jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Simone Manai | Laura Gemme | Luca Savio | Jörg Martin
Source:  (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

AI-Driven Workflow for Chemical Compounds Classification from IR Spectra of Solutions This work presents an AI-driven workflow for the classification of chemical compounds using near-infrared (NIR) spectroscopy of solutions. This approach addresses the challenge of identifying specific substances in complex spectral data, particularly at low concentrations. By leveraging data augmentation techniques such as noise addition and spectral transformations, the study enhances the robustness and accuracy of AI models in spectral analysis. Experimental validation was performed using substances like Progesterone, Indol-3 acetic acid (IAA), $\beta$-Estradiol and Bisphenol A, dissolved in methanol and measured with Fourier-transform NIR spectroscopy. The proposed workflow provides promising results for substance identification. Workflow ; Computer science ; Spectral line ; Artificial intelligence ; Pattern recognition (psychology) ; Physics ; Database Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Efi-Maria Papia | Alex Kondi | Dimitrios Nioras | Filippos Sofos | V. Constantoudis
Source: Computational Materials Science (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Data-driven investigation of analytical relationships in moiré structured materials Moiré patterns, created by overlapping periodic structures, offer tunable structural properties, making them essential in fields like materials science and electronics. Their sensitivity to geometric parameters enables the design of advanced materials with specific functionalities. However, modeling the complex relationships between these parameters and material properties is challenging. Symbolic regression, a data-driven approach, addresses this by autonomously discovering mathematical expressions without predefined models, capturing intricate dependencies. This study employs an analytical formula development and then symbolic regression on simulated Moiré hexagonal patterns to model the relationship of shade coefficient, a parameter quantifying material coverage, with geometric variables, demonstrating the method's potential to enhance material design and understanding in complex geometries. The formula output by symbolic regression is validated with a test dataset and applied to real experimental images of layered nylon meshes. • Moiré patterns offer tunable material properties via geometric layering. • Symbolic regression models Moiré pattern shade coefficients from geometric data. • Outperforms traditional theoretical models in complex configurations. • Validated on unseen test data and real experimental images. • Enables interpretable, data-driven design of patterned materials. Symbolic regression ; Hexagonal crystal system ; Geometric modeling ; Sensitivity (control systems) ; Characterization (materials science) ; Regression ; Regression analysis ; Material properties ; Geometric shape Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Hongtao Zhang | Huadong Fu | Jingtai Sun | Lei Jiang | Shuaicheng Zhu | Jianxin Xie
Source: Acta Materialia (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Data-driven multi-process modeling and integrated design strategy for complex high-performance alloys Alloy ; Process (computing) ; Integrated design ; Stacking ; Process design ; Ultimate tensile strength ; Work (physics) ; Cascade Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Additive Manufacturing Materials and Processes
Authors: jie LIU | QI ji | Shiyan GAO | Wenyi CHEN | Yuexin SUI | Zemin HE | Haiyan YANG | Zongcheng MIAO
Source: Chinese Journal of Liquid Crystals and Displays (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in data-driven research on liquid crystal materials Materials science ; Computer science Liquid Crystal Research Advancements
Authors: Jiayi Zhu (547089) | Li-Hong Lin (20999061) | Haoren Niu (20520677) | Qiaoyan Shang (1637485) | Qiang Wang (32383) | Fangyou Yan (1530637) | Jin-Jin Li (1493293)
Source: Figshare (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Data-Driven Predictionof Flory–Huggins Parameterfor Quantifying Polymer–Solvent Interaction The Flory–Huggins interaction parameter is an influential thermodynamic parameter for quantifying polymer–solvent interaction. The data-driven quantitative structure–property relationship (QSPR) model provides a rapid and accurate route to obtain the Flory–Huggins parameter of polymer–solvent mixtures. To evaluate the correlation between the polymer–solvent structure and the Flory–Huggins parameter, we developed a QSPR model based on 29 norm descriptors. To overcome the diversity of structural representations of a polymer, a ring repeating unit is adopted for unique structural representation of the polymers. Considering the effects of polymers, solvents, and polymer–solvent interactions on the Flory–Huggins parameters, the descriptors are divided into three parts. The results of statistical parameter analysis indicate that the QSPR model exhibits good prediction performance (<i>R</i><sup>2</sup><sub>test</sub> = 0.9348, AAE<sub>test</sub> = 0.1943) and robustness (<i>Q</i><sup>2</sup><sub>LOO‑CV</sub> = 0.9177). This work offers a quantitative reference and a tool to understand polymer–solvent interactions. Quantitative structure–activity relationship ; Robustness (evolution) ; Representation (politics) ; Estimation theory ; Experimental data ; Statistical model ; Work (physics) ; Model parameter Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Lin Ma | Kang Xu
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

( <i>Invited</i> ) Searching for Ideal Electrolytes in the Molecular Universe via AI In the past three decades since the invention of lithium-ion batteries, extensive efforts have been made to seek ideal electrolyte components that can resolve the many challenges faced by emerging battery chemistries. However, scientists have been confined to a rather narrow chemical space of 10 2 , most of which were either discoveries of serendipity, or from human-designs in empirical instead of rational manners. Leveraging the rapid development of artificial intelligence, we seek to map the entire molecular universe of small organic compounds computationally, and rapidly identify those most promising candidates for the next-generation batteries. In this talk I will summarize my recent adventure at SES AI, where we built immense databases of battery literature, chemical structures, and properties, mapped the molecular as well as ionic universes of cosmic scales, and trained an omniscient large-language-model to explore the large maps. Some of the AI-discovered/-designed molecules have already been synthesized, tested and outperformed human-designed electrolyte materials. Ideal (ethics) ; Chemical space ; Universe ; Space (punctuation) ; Adventure ; Electrolyte Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Inorganic Chemistry and Materials
Authors: Maziyar Sabet
Source: Journal of Materials Science Materials in Electronics (article, 2025-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Scalable graphene-based hybrid electrodes for supercapacitors: experimental synthesis, characterization, and a conceptual ai-guided optimization framework Polyaniline ; Graphene ; Electrode ; Supercapacitor ; Capacitance ; Power density ; Scalability ; Sheet resistance ; Conductivity Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Ronald Marquez (13871511) | Blaise L. Tardy (2553544) | Roberto J. Aguado (14671561) | Giovana Signori-Iamin (22663122) | Esther Argelagós (22663125) | Marc Delgado-Aguilar (14671576)
Source: Figshare (article, 2025-11-21)
Field: Materials Science — Biomaterials

Artificial Intelligence(AI)-Augmented “Living”Meta-Analyses toward Critical Thinking Engagement in Chemical Educationand Research: A Case Study of Nanocellulose-Stabilized Pickering Emulsions As research on sustainable and advanced materials accelerates (e.g., cellulose-based composites and functionalized nanomaterials), research output has expanded rapidly, increasing complexity and making it challenging for industrial and engineering chemistry researchers to maintain comprehensive, up-to-date data compilation and analysis. Therefore, traditional meta-analyses, even when attempting to adhere to systematic methodologies such as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), face challenges with manual data curation, risking oversights and impacting reproducibility in the face of such volume. Updated meta-analysis methodologies are necessary to critically assess advances and new technologies for the upscaling processes and innovation in the advanced materials field. To address this, we propose a novel framework for creating “living” meta-analyses augmented by artificial intelligence (AI). We explore these concepts via a tutorial case study on nanocellulose-stabilized Pickering emulsions, illustrating how the integration of AI-based extraction with bibliometric mapping reveals patterns, identifies research gaps, and enables the deployment of informed decisions for future research and accelerated product development in academia and industry. Integrating Large Language Models (LLMs), such as ChatGPT and Gemini, with bibliometric platforms (e.g., ACS CAS SciFinder, Scopus, Dimensions) and VOSviewer, allowed one to systematically curate and synthesize data from over 50 publications. The resulting interactive platform reveals complex relationships among nanocellulose properties (e.g., type, modification, concentration), processing conditions, and emulsion characteristics (e.g., droplet size, stability). The database and software are available at 10.5281/zenodo.15808694. We critically discuss the current limitations of LLMs in performing meta-analyses in the chemical engineering and advanced materials fields and emphasize the role of “human-in-the-loop” expertise in interpretation. Pickering emulsion ; Software deployment ; New product development ; Face (sociological concept) ; Product (mathematics) ; Applications of artificial intelligence ; Critical thinking Advanced Cellulose Research Studies ; Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Simin Huang | Xiaotian Hu | Jinhang Liu | Chunmei Huang
Source:  (article, 2025-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances in nickel-based metal-organic framework (MOF) electrode materials for supercapacitors and their impact on energy management in computer and artificial intelligence systems This review summarizes the latest advancements in nickel-based metal-organic framework (Ni-MOF) electrode materials for supercapacitors and discusses their impact on energy management in computer and artificial intelligence (AI) systems. It highlights a significant breakthrough in directly growing binder-free MOFs on nickel foam substrates, which enhances the conductivity and stability of the electrodes by eliminating the need for additional binders, thereby improving energy density and power output. Furthermore, the article explores a diverse range of materials prepared from Ni-MOF precursors, including Ni-based oxides/hydroxides, sulfides, phosphides, and carbides, which exhibit tailored electrochemical properties and cycling stability. These materials provide unique design opportunities for supercapacitors to meet the stringent requirements of modern computing architectures, such as edge computing devices, AI accelerators, and high-performance computing clusters. This review not only emphasizes the potential of Ni-MOFs in achieving high-performance supercapacitors but also points out their adaptability to a wide range of electrochemical conditions, making them ideal candidate materials for applications where energy density, cycle life, and operating temperature range are critical. Supercapacitor ; Nickel ; Materials science ; Metal-organic framework ; Electrode ; Computer science ; Nanotechnology ; Process engineering ; Metallurgy ; Engineering ; Capacitance ; Chemistry Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Amit M. Deshpande
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Data-driven AI-enabled Approach to Predict Crystal Morphology Evolution in Heterogeneous Semicrystalline Thermoplastic Composites A data-driven, AI-enabled approach to predict crystal morphology evolution in fiber reinforced semi-crystalline thermoplastic composites has been presented. Thermoplastic composites ; Morphology (biology) ; Thermoplastic ; Crystallinity ; Crystal (programming language) ; Fiber ; Crystal structure Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Composite Material Mechanics
Authors: Amit M. Deshpande
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Data-driven AI-enabled Approach to Predict Crystal Morphology Evolution in Heterogeneous Semicrystalline Thermoplastic Composites A data-driven, AI-enabled approach to predict crystal morphology evolution in fiber reinforced semi-crystalline thermoplastic composites has been presented. Thermoplastic composites ; Morphology (biology) ; Thermoplastic ; Crystallinity ; Crystal (programming language) ; Fiber ; Crystal structure Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Composite Material Mechanics
Authors: Valentín Vassilev-Galindo | Javier LLorca
Source: ChemRxiv (preprint, 2025-07-16)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for materialsdiscovery: application to catalysts for the HERand ORR Set (abstract data type) ; Embedding ; Coupling (piping) ; Counterfactual conditional ; Work (physics) ; Energy (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Junchao Ren | Yin Wan
Source: Processes (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Data-Driven Soft Sensor Model Based on Multi-Timescale Feature Fusion for Crystal Quality Prediction in Czochralski Process The accurate real-time prediction of the crystal quality index v/G is an important reference for the real-time monitoring of the growth quality status and the process optimization adjustment of semiconductor silicon single crystals. This paper proposes a data-driven crystal quality indicator v/G soft sensor prediction model based on multi-timescale feature fusion to achieve the effective prediction of the crystal quality indicator v/G. Firstly, the characteristics of the crystal quality index v/G in the growth process of Czochralski silicon single crystal are analyzed. Secondly, the crystal quality index v/G is broken down into several natural components using something called complete ensemble empirical mode decomposition with adaptive noise (CEEMDAD), which provides more stable data. On this basis, each intrinsic mode component is reconstructed according to the sample entropy. Then, the maximum mutual information coefficient (MIC) method is applied to identify the characteristic variables most closely associated with each reconstructed component of the crystal quality index v/G from the process-influencing factors. Then, a long short-term memory network with a self-attention mechanism is used to establish a prediction model of the reconstructed components to extract the multi-timescale feature information from the different components of the crystal quality index v/G. Finally, the prediction results of the crystal quality index v/G are obtained by fusing each subsequent prediction model. According to the actual field data, the comprehensive experimental results validate the efficacy of the proposed soft sensor modeling method for the crystal quality index v/G. Compared with the single model, the proposed prediction model has a smaller MAE, RMSE, and prediction performance index and a higher HR prediction hit rate. Soft sensor ; Fusion ; Process (computing) ; Sensor fusion ; Feature (linguistics) ; Materials science ; Quality (philosophy) ; Crystal (programming language) ; Computer science ; Data mining ; Biological system ; Pattern recognition (psychology) ; Process engineering ; Artificial intelligence ; Engineering ; Physics Crystallization and Solubility Studies
Authors: Mirta R. Alcaraz | Elisabet Martín-Tornero | Héctor C. Goicoechea | Arsenio Muñoz de la Peña | Isabel Durán Merás
Source: Talanta (article, 2025-12-22)
Field: Chemistry — Spectroscopy

Comprehensive chemical fingerprinting by LC×LC-fluorescence and data-driven chemometric modelling for unsupervised classification Chemometrics ; Principal component analysis ; Hierarchical clustering ; Cluster analysis ; Liquid chromatography–mass spectrometry ; Pattern recognition (psychology) ; Sample (material) ; Fluorescence ; Sample preparation Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Balaji Sriram | Nandini Gupta
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

A Data-Driven Approach to the Estimation of Charge Transport Parameters in Polymeric Dielectrics Analytical and numerical techniques are used to study the charge dynamics in insulation systems. These methods are complex and computationally tedious. In recent years, Physics-Informed-Learning (PIL) methods such as the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm have been used to formulate appropriate Partial Differential Equations (PDEs) to describe dynamical systems. Such methods are able to rapidly estimate parameters with reduction in computational complexity. In this work, a methodology for the application of the SINDy algorithm to the charge transport in polymeric dielectrics is developed. The equations of charge transport (such as the non-linear convection diffusion equations) and the charge transport parameters (such as mobility and diffusivity) are discovered (rather than assumed), from the space charge data, using the SINDy algorithm. The methodology is first developed using synthesized computational data. It is shown that the spatial/temporal resolution of input data impacts the discovery of Nonlinear-Partial Differential Equations (PDEs) and consequently the estimation of sparse coefficients using SINDy. At high spatio-temporal resolutions of input data, the SINDy model proves to be over 90% accurate; however, accuracy decreases with input data resolution. Finally, the model is applied to space charge data experimentally obtained from Pulsed-Electro-Acoustic (PEA) measurements. Despite the low resolution of PEA data, the SINDy algorithm correctly identifies important features of the charge transport phenomenon from experimental data. The estimates are validated against computational data obtained from a Bipolar Charge Transport (BCT) model. This study illustrates the potential of data driven techniques to study the space charge phenomena in dielectrics. Space charge ; Charge (physics) ; Nonlinear system ; Charge conservation ; Diffusion ; Partial differential equation ; Dielectric ; Differential equation ; Numerical analysis High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Model Reduction and Neural Networks
Authors: Yejie Qiao | Xin Li | Han Zhao | Chao Zhang
Source: Aerotecnica Missili & Spazio (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Comparison of Data-Driven Approach and MAT224 Material Model: A Numerical Study on the Ballistic Impact Behavior of 2024-T351 Aluminum Plates Ballistic impact ; Materials science ; Aluminium ; Structural engineering ; Composite material ; Engineering ; Composite number High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Energetic Materials and Combustion
Authors: Raymond P. H. Wu | S. Otsuki | Huanmei Wu
Source: Materials Today Electronics (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

The advancement of materials discovery through the applied artificial intelligence In the past decade, several new informatics models have been introduced in materials science, offering rapid predictions instead of starting from scratch. Our research aims to achieve the optimization between targeted yield and likelihood. We gathered a matrix of 565 × 264 (264 features and 565 records) of laboratory data and conducted a series of experiments supported by various machine learning (ML) design patterns. Our research investigates the methods in the areas of 1). data foundation (data creation, data cleansing, data validation, and encoding/decoding); 2). model evaluations, including XGBoost (XGB), random forest (RF), and support vector machines (SVMs); 3). feature evaluation (clustering, feature selection, and feature weight evaluation); and 4). Bayesian optimization, optimization, and masking processes. Through this research, we explore the genuine methods in Materials Informatics (MI), and our methods such as Regression Deviation Degree Threshold (RDDT) and R:M ratio in data quality and measurement, and masking process in optimization process are novel in MI. Applications of artificial intelligence ; Expert system ; Key (lock) ; Field (mathematics) ; Automation Machine Learning in Materials Science ; Big Data and Digital Economy ; X-ray Diffraction in Crystallography