jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ke Wang | Yueting Wang | Ziyi Liu | Guiyan Yang | Xuejiao Huang | Shixiang Ma | Liangliang Hao | Leizi Jiao | Chunjiang Zhao | Daming Dong
Source: Computers and Electronics in Agriculture (article, 2025-07-16)
Field: Chemistry — Analytical Chemistry

Enhanced classification of wheat disease: In-depth analysis of plant volatile organic compounds based on PTR-MS with prior knowledge and convolutional neural network Convolutional neural network ; Artificial neural network ; Plant disease ; Pattern recognition (psychology) ; Artificial intelligence ; Environmental science ; Computer science ; Biotechnology ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Yiran Li | Lingwei Ma | Zongbao Li | Xin Guo | Jingzhi Yang | Jinke Wang | J.M.C. Mol | Dawei Zhang
Source: Corrosion Science (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Design of rust layer stabilizers for weathering steel guided by interpretable machine learning and Bayesian optimization Rust (programming language) ; Corrosion ; Bayesian optimization ; Layer (electronics) ; Interpretability ; Weathering steel ; Passivation ; Hyperparameter Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors ; Concrete Corrosion and Durability
Authors: Justin Trujillo | Russell Fung | S. Madan Kumar | Peter Schwander | Ahmad Hosseinizadeh
Source: Structural Dynamics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Filling data analysis gaps in time-resolved crystallography by machine learning There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data. Computer science ; Algorithm ; Noise (video) ; Bin ; Temporal resolution ; Data mining ; Artificial intelligence ; Physics ; Optics ; Image (mathematics) Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray Imaging Techniques
Authors: Yuanhao Zhou | Jiawen Ai | Zi Ye | Kexin Chen | JingXiong Lin | Zhenhua Zhang | Mi Luo | Benjie Zhou | Shijian Xiang | Jianhua Zhou | Xinming Huo
Source: Advanced Science (article, 2025-05-23)
Field: Chemistry — Spectroscopy

Fully Automated Deep Learning Enabled Miniature Mass Spectrometry System for Psychoactive Therapeutic Drug Monitoring Advancing precision medicine requires efficient small molecule biomarker detection in biofluids, yet existing methods encounter challenges in complexity, portability, and throughput. This study presents an integrated miniature blood processing and mass spectrometry (MS) analysis system, which incorporates automated magnetic solid-phase extraction, self-aspiration sampling miniature mass spectrometer, and deep learning algorithms for automated quantitative analysis. It achieves full automation from sample preparation to detection, demonstrating the capability to analyze serum psychoactive drugs with a 15-second/MS acquisition and 8-sample parallel processing within 30 minutes (including pretreatment). This has significantly increased detection throughput and facilitated the establishment of the standard curve. The novel dual-target ion parallel tandem MS analysis technique, combined with a U-net peak area recognition algorithm, achieved over 98% identification accuracy with less than 0.2% area prediction deviation. Quantitative analysis showed high correlation coefficients >0.99 in medically relevant ranges, supported by relative standard deviation < 10% and average back-calculated accuracy deviation < 3.5%. Clinical validation revealed strong concordance with LC-MS/MS. The system's integration of automated sample processing, miniature MS hardware, and AI-driven data analysis establishes a paradigm for high-throughput clinical detection. The advantages of accuracy, automation, intelligence, miniaturization, and high throughput suggest significant potential for this system in clinical detection and personalized medicine. Throughput ; Computer science ; Automation ; Mass spectrometry ; Software portability ; Miniaturization ; Artificial intelligence ; Chemistry ; Chromatography ; Engineering ; Nanotechnology ; Materials science Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Nurcan Vardar-Yel
Source: Biologia (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Machine learning techniques for differentiating psychrophilic and non-psychrophilic bacterial α/β hydrolase enzymes Psychrophile ; Enzyme ; Microbiology ; Hydrolase ; Biology ; Biochemistry ; Extremophile ; Chemistry ; Thermophile Enzyme Structure and Function ; Metalloenzymes and iron-sulfur proteins ; Biotin and Related Studies
Authors: Wei-Hsin Chen | Sheng-Ting Lin | Ding Luo | Anh Tuan Hoang | Liwen Jin | Yuan Yu
Source: Energy (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Carbon-based flexible thermoelectric generators enhanced by dual-elastomer design, AI prediction, and life cycle optimization Thermoelectric effect ; Thermoelectric generator ; Thermal conductivity ; Thermoelectric materials ; Thermoelectric cooling ; Finite element method ; Power (physics) ; Electricity generation ; Carbon footprint Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; MXene and MAX Phase Materials
Authors: César E. Ramírez | Elena Di Mare | James Byrnes | Eman Ahmed | Maria Pineiro-Goncalves | Christelle Lopez | N. Sanjeeva Murthy | Adam J. Gormley
Source: Biophysical Journal (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles Small-angle X-ray scattering ; Cluster analysis ; Scattering ; Radius of gyration ; Small-angle scattering ; Characterization (materials science) ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Enzyme Structure and Function
Authors: Corinna Brungs | Robin Schmid | Steffen Heuckeroth | Aninda Mazumdar | Matúš Drexler | Pavel Šácha | Pieter C. Dorrestein | Daniel Petras | Louis‐Félix Nothias | Václav Veverka | Radim Nencka | Zdeněk Kameník | Tomáš Pluskal
Source: ChemRxiv (preprint, 2025-03-25)
Field: Chemistry — Spectroscopy

Efficient generation of open multi-stage fragmentation mass spectral libraries Untargeted analysis based on high-resolution mass spectrometry is a key tool in clinical metabolomics, natural product discovery, and exposomics, with compound identification remaining the major bottleneck. Currently, MS2 fragmentation data and spectral library matching are the standard workflow for confident compound annotation. Multi-stage fragmentation (MSn) yields more profound insights into substructures, enabling validation of fragmentation pathways; however, the community lacks open MSn data for reference compounds. Here, we describe a high-throughput method for acquiring MSn trees and an automated workflow for extracting and building open MSn libraries. By applying this pipeline to 37,829 small molecules, we obtained MSn spectra for 30,008 unique compound structures within 23 days. This resource includes 2,350,646 MSn spectra (merged and individual) and can be leveraged for compound annotation based on library matching, including substructures and training of machine learning models on substructure-fragmentation patterns. The workflow, implemented in mzmine and Python scripts, is open-source and freely available. Fragmentation (computing) ; Stage (stratigraphy) ; Computer science ; Geology ; Operating system Mass Spectrometry Techniques and Applications
Authors: Grier M. Jones | Konstantinos D. Vogiatzis
Source: Journal of Chemical Theory and Computation (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework Multireference perturbation theory methods, such as complete active space second-order perturbation theory (CASPT2), are often employed to recover the missing electron correlation from multiconfigurational zeroth-order wave functions. Here, we introduce the data-driven CASPT2 (DDCASPT2) method to capture dynamic electron correlation using features generated from lower-level electronic structure methods, such as Hartree-Fock and complete active space self-consistent field (CASSCF) theory. We examine the effects of system size, basis set size, and the number of two-electron excitations using a small, but diverse, set of molecules. We also provide insights into our physics-based feature set using SHapley Additive exPlanation (SHAP) analysis, a feature analysis method based on cooperative game theory. In this paper, we utilize these insights to introduce a DDCASPT2 method, which provides a machine-learning-based alternative to traditional single- and multistate CASPT2 for capturing dynamical electron correlation with near-CASPT2 quality accuracy. Electronic correlation ; Correlation ; Perturbation (astronomy) ; Complete active space ; Perturbation theory (quantum mechanics) ; Set (abstract data type) ; Space (punctuation) ; Parameter space Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Richard Woollam | Ethan Proudlove | Michael Jones | H.M. Thompson | Richard Barker
Source:  (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Symbolic Regression Based Surrogate Modelling of a High-Fidelity Multiphysics CO2 Corrosion Model Abstract A high-fidelity mechanistic carbon steel/carbon dioxide (CO2) corrosion prediction model developed at the University of Leeds has been implemented in the multi-physics (MP) software package COMSOL. The model integrates the bulk solution equilibria, interfacial mass transport and solution reactions, and surface anodic and cathodic electrochemical processes. In this work, the high-fidelity model is randomly sampled in the 5-input dimensions (bulk pH, temperature, CO2 partial pressure, piping velocity and diameter) using a Beta distribution, B(α, β), with the parameters α and β set to 12 in order to oversample the extremes of the input range. The high-fidelity MP model was sampled approximately 225,000 times and the data refined using a Deep Neural Network (DNN) as described by Proudlove et al.. The output from the high-fidelity model went through three rounds of refinement via the DNN to produce the final dataset. After data refinement and 5-fold cross-validation, the data was further analyzed using a symbolic regression (SR) genetic algorithm. Each generation from the SR was plotted on a Pareto optimization plot, displaying the relationship between model complexity and the fit quality metric, (1 − R2). From the Pareto front, showing the trade-off between maximum quality of fit for minimal model complexity, the ‘best’ mathematical expression representing the data was chosen. Additionally, the mathematical expressions generated were reviewed subjectively for ‘physical reasonableness’ and for ‘ease of implementation in a typical spreadsheet. The output from the three models were compared in a 3-dimensional correlation diagram with excellent agreement between all models, R2 &amp;gt; 0.99. Multiphysics ; Computer science ; Surrogate model ; Regression analysis ; Corrosion ; Materials science ; Engineering ; Metallurgy ; Machine learning ; Finite element method ; Structural engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Mustafa Kurban | Can Polat | Erchin Serpedin | Hasan Kurban
Source: International Journal of Hydrogen Energy (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Multimodal explainable artificial intelligence-driven analysis of quantum size effects in copper nanoclusters for hydrogen storage Nanoclusters ; Copper ; Hydrogen ; Hydrogen storage ; Quantum ; Electronic structure ; Valence (chemistry) Hydrogen Storage and Materials ; Nanocluster Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Wei Li | Yuqian Wang | Louzhen Fan | Runpu Shen | Zhongmin Xiao | Jianzhong Xu | Junyang Chen | Gaoxiang Xu
Source: Analytical Chemistry (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Silver-Programmed Dual-Optical Au Nanostructures and Machine Learning for Intelligent Biosensing The evolution of biosensors demands synergistic improvements in signal transduction and data processing. We present a universal biosensing platform that combines dual-mode signal responses from silver-modulated gold nanorods (AuNRs) and gold-silver nanoclusters (AuAgNCs) (including localized surface plasmon resonance (LSPR) shifts and fluorescence variations) with machine learning (ML)-enhanced image analysis. Initially, AuNR was synthesized and transformed into silver-coated gold nanorods (AuNR@Ag) via silver reduction, with LSPR shifts precisely characterized. Concurrently, AuAgNCs were engineered to enhance their fluorescence through antigalvanic reactions between surface Ag(I) and Au(0) cores. The dual-mode platform leverages the silver-linked fluorescence intensity of AuAgNCs and LSPR of AuNR@Ag, as well as the increasingly enhanced inner filter effect between AuAgNCs and the evolving LSPR of AuNR@Ag, enabling simultaneous fluorescence and colorimetric readouts. The platform achieved high-precision detection of alkaline phosphatase via dual-signal correlation (<i>R</i><sup>2</sup> > 0.99), demonstrating robustness in complex matrices. Furthermore, to facilitate point-of-care testing applications, an ML algorithm encompassing feature extraction, dimensionality reduction, and model validation was integrated to process bimodal signal images. The subsequent data analysis exhibited robust correlations (<i>R</i><sup>2</sup> > 0.95), thereby substantiating the effectiveness of this approach in analyzing bimodal data. The ML-augmented analytics was validated for the analysis of serum samples, giving results that matched well with those from the spectra-based standard method. This work bridges nanomaterial engineering with ML-augmented analytics, offering a versatile framework for next-generation biosensors with clinical diagnostic potential. Biosensor ; Nanoclusters ; Nanorod ; Surface plasmon resonance ; Robustness (evolution) ; Fluorescence ; Nanostructure ; Colloidal gold Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Jianing Shen | Bo Zhang | Tianhao Xue | Yao Zhang | Guixian Zhu
Source: Analytical Methods (article, 2025-01-01)
Field: Chemistry — Electrochemistry

Multi-component metabolite electrochemical detection and analysis based on machine learning Metabolic molecules are highly correlated with various physiological indicators and diseases, so it is particularly important to monitor the levels of multiple metabolites in the body. Due to the similar electrochemical properties of uric acid (UA), dopamine (DA), and ascorbic acid (AA), multi-component detection of these substances is challenging. When establishing relationships between electrochemical characteristics and concentrations of the respective components, there will be issues such as overlapping peaks and other difficulties. In order to accurately identify the components and determine their concentration in the detection solution, we designed a multi-component detection experiment for AA, UA, and DA. After obtaining the detection results, we applied curve smoothing and feature extraction to construct classification and regression machine learning models. The ANN model achieved the highest accuracy of 94.06% among the five classification models evaluated. Regression models were built using RF and XGBoost, with the best performing XGBoost model achieving an average R-squared prediction of 96.2%. With high component discrimination and prediction accuracy, these models ensure user-friendliness and support qualitative and quantitative analysis of multi-component solutions. Metabolite ; Component (thermodynamics) ; Computer science ; Electrochemistry ; Component analysis ; Chemistry ; Chromatography ; Artificial intelligence ; Electrode ; Physics ; Biochemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Vitamin C and Antioxidants Research
Authors: Rakesh Kumar Gupta | Proshanta Guha | Prem Prakash Srivastav
Source: Food and Bioprocess Technology (article, 2025-12-17)
Field: Materials Science — Biomaterials

Comparative Study on the Optimization and Characterization of Underutilize Taro Starch Based Composite Film using Response Surface Methodology and Artificial Neural Network Coupled with Genetic Algorithm Response surface methodology ; Starch ; Ultimate tensile strength ; Composite number ; Guar gum ; Central composite design ; Crystallinity Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites ; Bamboo properties and applications
Authors: Jia Lin Lee | Gun Hean Chong | Masaki Ota | Haixin Guo | Richard L. Smith
Source: Fluid Phase Equilibria (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Prediction of pharmaceutical solubility in mixed-solvents with a local composition-quantum energy parameter model Chemistry ; Solubility ; Hildebrand solubility parameter ; Composition (language) ; Thermodynamics ; Quantum chemical ; Quantum ; Computational chemistry ; Organic chemistry ; Molecule Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure ; Analytical Chemistry and Chromatography
Authors: Arkadiusz Leniak | Wojciech Pietruś | Rafał Kurczab
Source: Journal of Chemical Information and Modeling (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

From NMR to AI: Fusing <sup>1</sup>H and <sup>13</sup>C Representations for Enhanced QSPR Modeling The ability to predict log <i>D</i> directly from spectral patterns marks a conceptual shift in cheminformatics. In this work, we demonstrate that <sup>1</sup>H and <sup>13</sup>C NMR spectra, computationally generated from molecular structures and transformed into machine learning-compatible vectors, can approach and rival classical structure-based descriptors such as ECFP4 fingerprints in modeling the log <i>D</i> parameter. Through comprehensive benchmarking of nearly 70 models across seven algorithmic classes and three pH conditions, we show that concatenation of <sup>1</sup>H and <sup>13</sup>C NMR spectra offers the best trade-off between accuracy and efficiency. In the best case, a fused spectral CNN model achieved a root-mean-square error (RMSE) of 0.57 and a <i>Q</i><sup>2</sup> of 0.76 using a 400-dimensional input vector─closely matching the ECFP4 benchmark (RMSE 0.56, <i>Q</i><sup>2</sup> 0.78) despite being five times smaller. These findings challenge the assumption that descriptor richness must come at the cost of dimensional complexity. SHAP-based analysis revealed modality-specific patterns: <sup>13</sup>C regions linked to aromatic and carbonyl carbons (110-170 ppm) increased predicted log <i>D</i>, while <sup>1</sup>H signals associated with polar groups, including OH, NH, amides, and ethers (2-4.5 and ∼8 ppm), reduced it. This positions NMR-based vectors as both interpretable and scalable alternatives to conventional fingerprints. By releasing a standalone graphical prediction tool based on our models, we make this paradigm practically accessible for real-world applications. This study establishes <i>in silico-</i>generated NMR spectra as valid and powerful descriptors in predictive modeling, paving the way for spectrum-driven approaches to drug discovery and property prediction. Concatenation (mathematics) ; Benchmark (surveying) ; Matching (statistics) ; Benchmarking ; Scalability ; Pattern recognition (psychology) ; Quantitative structure–activity relationship ; Curse of dimensionality ; NMR spectra database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies