, playing a pivotal role in the outcomes of the study. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) were employed to interpret a set of six local samples, allowing for the analysis of the influence exerted by each chosen descriptor. To enhance the robustness and generalizability of the model, we implemented a data augmentation process that combined both synthetic and original data sets for training and testing. This approach ensured that the model was exposed to a diverse range of scenarios, improving its ability to handle variability and uncertainty. In comparison with other evaluated models (supporting vector regression, random forest), the SXGB model demonstrated excellent performance in predicting zwitterionic polymer-based nanoparticle (NPs) size, achieving an accuracy (<i>R</i> <sup>2</sup>) of 84.2% and 80.9% in the training and testing sections, respectively. Boosting (machine learning) ; Nanoparticle ; Reduction (mathematics) ; Polymer ; Materials science ; Artificial intelligence ; Computer science ; Gradient boosting ; Biological system ; Nanotechnology ; Mathematics ; Biology ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Haokun Ti | Lianzhong Yang | Weihao Yan | Yongdong Chen | Yang Fu | Yujing Yue | Yunfei Sun | Xia Li | Yanqing Niu | Shi Li
Source: Process Safety and Environmental Protection (article, 2025-10-01)
Field: Chemistry — Inorganic Chemistry

Structure-based machine learning with standardized feature selection for screening MOFs with high ammonia capture capacity Interpretability ; Feature selection ; Identification (biology) ; Feature (linguistics) ; Process (computing) ; Transferability ; Stability (learning theory) ; Variance (accounting) Metal-Organic Frameworks: Synthesis and Applications ; Sulfur Compounds in Biology ; Chemical Synthesis and Characterization
Authors: Kang Li | Xiaoxue Wang | Huidi Tang | Feifan Wang | Zhenzhen Qu | Xiaojie Wang | Xiang Li | Xiaoqing Cao
Source: Biology (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Coixol Protects Against Acute Kidney Injury by Reducing Cell Senescence Acute kidney injury (AKI) is associated with increased in-hospital mortality, yet effective therapeutic agents remain limited. Coixol, a polyphenolic compound derived from Coix, possesses anti-inflammatory properties, but its role in AKI remains unclear. In this study, we demonstrate that Coixol exerts protective effects against ischemia/reperfusion (I/R)-induced AKI by alleviating cellular senescence. Coixol treatment significantly reduced serum creatinine (SCr) and blood urea nitrogen (BUN) levels and decreased the expression of KIM1 and NGAL. RNA sequencing and validation experiments revealed that Coixol suppressed cellular senescence in AKI. Through a weighted gene co-expression network analysis and machine learning, we identified Plaur as a key target of Coixol, which was further validated using RNA-seq data. Notably, Plaur overexpression in AKI mice diminished the protective effects of Coixol, confirming its crucial role. Additionally, molecular docking and molecular dynamics simulations demonstrated strong binding affinity between Coixol and Plaur. These findings highlight Coixol as a promising renal protective agent targeting Plaur and cellular senescence in AKI. Acute kidney injury ; Senescence ; Biology ; Creatinine ; Blood urea nitrogen ; Pharmacology ; Kidney ; Renal ischemia ; Bioinformatics ; Reperfusion injury ; Biochemistry ; Ischemia ; Internal medicine ; Cell biology ; Medicine ; Endocrinology Nanoparticles: synthesis and applications ; Vitamin C and Antioxidants Research ; S100 Proteins and Annexins
Authors: Jiahui Ma | Xiuli Xu | Yunhe Hong | Yun Xie | Xuesong Feng | Xiujuan Wang | Yuan Zhang | Feng Zhang
Source: Food Chemistry (article, 2025-06-21)
Field: Chemistry — Analytical Chemistry

Advancing freshness classification of freshly squeezed fruit juice via integrated multivariate analysis and machine learning approaches Fruit juice ; Multivariate statistics ; Multivariate analysis ; Artificial intelligence ; Machine learning ; Food science ; Computer science ; Pattern recognition (psychology) ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Rodrigo A. F. Alves | H. Rodrigues | José A.S. Laranjeira | Fábio Lúcio Lópes de Mendonça | Alysson Martins Almeida Silva | Júlio R. Sambrano | Luiz Antônio Ribeiro
Source: FlatChem (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

PolyRingene: A novel 2D carbon allotrope explored via first-principles and machine learning interatomic potentials Phonon ; Density functional theory ; Interatomic potential ; Anisotropy ; Ab initio ; Ab initio quantum chemistry methods ; Lattice (music) ; Molecular dynamics ; Electronic structure Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Shujie Fan | Philip E. Mason | Víctor Cruces Chamorro | B. Shanks | Hector Martinez‐Seara | Pavel Jungwirth
Source: ChemRxiv (preprint, 2025-06-12)
Field: Chemistry — Spectroscopy

Charge Scaling Force Field for Biologically Relevant Ions Utilizing a Global Optimization Method Charge scaling, also denoted as the electronic continuum correction, has proven to be an efficient method of effectively including electronic polarization in force field molecular dynamics simulations without additional computational costs. However, scaling charges in existing force fields, fitted at least in part to experimental data, leads to inconsistencies such as overscaling. We have, therefore, recently developed a 4-site water model consistent with charge scaling, i.e., possessing the correct low-frequency dielectric constant of 45. Here, we build on top of this water charge scaled models of biologically relevant Li+, Na+, K+, Ca2+, Mg2+ cations and Cl−, Br−, and I−, employing machine learning to streamline and speed-up the parameterization process. On the one hand, we show that the present model outperforms the best existing charge scaled model of aqueous ions. On the other hand, the present work points to a future need for improving consistently and simultaneously the water and ion models within the electronic continuum correction framework. Scaling ; Charge (physics) ; Ion ; Force field (fiction) ; Field (mathematics) ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Geometry Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Photoreceptor and optogenetics research
Authors: Sooyoon Koh | Wonkook Kim | Seung‐Woon Baek | Tae‐Wan Kim
Source: Construction and Building Materials (article, 2025-06-04)
Field: Chemistry — Analytical Chemistry

Estimation of coarse aggregate properties in concrete using hyperspectral imaging and machine learning Hyperspectral imaging ; Aggregate (composite) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Geotechnical engineering ; Materials science ; Composite material ; Engineering Spectroscopy and Chemometric Analyses ; Infrastructure Maintenance and Monitoring ; Smart Agriculture and AI
Authors: Kwang-Hu Jung | Seong Jong Kim
Source: Materials (article, 2025-09-19)
Field: Materials Science — Metals and Alloys

Statistical and ANN Modeling of Corrosion Behavior of Austenitic Stainless Steels in Aqueous Environments This study applies statistical approaches utilizing linear regression and artificial neural networks (ANNs) to predict the corrosion behavior of austenitic stainless steels (316L, 904L, and AL-6XN) under various environmental conditions. The environmental variables considered include temperature (30-90 °C), chloride ion concentration (20-40 g/L), and pH (2-6). Analysis of variance (ANOVA) confirmed that the input variables, including the Pitting Resistance Equivalent Number (PREN ranging from 24 to 45), significantly affect the critical pitting potential. The influence of the variables was ranked in the order: PREN, temperature, pH, and chloride ion concentration. A linear regression model was developed using significant factors and interactions identified at the 95% confidence level, achieving a predictive performance with R<sup>2</sup> = 0.789 for critical pitting potential. To predict potentiodynamic polarization curves, an ANN based on supervised learning with backpropagation was employed. The ANN model demonstrated a remarkably high predictive performance with R<sup>2</sup> = 0.972 in complex corrosion environments. The predicted polarization curves reliably estimated electrochemical characteristics such as corrosion current, corrosion potential, and pitting potential. These results provide a valuable tool for predicting and understanding the corrosion behavior of stainless steels, which can aid in corrosion prevention strategies and material selection decisions. Pitting corrosion ; Corrosion ; Chloride ; Polarization (electrochemistry) ; Austenite ; Backpropagation ; Austenitic stainless steel Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Xinwei Wang | Bohan Cao | Mengxin Yang | Haobo Li | Xiaohan Wang | Defang Duan | Liang Li | Fubo Tian | Tian Cui
Source: Physical review. B./Physical review. B (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Deep learning assisted exploration of superionic states and melting temperatures in Li-Sn superconductors The unique spatial arrangement of Li atoms in Li-rich compounds induces properties on Li-rich compounds, including interstitial quasiatoms, superconductivity, and superionic states. However, the dependence of increasing Li concentration on electron-phonon coupling (EPC) and superionic behavior at high pressure remains inadequately understood. Here, we systematically investigate the crystal structure of the $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--8) compound at 0--150 GPa using structure prediction and first-principles calculations. Band calculations reveal that the $P6/mmm$ phases of $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--7) commonly exhibits Dirac points, van Hove singularities, and flat bands. Superconducting critical temperatures (${T}_{\mathrm{c}}$) were also investigated, with LiSn exhibiting a ${T}_{\mathrm{c}}$ of 9.3 K at 0 GPa. We further analyzed the relationship between superconductivity and EPC strength, phonon softening, linewidth, and density of states. Deep learning molecular dynamics simulations of LiSn, ${\mathrm{Li}}_{2}\mathrm{Sn}, {\mathrm{Li}}_{3}\mathrm{Sn}$, and ${\mathrm{Li}}_{4}\mathrm{Sn}$ demonstrate that when the Li concentration exceeds twice that of Sn, Li atoms undergo melting while Sn remains solid, indicating the emergence of a superionic state. Among them, ${\mathrm{Li}}_{3}\mathrm{Sn}$ exhibits the highest melting temperature of 1013 K. These findings enrich the crystal structure of Li-Sn compounds and provide a theoretical basis for constructing superconductors with a Kagome lattice and superionic states. Superconductivity ; Condensed matter physics ; Materials science ; Engineering physics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Zhenjie Lun | Minbo Wu | Di Zuo | Huanhuan Zou | Dongdan Chen | Shanhui Xu | Zhongmin Yang
Source: Materials Futures (article, 2025-08-27)
Field: Materials Science — Ceramics and Composites

Rational design of chemically complex laser glasses via neighboring glassy compounds model Abstract The intricate amorphous structure and elusive local environment of rare-earth (RE) doped laser glasses render existing physical or machine-learning models inadequate for guiding the design of next-generation laser glasses, thereby maintaining iterative experimentation as the primary method for development. Here, the microenvironment surrounding RE ions in a multicomponent laser glass is treated as a statistical ensemble derived from its neighboring glassy compounds (NGCs). The NGCs model employs statistical ensemble averaging over the NGCs to provide a rigorous mathematical description of the key local structural and luminescent behaviors of RE-doped laser glasses. Validation through molecular dynamics simulations and experimental data for quaternary germanate glass system demonstrates the model’s excellent predictive capabilities, allowing it to establish the composition–structure relationship and populate the composition–property space. Moreover, the model enables the creation of multi-luminescence property charts, facilitating the de novo design of chemically complex laser glasses for targeted applications by efficiently screening the compositions which simultaneously meet several performance constraints. This work offers a robust framework for studying the luminescent behaviors of glass and paves the way for new explorations in laser glass technology. Materials science ; Laser ; Rational design ; Nanotechnology ; Optics ; Physics Glass properties and applications ; Laser Material Processing Techniques