jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sushil E Jamale | Suresh G Vedpathak
Source: International Journal of Advanced Chemistry Research (article, 2025-11-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Comprehensive Review of Volumetric Analysis: From Classical Titrations to Modern Cross-Disciplinary Applications Volumetric analysis plays a crucial role in quantitative analytical chemistry for centuries and remains a fundamental technique in both educational and professional laboratories. This review discusses the evolution of volumetric analysis from its classical roots which are in acid-base, redox, complexometric, and precipitation titrations to its modern incarnations. It also explores the enduring theoretical principles, including the mathematics of titration curves and error analysis, and discuss the persistent challenges students face in mastering the technique. Furthermore, the article highlights the paradigm shift brought by technological advancements, including digital instrumentation, automated titration systems, and sophisticated data analysis algorithms. Crucially, this review contextualizes the expanded meaning of "volumetric analysis" beyond chemistry, showcasing its critical applications in medical imaging, materials science, and environmental monitoring. Finally, we consider future perspectives, emphasizing the integration of micro-sampling techniques, advanced sensors, machine learning, and green chemistry principles to enhance the accuracy, speed, sustainability, and scope of volumetric methods. Scope (computer science) ; Titration ; Meaning (existential) ; Titration curve ; Face (sociological concept) ; Statistical analysis Various Chemistry Research Topics ; Water Quality Monitoring and Analysis ; Biosensors and Analytical Detection
Authors: Kaustav Das | Jan Sýkora | Anna Kučerová
Source: Acta Polytechnica CTU Proceedings (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Tractable descriptors for digital aggregate generation This paper introduces a deep learning-based approach for generating 2D aggregate shapes using a small set of tractable and physically meaningful parameters. In contrast to existing methods that often rely on quantities without clear physical interpretations, the approach presented here leverages scale invariant moments to capture essential shape characteristics. Additionally, we introduce the idea of using scale-invariant state descriptors, such as area, to control the size of the generated shapes. The neural network is trained to generate shapes corresponding to these parameters, and its ability to learn the relationship between shape constants and state descriptors without explicit data augmentation is demonstrated. The framework thus presented provides a foundation for developing microstructure generators that offer enhanced interpretability by relying on parameters that provide meaningful insights into the description of material morphology composed of non-trivial shapes. Interpretability ; Aggregate (composite) ; Set (abstract data type) ; Artificial neural network ; Scale (ratio) ; Contrast (vision) ; Training set ; Invariant (physics) Machine Learning in Materials Science ; 3D Shape Modeling and Analysis ; Advanced Sensor and Energy Harvesting Materials
Authors: Serhii Fedoriachenko | Kyrylo Ziborov | Ivan Lutsenko | D Harkavenko
Source: Materials science forum (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Steel Microstructure Prediction Mechanism Using Convolutional Neural Networks Accurate prediction of steel microstructure is critical for ensuring desirable mechanical properties in industrial applications. This research integrates metallurgical transformation models into a convolutional neural network (CNN) for the classification and quantitative analysis of steel microstructures, including ferrite, pearlite, bainite, and martensite. The model utilizes image-based grain structure recognition in combination with explicit mathematical relations, such as the Hall-Petch equation for yield strength, the Koistinen-Marburger equation for martensitic transformation, and the Avrami equation for ferrite and pearlite phase fraction prediction. By implementing these relations within a Python-based machine learning framework, the network not only classifies steel phases but also estimates grain size, transformation kinetics, and mechanical properties. The developed approach achieves an accuracy of over 90% in microstructure classification and enables real-time prediction of metallurgical characteristics from microstructure images. This research provides a new avenue for computational material science by integrating data-driven neural networks with fundamental physical models. Microstructure ; Pearlite ; Artificial neural network ; Convolutional neural network ; Ferrite (magnet) ; Martensite ; Grain size ; Transformation (genetics) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Baum, Dario | Förster, Arno | Visscher, Lucas
Source: ArXiv.org (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Transfer learning of GW-Bethe-Salpeter Equation excitation energies A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained on DFT and TDDFT properties can be finetuned with limited qs$GW$ and qs$GW$-BSE data to yield accurate predictions of quasiparticle and excitation energies. Assessing both full-model and readout-only finetuning across chemically diverse test sets, we find that pretraining improves accuracy, reduces reliance on costly qs$GW$ data, and mitigates large predictive outliers even for molecules larger or chemically distinct from those seen during finetuning. Our results demonstrate that multi-fidelity transfer learning can substantially extend the reach of many-body-level predictions across chemical space. Time-dependent density functional theory ; Excitation ; Transfer of learning ; Quasiparticle ; Perturbation theory (quantum mechanics) ; Outlier ; Density functional theory ; Yield (engineering) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: He Lin (217878) | Xiaoqi Cui (2918258) | Binglin Dai (21537933) | Jiawei Chen (186337) | Pengkun Su (13206125) | Zhaomin Su (20839316) | Huihui Hu (5693204) | Yibin Jiang (702035) | Cheng Wang (102692)
Source: Figshare (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Reasoning LanguageModel as Rule Finder: A Case Studyon C–H Bond Activation Using 2D Metal–Organic Frameworks Unraveling the structure–activity relationship in catalysis requires interpretable models that can extract governing principles from complex data sets. This study explores reasoning large language models (LLMs) as rule-finders for predicting C­(sp<sup>3</sup>)–H activation outcomes catalyzed by 2D Fe-terpyridine MOFs. Surface modifications with molecular modifiers systematically modulate the catalytic microenvironment, but linking modifier structure to activity remains challenging. While traditional descriptors offer high predictive accuracy, LLM-derived rules provide interpretable insights. Integrating LLM reasoning with experimental features (e.g., Fe-loading, modifier ratios) identified para-substituted benzoates with electron-withdrawing or coordinating groups as performance boosters. Validated by machine learning, this rule achieved 82.6% prediction accuracy. Notably, the coordinating group can become electron-withdrawing upon Fe<sup>3+</sup> coordination or protonation. The LLM revealed that modifiers tune the catalyst’s electronic state rather than directly interacting with intermediates/transition states, bridging data-driven predictions with mechanistic understanding. This highlights LLM’s potential to derive chemically meaningful rules in catalysis. Bridging (networking) ; State (computer science) ; Computational model ; Model-based reasoning ; Automated reasoning Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Bilgesu Bekremen | Barbaros Özer
Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi (article, 2025-12-02)
Field: Chemistry — Analytical Chemistry

Potential application areas of artificial intelligence in dairy industry Food manufacturers are taking a proactive approach to food safety and quality issues through predictive modeling. This situation necessitates reaching the most accurate result with high speed and repeatability. Therefore, food manufacturers aim to integrate artificial intelligence systems into their routine production by utilising machine learning (ML), the Internet of Things (IoT), and deep learning (DL) through big data analysis. The fact that milk is easily spoilt due to its nature, that the quality of raw milk directly affects the quality of the final product and that raw milk production is predominantly carried out by both dispersed and small producers makes it mandatory for the dairy industry to focus on quality and food safety issues in a disciplined manner from raw materials to the end product. This review discusses the possibilities of utilising artificial intelligence models in the dairy industry to maintain quality and ensure food safety. Quality (philosophy) ; Dairy industry ; Product (mathematics) ; Production (economics) ; Food industry ; Raw milk ; Industry 4.0 Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Microbial Inactivation Methods
Authors: Ziyang Pei | Jianfeng Zhao | N. Wang
Source: International Journal of Energy and Environmental Science (article, 2025-09-25)
Field: Chemistry — Analytical Chemistry

A Study on a Salt Solution Concentration Prediction Method Based on CNN-LSTM-Attention The rapid and accurate analysis of ion concentrations in mixed salt solutions is a critical aspect of utilizing salt lake chemical resources. To explore an efficient and non-destructive detection method, this study proposes a deep learning model that fuses a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and an Attention mechanism for the prediction of salt solution concentrations using Near-Infrared Spectroscopy (NIRS). First, single-component and two-component mixed salt solution samples of NaCl, KCl, and MgCl&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; were prepared, and their near-infrared spectral data were collected. After applying Savitzky-Golay smoothing and derivative preprocessing to the spectra, a CNN-LSTM-Attention prediction model was constructed. A comparative analysis was conducted against common models such as Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF), and an ablation study was performed to analyze the contribution of each deep learning module. The results show that for single-component salt solutions, the proposed model&amp;apos;s performance is comparable to that of the high-performing SVR and RF models. In complex mixed solutions with severe spectral overlap, the CNN-LSTM-Attention model demonstrated significant superiority, with its prediction accuracy surpassing all traditional baseline models across all mixed datasets, achieving a coefficient of determination (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;) as high as 0.973. The study concludes that the proposed CNN-LSTM-Attention model can effectively address the challenge of spectral overlap, demonstrating the potential of using deep learning for the quantitative analysis of complex mixture systems via near-infrared spectroscopy. Partial least squares regression ; Smoothing ; Support vector machine ; Salt (chemistry) ; Artificial neural network ; Preprocessor ; Deep learning ; Regression analysis Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Jiaxian Zhang | Hongxia Guo
Source: Polymer (article, 2025-10-26)
Field: Chemistry — Organic Chemistry

Erratum to “Constructing a temperature transferable Coarse-Grained model of cis-1,4-Polyisoprene with the structural and thermodynamic consistency aided by machine learning” [Polymer 311 (2024) 127516] Consistency (knowledge bases) ; Data consistency Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Yanxiao Hu | Sheng Ye | Jing Huang | Xiaoxin Xu | Yabei Wu | Caichao Ye | Jiong Yang | Wenqing Zhang
Source: Physical review. B./Physical review. B (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Local atomic energy regularization with posterior information optimization in machine-learning interatomic potentials Decomposition of global properties (e.g., total energy) into local entities, as implemented in machine-learning- and neural-network-driven materials modeling, inherently introduces uncertainty due to the physically undefined nature of local properties. The validity of local decomposition constitutes an essential but still debated consideration in current studies. To address this ambiguity, we introduce a probabilistic atomic energy regularization (AER) framework designed to constrain local energy projections in machine learning interatomic potentials (MLIPs). The essence of AER is to regulate local-environment-determined atomic properties by incorporating statistically significant long-range correlations, thereby establishing a probability-based framework that selectively filters local decomposition incompatible with established correlation constraints. Beyond significantly improving decomposition robustness in regular solids, AER demonstrates excellent physical scalability as evidenced by the thermal transport simulations for $\ensuremath{\beta}\text{\ensuremath{-}}\mathrm{C}{\mathrm{u}}_{1.95}\mathrm{Se}$ with highly fluctuating local environment and higher-order nonlinearity. Our findings not only underscore the critical role of prior beliefs in MLIP development but also offer a generalized framework for resolving property decomposition controversies across data-driven machine-learning models. Regularization (linguistics) ; Interatomic potential ; Computer science ; Energy minimization ; Statistical physics ; Physics ; Artificial intelligence ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal properties of materials
Authors: Friedrich Hastedt | Klaus Hellgardt | Sophia N. Yaliraki | Dongda Zhang | Ehecatl Antonio del Rio‐Chanona
Source: Journal of Cheminformatics (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

MolPrice: assessing synthetic accessibility of molecules based on market value Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce MolPrice, an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning, MolPrice autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that MolPrice reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore, MolPrice achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how MolPrice successfully identifies purchasable molecules from a large candidate library. MolPrice bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery. Virtual screening ; Synthetic data ; Generative model ; Generative grammar ; Training set ; Cheminformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Rick Oerder | Gerrit Schmieden | Jan Hamaekers
Source: Machine Learning Science and Technology (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

Multi-fidelity learning for atomistic models via trainable data embeddings Abstract We present an approach for end-to-end training of machine learning models for structure-property modeling on collections of datasets derived using different density functional theory functionals and basis sets. This approach overcomes the problem of data inconsistencies in the training of machine learning models on atomistic data. We rephrase the underlying problem as a multi-task learning scenario. We show that conditioning neural network-based models on trainable embedding vectors can effectively account for quantitative differences between methods. This allows for joint training on multiple datasets that would otherwise be incompatible. Therefore, this procedure circumvents the need for re-computations at a unified level of theory. Numerical experiments demonstrate that training on multiple reference methods enables transfer learning between tasks, resulting in even lower errors compared to training on separate tasks alone. Furthermore, we show that this approach can be used for multi-fidelity learning, improving data efficiency for the highest fidelity by an order of magnitude. To test scalability, we train a single model on a joint dataset compiled from ten disjoint subsets of the MultiXC-QM9 dataset generated by different reference methods. Again, we observe transfer learning effects that improve the model errors by a factor of 2 compared to training on each subset alone. We extend our investigation to machine learning force fields for material simulations. To this end, we incorporate trainable embedding vectors into the readout layer of a deep graph neural network (M3GNet) that is simultaneously trained on PBE and r2SCAN labels of the MatPES dataset. We observe that joint training on both fidelity levels reduces the amount of r2SCAN data required to achieve the accuracy of a single-fidelity model by a factor of 10. Embedding ; Fidelity ; Disjoint sets ; Transfer of learning ; Artificial neural network ; Training set ; Joint (building) ; Basis (linear algebra) ; Graph Machine Learning in Materials Science
Authors: Hanwen Yan (11587323) | Xiaoyan Zheng (499676) | Jie Li (15030) | Feng Huo (1435492) | Chunshan Li (1235667)
Source: Figshare (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Barrier-BalancingMachine Learning Framework withMultidimensional Descriptors for Interpretable Screening of SecondaryAmine Ionic Liquid Catalysts Rational screening and design of homogeneous catalysts remain a core challenge in multistep reaction systems, particularly when multiple elementary steps exhibit comparable and elevated activation barriers. Inspired by the Sabatier principle and the degree of rate control theory, we propose a barrier-balancing machine learning framework that focuses on coordinating rather than minimizing individual energy barrier with density functional theory calculations and experimental validation, to identify efficient secondary amine ionic liquid catalysts. Taking the Mannich reaction to synthesize methylacrolein as a probe, we target two kinetically significant steps of nucleophilic attack on formaldehyde (Δ<i>E</i><sub>1</sub>) and decomposition of the intermediate (Δ<i>E</i><sub>2</sub>), and define the maximum of the two as the model’s training target. A physically interpretable multidimensional descriptor library is constructed, integrating physicochemical-based properties, Morgan fingerprints, volume-based steric parameters, and quantum chemical features. The trained random forest model, interpreted via SHapley Additive exPlanations (SHAP), identifies key descriptors that govern barrier heights. A barrier-based scoring metric derived from model predictions shows strong correlation with experimental selectivity, validating its physical relevance and predictive power. This framework not only differentiates aryl- and alkyl-substituted amines by their electronic environments, but also offers an interpretable and generalizable tool for catalyst screening in complex homogeneous systems. Steric effects ; Metric (unit) ; Nucleophile ; Catalysis ; Ionic liquid ; Decomposition ; Homogeneous ; Molecular descriptor ; Relevance (law) Machine Learning in Materials Science ; Ionic liquids properties and applications ; Catalysis and Oxidation Reactions
Authors: Kisna Patel
Source: International Journal of Research in Science and Technology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Material Lifespan Predictor Predicting the operational lifespan of industrial components remains a critical challenge in manufacturing, where early failure leads to costly downtime and waste. This study addresses that problem by applying machine learning to estimate the lifespan of materials based on manufacturing process parameters and alloy composition. The dataset comprises 1,000 samples containing attributes such as component type, microstructure, cooling and heat-treatment parameters, alloy percentages, and defect counts. Seven regression models were developed and compared—Linear Regression, Polynomial Regression, Random Forest, XGBoost, Support Vector Regression, CatBoost, and LightGBM—using standardized numeric features and one-hot encoded categorical variables. Model performance was evaluated through mean squared error (MSE), mean absolute percentage error (MAPE), and the coefficient of determination (R²). Tree-based ensemble methods achieved superior results, with LightGBM delivering the best performance (MSE = 3757.36, MAPE = 4.13%, R² = 0.964). SHAP explainability analysis revealed that cooling rate, alloy composition, and defect counts were the most influential features. These findings demonstrate that gradient boosting ensembles, combined with explainability techniques, can provide accurate and interpretable predictions for material lifespan optimization in manufacturing environments. Categorical variable ; Mean absolute percentage error ; Downtime ; Random forest ; Gradient boosting ; Support vector machine ; Regression ; Mean squared error ; Linear regression Machine Learning in Materials Science ; Machine Fault Diagnosis Techniques ; Reliability and Maintenance Optimization