jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiyoung Lee | Logan J. Augustine | Graeme Henkelman | Ping Yang | Danny Pérez
Source: Journal of Chemical Theory and Computation (article, 2025-05-13)
Field: Chemistry — Inorganic Chemistry

Data-Driven Kinetic Reaction Networks for Separation Chemistry Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. In this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid-liquid extraction mechanism of uranium using <i>N</i>,<i>N</i>-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO<sub>2</sub>(NO<sub>3</sub>)<sub>2</sub>(DEHiBA)<sub>2</sub>, agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost. Separation (statistics) ; Kinetic energy ; Computer science ; Chemistry ; Data science ; Physics ; Machine learning Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Ruchika Mahajan | Ashton M. Aleman | Colin F. Crago | Suman Bhasker-Ranganath | Melissa E. Kreider | José A. Zamora Zeledón | Johanna Schröder | Gaurav A. Kamat | McKenzie A. Hubert | Adam C. Nielander | Thomas F. Jaramillo | Michaela Burke Stevens | Johannes Voss | Kirsten T. Winther
Source: The Journal of Chemical Physics (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

A research database for experimental electrocatalysis: Advancing data sharing and reusability The availability of high-fidelity catalysis data is essential for training machine learning models to advance catalyst discovery. Furthermore, the sharing of data is crucial to ensure the comparability of scientific results. In electrocatalysis, where complex experimental conditions and measurement uncertainties pose unique challenges, structured data collection and sharing are critical to improving reproducibility and enabling robust model development. Addressing these challenges requires standardized approaches to data collection, metadata inclusion, and accessibility. To support this effort, we have developed an extensive data infrastructure that curates and organizes multimodal data from electrocatalysis experiments, making them openly available through the catalysis-hub.org platform. Our datasets, comprising 241 experimental entries, provide detailed information on reaction conditions, material properties, and performance metrics, ensuring transparency and interoperability. By structuring electrocatalysis data in web-based as well as machine-readable formats, we aim to bridge the gap between experimental and computational research, allowing for improved benchmarking and predictive modeling. This work highlights the importance of well-structured, accessible data in overcoming reproducibility challenges and advancing machine learning applications in catalysis. The framework we present lays the foundation for future data-driven research in electrocatalysis and offers a scalable model for other experimental disciplines. Benchmarking ; Data sharing ; Metadata ; Comparability ; Scalability ; Reusability ; Data access ; Big data ; Data modeling Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Nadab Raza | Sheeza Pervaiz | Urooj Haroon
Source: Next research. (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Machine learning-driven design of MXene-based electrocatalysts for HER, OER, and NRR: A comprehensive review MXenes ; Electrocatalyst ; Nitride ; Carbide MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ammonia Synthesis and Nitrogen Reduction
Authors: Sudhir Kumar Pathak | V.V. Tyagi | K. Chopra | Ahmet Sarı | A.K. Pandey
Source: Journal of Energy Storage (article, 2025-03-12)
Field: Materials Science — Polymers and Plastics

Energy matrices, techno-economic and environmental assessment of fin-integrated HP-ETSC systems with thermal battery backup: Machine learning insights from experimental data Backup ; Battery (electricity) ; Energy (signal processing) ; Fin ; Computer science ; Reliability engineering ; Engineering ; Mechanical engineering ; Thermodynamics ; Mathematics Transition Metal Oxide Nanomaterials ; Solar Thermal and Photovoltaic Systems ; Advanced Battery Technologies Research
Authors: Yifan Kang | Yijie Li | Hanyu Wang | Juan Guo | Ziqi Huang | Juan Du
Source: Journal of Food Science (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

The Use of Deep Learning in Primary Agricultural Products Freshness Assessment: A Systematic Review Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life. Currently, the freshness of primary agricultural products evaluation methods primarily includes sensory evaluation, spectroscopy, and colorimetric analysis. However, these techniques generally suffer from strong subjectivity, high requirements for specialized skills, and lengthy detection times. As a significant branch of machine learning, deep learning is based on neural networks and employs multi-layer architectures to process and learn from large-scale, complex data. It is capable of automatically extracting features from data and, by learning these features, can recognize and predict complex patterns. The application of deep learning for primary agricultural product freshness assessment not only enables automation and intelligent analysis of the evaluation process but also allows trained models to rapidly and accurately assess primary agricultural product freshness, thereby reducing the influence of manual intervention and subjective judgment. This paper reviews the integration of machine vision (based on physicochemical properties and smart visual labels), spectroscopy (hyperspectral imaging, near infrared spectrum, fluorescence spectra, and Raman spectrum), and electronic noses with deep learning for freshness of primary agricultural products evaluation, and highlights the current limitations of these technologies along with future development directions. Deep learning ; Agriculture ; Product (mathematics) ; Process (computing) ; Automation ; Food products ; Food spoilage ; Agricultural productivity Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Giorgio Felizzato | Giuliano Iacobellis | Nicola Liberatore | Sandro Mengali | Martin Sabo | Patrizia Scandurra | Roberto Viola | Francesco Saverio Romolo
Source: ACS Omega (article, 2025-02-11)
Field: Chemistry — Analytical Chemistry

Machine Learning-Driven Data Fusion of Chromatograms, Plasmagrams, and IR Spectra of Chemical Compounds of Forensic Interest Achieving fast analytical results on-site with the highest possible accuracy in forensic analyses is crucial for investigations. While portable sensors are essential for crime scene analysis, they often face limitations in sensitivity and specificity, especially due to environmental factors. Data fusion (DF) techniques can enhance accuracy and reliability by combining information from multiple sensors. This study develops different DF approaches using data from two sensors: ion mobility spectrometry (IMS) and gas chromatography-quartz-enhanced photoacoustic spectroscopy (GC-QEPAS), aiming to improve the safety of crime scene operators and the accuracy of on-site forensic analysis. Two DF approaches were developed for acetone and DMMP: low-level (LLDF) and mid-level (MLDF), meanwhile a high-level (HLDF) approach was applied to TATP. LLDF concatenated preprocessed data matrices, while MLDF employed principal component analysis for feature extraction. LLDF and MLDF used one-class support vector machines (OC-SVM) for classification, while HLDF combined OC-SVM for IMS and SIMCA for GC-QEPAS. Sensor location within crime scenes was established using traditional measuring tape and laser distance meters, with a 1 m cutoff distance between sensors deemed appropriate for indoor crime scenes. LLDF achieved high accuracy but was sensitive to concentration variations, while MLDF enhanced the classification robustness. HLDF allowed for independent sensor use in real scenarios. All of the methods reached 100% accuracy for DMMP and acetone, and the MLDF approach was the fastest among the DF methods, demonstrating its potential for rapid applications. DF approaches can significantly enhance the safety and accuracy of forensic investigations, with future research planned to extend data sets and include more sensors. Pattern recognition (psychology) ; Fusion ; Computer science ; Forensic science ; Artificial intelligence ; Chemistry ; Biology ; Philosophy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Zhaobin Zhang | Jianfu Li | Yang Lv | Yong Liu | Jianan Yuan | Jiani Lin | Xiaoli Wang
Source: Inorganic Chemistry (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Machine Learning Guided the Discovery of Superionic Delafossite AgFeO<sub>2</sub> The fundamental principle of solid-state electrolytes (SSEs) is predicated on the presence of superionic conductors (SICs), which are distinguished by the diffusion of liquid ions within the solid crystal lattice. These characteristics demonstrate considerable potential for achieving safe, high energy density, and reversible electrochemical energy storage in batteries. In this work, we employ molecular dynamics simulations based on machine learning force fields (MLFF) to elucidate the diffusion behavior of Ag<sup>+</sup> through the Fe-O solid lattice in delafossite AgFeO<sub>2</sub> above 800 K. The analysis of atomic trajectories, mean square displacements (MSD), and radial distribution functions (RDF) indicates that Ag<sup>+</sup> ions migrate primarily through the concerted mechanism within the structure. Madelung energy analysis revealed that the interaction between Fe and O is more intense than that between Ag and O. It is notable that the introduction of Ag vacancies resulted in a reduction of the superionic transition temperature from 800 to 600 K, thus demonstrating the impact of structural defects on ionic behavior. The present study opens up avenues for targeted materials in solid-state electrolytes and provides deeper insights into delafossite. Delafossite ; Chemistry ; Oxide ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Xiaohan Hu | Yinan Zhao | Shihui Qiu | Jing Nie | Hanqiang Liu | Tongtong Wang | Shaohua Zhang | Song Zhou | Wenlong Xu
Source: Small (article, 2025-09-09)
Field: Materials Science — Polymers and Plastics

Bifunctional Flexible Eutectogel: Zinc Cell Dendrite Inhibition and Machine Learning‐Assisted Parkinson's Disease Monitoring With the continuous development of flexible sensors and flexible energy storage devices, gel materials with good flexibility, toughness, and tunable properties have attracted wide attention. Deep eutectic solvents (DES) have an obvious advantage of thermal and chemical stability over water. Therefore, eutectogels can effectively solve the problem of insufficient stability of traditional hydrogels. In this paper, eutectogels with excellent electrochemical and sensing properties are prepared using acrylic acid (AA) and acrylamide (AM) as monomers and DES as a solvent. First, the eutectogel can guide zinc ions to preferentially deposit on the surface of (002) crystals, thus inhibiting the growth of dendrites and enabling the smooth operation of zinc-symmetric batteries for 800 h. Moreover, the eutectogel has sensitive sensing properties (gauge factor (GF) of 3.7) and excellent low dissipation performance (5.18 kJ m<sup>-3</sup>). The eutectogel can be used as a sensor for real-time monitoring of Parkinson's disease through machine learning (ML) techniques to effectively monitor the occurrence and severity of the disease. This work is expected to address the safety concerns of electrolyte dendrite growth in conventional zinc batteries, while providing innovative ideas for the integrated design of flexible sensors and high-safety energy storage systems. Bifunctional ; Dendrite (mathematics) ; Electrolyte ; Battery (electricity) ; Zinc ; Eutectic system ; Monomer ; Energy storage Conducting polymers and applications ; Advanced battery technologies research ; Advanced Sensor and Energy Harvesting Materials
Authors: Youcun Bai | Wenhao Liang | Heng Zhang
Source: Journal of Colloid and Interface Science (article, 2025-09-14)
Field: Chemistry — Inorganic Chemistry

Engineering strategies of MOFs-based materials for rechargeable batteries: Advances and perspectives Energy storage ; Electrochemical energy storage ; Battery (electricity) ; Renewable energy ; Electrochemical energy conversion ; Energy transformation ; Scalability Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Magnesium Oxide Properties and Applications
Authors: Sumner B. Harris | Patrick T. Gemperline | Christopher M. Rouleau | Rama K. Vasudevan | R. Comès
Source: Nano Letters (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Deep Learning with Reflection High-Energy Electron Diffraction Images to Predict Cation Ratio in Sr<sub>2<i>x</i></sub>Ti<sub>2(1–<i>x</i>)</sub>O<sub>3</sub> Thin Films Machine learning (ML) with in-situ diagnostics offers a transformative approach to accelerate, understand, and control thin film synthesis by uncovering relationships between synthesis conditions and material properties. In this study, we demonstrate the application of deep learning to predict the stoichiometry of Sr<sub>2<i>x</i></sub>Ti<sub>2(1-<i>x</i>)</sub>O<sub>3</sub> thin films using reflection high-energy electron diffraction images acquired during pulsed laser deposition. A gated convolutional neural network trained for regression of the Sr atomic fraction achieved accurate predictions with a small dataset of 31 samples. Explainable AI techniques revealed a previously unknown correlation between diffraction streak features and cation stoichiometry in Sr<sub>2<i>x</i></sub>Ti<sub>2(1-<i>x</i>)</sub>O<sub>3</sub> thin films. Our results demonstrate how ML can be used to transform a ubiquitous <i>in-situ</i> diagnostic tool, that is usually limited to qualitative assessments, into a quantitative surrogate measurement of continuously valued thin film properties. Such methods are critically needed to enable real-time control, autonomous workflows, and accelerate traditional synthesis approaches. Electron diffraction ; Reflection (computer programming) ; Diffraction ; Materials science ; X-ray crystallography ; Energy (signal processing) ; Crystallography ; Physics ; Optics ; Chemistry ; Computer science Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Advanced X-ray and CT Imaging
Authors: Oreoluwa Alade | Onuh Matthew Ijiga
Source: International Journal on Science and Technology (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

A Physics-Embedded AI Framework for Predictive Polymer Thermodynamics: Methodology, System Design, and Validation This research presents a Physics-Embedded Artificial Intelligence (AI) Framework for Predictive Polymer Thermodynamics, integrating machine learning models with fundamental thermodynamic equations to generate accurate, real-time predictions of polymer behavior across diverse temperature and pressure conditions. The system combines Graph Neural Networks, Physics-Informed Neural Networks, and ensemble predictive techniques with embedded constraints such as heat capacity derivatives and Gibbs free energy relationships, ensuring scientific consistency and preventing non-physical outputs. A fully interactive Graphical User Interface (GUI) was developed to support user-driven simulations, database management, explainability visualization, and uncertainty quantification. Comprehensive evaluations demonstrate that the physics-embedded AI model outperforms traditional thermodynamic methods in accuracy, stability, and computational efficiency. The predictive engine delivers smooth thermodynamic curves, low error rates, and reliable uncertainty bounds, while maintaining robustness under extreme and boundary conditions. The visualization and interaction modules enable intuitive exploration of thermodynamic trends through dynamic plots, tables, history logs, and feature importance analytics. The system contributes significantly to polymer science by accelerating computational workflows, improving interpretability, and reducing reliance on resource-intensive laboratory and simulation procedures. Industrial applications include polymer formulation, process optimization, and materials design, while academic and educational users benefit from its clarity, interactivity, and reproducibility. Despite limitations related to dataset availability, computational training costs, and interpretability complexity, the framework establishes a powerful foundation for future advances in AI-driven thermodynamic modeling. The findings highlight the potential of hybrid physics–AI approaches to transform material analysis and support next-generation digital tools for polymer engineering. Interpretability ; Robustness (evolution) ; Artificial neural network ; Visualization ; Consistency (knowledge bases) ; Process (computing) ; Predictive modelling ; Feature selection ; Ensemble forecasting Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Model Reduction and Neural Networks
Authors: Mengqi Cheng | Weidong Luo | Hong Sun
Source: Physical review. B./Physical review. B (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

On-the-fly machine learning augmented constrained <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>a</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:math> molecular dynamics to design routes from glassy carbon to quenchable amorphous diamond Recent advances in machine learning have enabled large-scale atomic simulations with first-principles accuracy, allowing precise modeling of disordered materials such as glassy carbon (GC). However, conventional ab initio molecular dynamics (AIMD) cannot effectively capture anisotropic stress effects, which are believed to play a key role in the transformation of GC into amorphous diamond under extreme conditions. In this work, we present an on-the-fly machine learning-augmented constrained AIMD (ML-augmented CAIMD) approach by modifying VASP 6.3.2. Our simulations not only reproduce major experimental features of GC but also provide restrictive synthesis conditions and microscopic insights. We show that GC exhibits unexpectedly high plasticity, with its compressive and shear strengths enhanced by large strains. Under pressure, increasing annealing temperature promotes the formation of quenchable amorphous diamond via enhanced sp3 preservation, but this trend reverses above 2900 K due to thermal graphitization. Under non-hydrostatic compression, GC transforms into a superhard structure sustaining large stress differences, which sharply increase when confining pressure exceeds 40 GPa. Finally, severe rotational shear at 30 GPa induces sp3 fractions up to 80 percent at 300 to 1000 K. A hardened amorphous carbon retaining 64 percent sp3 content is achieved by decompression at 300 K, marking the lowest pressure-temperature route ever predicted. Our ML-augmented CAIMD provides a general framework for modeling structural transformations in disordered materials under anisotropic stresses. Amorphous solid ; Amorphous carbon ; Molecular dynamics ; Anisotropy ; Diamond ; Shear (geology) ; Glassy carbon ; Annealing (glass) Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Catalysis and Oxidation Reactions
Authors: Ming Qu | Gyanendra Sharma | Naoki Wada | Hisaki Ikebata | Shigeyuki Matsunami | Kenji Takahashi
Source: Journal of Cheminformatics (article, 2025-05-21)
Field: Materials Science — Biomaterials

Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids. Cellulose ; Dissolution ; Ionic liquid ; Computer science ; Virtual screening ; Ionic bonding ; Data science ; Chemistry ; Biochemical engineering ; Process engineering ; Drug discovery ; Ion ; Organic chemistry ; Engineering ; Biochemistry Advanced Cellulose Research Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Ionic liquids properties and applications