jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mei Wang | Ting Liu | Han Liao | Xian-Biao Liu | Qi Zou | Hao-Cheng Liu | Xiao-Yin Wang
Source: Foods (article, 2026-01-24)
Field: Chemistry — Organic Chemistry

Adulteration Detection of Multi-Species Vegetable Oils in Camellia Oil Using SICRIT-HRMS and Machine Learning Methods We aimed to establish a rapid and precise method for identifying and quantifying multi-species vegetable oil (corn oil, olive oil (OLO), soybean oil, and sunflower oil (SUO)) adulterations in camellia oil (CAO), using soft ionization by chemical reaction in transfer-high-resolution mass spectrometry (SICRIT-HRMS) and machine learning methods. The results showed that SICRIT-HRMS could effectively characterize the volatile profiles of pure and adulterated CAO samples, including binary, ternary, quaternary, and quinary adulteration systems. The low <i>m</i>/<i>z</i> region (especially 100-300) exhibited importance to oil classification in multiple feature-selection methods. For qualitative detection, binary classification models based on convolutional neural networks (CNN), Random Forest (RF), and gradient boosting trees (GBT) algorithms showed high accuracies (98.70-100.00%) for identifying CAO adulteration under no dimensionality reduction (NON), principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) strategies. The RF algorithm exhibited relatively high accuracy (96.25-99.45%) in multiclass classification. Moreover, the five models, including CNN, RF, support vector machines (SVM), logistic regression (LR), and GBT, exhibited different performances in distinguishing pure and adulterated CAO. Among 1093 blind oil samples, under NON, PCA, and UMAP: 10, 5, and 67 samples were misclassified by CNN model; 6, 7, and 41 samples were misclassified by RF model; 8, 9, and 82 samples were misclassified by SVM model; 17, 18, and 78 samples were misclassified by LR model; 7, 9, and 43 samples were misclassified by GBT model. For quantitative prediction, the PCA-CNN model performed optimally in predicting adulteration levels in CAO, especially with respect to OLO and SUO, exhibiting a high coefficient of determination for calibration (RC2, 0.9664-0.9974) and coefficient of determination for prediction (Rp2, 0.9599-0.9963) values, low root mean square error of calibration (RMSEC, 0.9-5.3%) and root mean square error of prediction (RMSEP, 1.1-5.8%) values, and RPD (5.0-16.3) values greater than 3.0. These results indicate that SICRIT-HRMS combined with machine learning can rapidly and accurately identify and quantify multi-species vegetable oil adulterations in CAO, which provides a reference for developing non-targeted and high-throughput detection methods in edible oil authenticity. Random forest ; Pattern recognition (psychology) ; Support vector machine ; Dimensionality reduction ; Principal component analysis ; Vegetable oil ; Electronic nose ; Edible oil Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Biodiesel Production and Applications
Authors: Krsna Anand | Mohamad Rezi Abdul Hamid | Mohd F Ismail | Wan Azlina W.A.K.G | Musab Abdul Razak
Source: Materials Research Express (article, 2026-03-17)
Field: Chemistry — Inorganic Chemistry

A comprehensive bibliometric exploration of computational research on zeolitic-imidazolate frameworks over two decades Abstract The computational exploration of zeolitic-imidazolate frameworks (ZIFs) represents a paradigm shift in materials science, offering profound insights into the design and application of these versatile materials. This study undertakes a bibliometric analysis of 1529 articles from Scopus over the past 20 years to examine the growth and collaboration patterns of ZIFs and computational studies. To elucidate the evolutionary trajectory of ZIF research, this study employed methods such as descriptive statistics, co-authorship analysis, and keyword co-occurrence analysis, alongside calculations of citation metrics and bibliometric visualization using VOSviewer. This study highlights a significant expansion in the field, as evidenced by the increase from 0 publications in 2005 to 319 in 2024, indicating a rapid phase of development. China was the most prolific contributor, with 832 articles, led by the Beijing University of Chemical Technology and highlighted by Jiang J from the National University of Singapore. Highly cited studies span gas adsorption and separation, catalysis, and structural simulations, reflecting the interdisciplinary impact of ZIFs. In addition to mapping the evolution of the field, this review addresses current methodological challenges, including inconsistencies in force fields and limitations in the modeling flexibility. This review outlines opportunities for future progress, including machine learning-assisted simulations, multiscale modeling, and the development of standardized computational protocols. By consolidating bibliometric evidence and scientific insights, this review provides a comprehensive overview of computational ZIF research. This highlights the directions that will shape the next generation of ZIF-based materials. Bibliometrics ; Scopus ; Beijing ; Visualization ; Co-citation ; Citation ; Salient ; Scientometrics Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Covalent Organic Framework Applications
Authors: René D. Boisseau Gomez | Md Razaul Karim | Jesus A. Anaya | Jaeyoung Cho
Source: Polymer Degradation and Stability (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

AI-experiment-theory integrated study on char formation kinetics of polymeric ablative materials: Comparative study of phenolic resin vs. poly(p-phenylene oxide) Char ; Pyrolysis ; Polymer ; Kinetics ; Monomer ; Activation energy ; Reaction mechanism ; Yield (engineering) Silicone and Siloxane Chemistry ; Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes
Authors: Boxin Li | Shiping Zhu | Shengling Zhou | Hua Huang | Hanbin Liu | Hao Jiang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-01-26)
Field: Chemistry — Analytical Chemistry

Qualitative identification of Bombyx batryticatus and its counterfeits using hyperspectral imaging and deep learning Hyperspectral imaging ; Deep learning ; Convolutional neural network ; Bombyx mori ; Pattern recognition (psychology) ; Bombyx ; Identification (biology) ; Counterfeit Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Remote-Sensing Image Classification
Authors: Saharudin Haron | Muhammad Baharum
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Furnace Tube Rupture Prediction by Machine Learning Multiclass Method for 35Cr-45Ni-1Nb Tube Feature selection ; Artificial neural network ; Logistic regression ; Feature (linguistics) ; Tube (container) ; Failure mode and effects analysis ; Support vector machine ; Multiclass classification Nuclear Materials and Properties ; Nuclear Engineering Thermal-Hydraulics ; Risk and Safety Analysis
Authors: Takahito Ohshiro | Yuki Komoto | Masateru Taniguchi
Source: Analytical Sciences (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Beyond ensemble averages: single-entity approaches for complex systems Nanodevice ; Context (archaeology) ; Paradigm shift ; Complex system ; Interpretation (philosophy) ; Focus (optics) Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; CO2 Reduction Techniques and Catalysts
Authors: Ashim Paudel | Abin Shakya | Shubhadeep Nag | Bijaya B. Karki | Yaxin An
Source: ChemRxiv (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

A structured dataset of polymers in molecular dynamics simulations extracted by LLM-assisted text mining Molecular dynamics (MD) simulations are widely used to study polymer behavior, yet the resulting simulation data remain scattered across the literature and are rarely available in standardized, machine-readable formats. To address this gap, we present a curated dataset of polymer MD simulations reported in peer-reviewed publications from 1995-2025 using largelanguage-model (LLM)-assisted extraction followed by expert curation. Starting from an initial set of 48,206 abstracts retrieved through keyword-based bibliographic searches, automated screening narrowed the corpus to 755 polymer-MD-relevant publications. Among these, 198 papers reported simulation-derived values for one or more of the six target properties: density, glass transition temperature, radius of gyration, Young's modulus, diffusion coefficient, and viscosity, resulting in 1,095 unique records. Each record captures the polymer system, force field, and available physical properties. By aggregating dispersed literature into a centralized, standardized database, this work supports data-driven analysis, enhances reproducibility in molecular modeling of polymeric systems, and facilitates the development of automated and machine-learning-assisted simulation workflows. Molecular dynamics ; Set (abstract data type) ; Polymer ; Data set ; Glass transition ; Dynamics (music) ; Reproducibility Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Advanced Text Analysis Techniques
Authors: Javier Sanz Rodrigo | Nicholas A. Kryger-Nelson | Lena A. Mittmann | Eugene P. Bertin | Ivano E. Castelli | Andrea Crovetto
Source: ArXiv.org (article, 2026-01-23)
Field: Chemistry — Inorganic Chemistry

AI-enhanced discovery and accelerated synthesis of metal phosphosulfides Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials. Characterization (materials science) ; Workflow ; Ternary operation ; Density functional theory ; Band gap ; Domain (mathematical analysis) Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Srinivas Rangarajan
Source: Current Opinion in Chemical Engineering (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Learning catalytic kinetic models from data: current and emerging methods Kinetic models are ubiquitous in catalysis and can be formulated using ab initio calculations, kinetic studies, or a combination of experimental and computational sources. This opinion first discusses emerging methods of learning kinetic models from data, such as sparse discovery of governing equations, physics-informed neural networks, residual neural networks, and neural ordinary differential equations, in the context of conventional methods such as rate expressions and microkinetic models. Parameter learning techniques and emerging sources of kinetic data are subsequently presented, culminating in a vision for multimodal closed-loop discovery of kinetic models from spectrokinetic and computational data. Context (archaeology) ; Kinetic energy ; Artificial neural network ; Ab initio ; Residual ; Computational model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Javier Sanz Rodrigo | Nicholas A. Kryger-Nelson | Lena A. Mittmann | Eugene P. Bertin | Ivano E. Castelli | Andrea Crovetto
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Chemistry — Inorganic Chemistry

AI-enhanced discovery and accelerated synthesis of metal phosphosulfides Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials. Characterization (materials science) ; Characterization (materials science) ; Workflow ; Workflow ; Ternary operation ; Ternary operation ; Density functional theory ; Density functional theory Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Lois Smith | Samuel Ericson | Vittoria Fantauzzo | Chin W. Yong | Paola Carbone | Alessandro Troisi
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Automated High-Throughput Screening of Polymers Using a Computational Workflow High-throughput computational screening of polymers offers a powerful way to address the imbalance between the vast number of polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic simulations. This work presents an automatic workflow designed to enable the rapid and efficient screening of an extensive polymer library. The workflow integrates an automated annealing protocol with adaptive control, allowing for reproducible simulations with minimal human intervention and minimisation of the computational cost. The availability of a homogenous large set of simulations enables the adoption of machine learning approaches for a variety of tasks. We exemplify this possibility by proposing rapid machine-learning-based method to predict the (computed) polymer density and (experimental) glass transition temperature. Workflow ; Polymer ; Simulated annealing ; Set (abstract data type) ; Computational model Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Block Copolymer Self-Assembly
Authors: Andong Hu | Luca Pennati | Stefano Markidis | Ivy Peng
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Enabling AI Deep Potentials for Ab Initio-quality Molecular Dynamics Simulations in GROMACS State-of-the-art AI deep potentials provide ab initio-quality results, but at a fraction of the computational cost of first-principles quantum mechanical calculations, such as density functional theory. In this work, we bring AI deep potentials into GROMACS, a production-level Molecular Dynamics (MD) code, by integrating with DeePMD-kit that provides domain-specific deep learning (DL) models of interatomic potential energy and force fields. In particular, we enable AI deep potentials inference across multiple DP model families and DL backends by coupling GROMACS Neural Network Potentials with the C++/CUDA backend in DeePMD-kit. We evaluate two recent large-atom-model architectures, DPA2 that is based on the attention mechanism and DPA3 that is based on GNN, in GROMACS using four ab initio-quality protein-in-water benchmarks (1YRF, 1UBQ, 3LZM, 2PTC) on NVIDIA A100 and GH200 GPUs. Our results show that DPA2 delivers up to 4.23x and 3.18x higher throughput than DPA3 on A100 and GH200 GPUs, respectively. We also provide a characterization study to further contrast DPA2 and DPA3 in throughput, memory usage, and kernel-level execution on GPUs. Our findings identify kernel-launch overhead and domain-decomposed inference as the main optimization priorities for AI deep potentials in production MD simulations. Deep learning ; Molecular dynamics ; Inference ; Artificial neural network ; Throughput ; Overhead (engineering) ; Interatomic potential ; Energy minimization Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Liming Wu | Rui Jiao | Qi Li | Mingze Li | Songyou Li | Shifeng Jin | Wenbing Huang
Source: Open MIND (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

DMFlow: Disordered Materials Generation by Flow Matching The design of materials with tailored properties is crucial for technological progress. However, most deep generative models focus exclusively on perfectly ordered crystals, neglecting the important class of disordered materials. To address this gap, we introduce DMFlow, a generative framework specifically designed for disordered crystals. Our approach introduces a unified representation for ordered, Substitutionally Disordered (SD), and Positionally Disordered (PD) crystals, and employs a flow matching model to jointly generate all structural components. A key innovation is a Riemannian flow matching framework with spherical reparameterization, which ensures physically valid disorder weights on the probability simplex. The vector field is learned by a novel Graph Neural Network (GNN) that incorporates physical symmetries and a specialized message-passing scheme. Finally, a two-stage discretization procedure converts the continuous weights into multi-hot atomic assignments. To support research in this area, we release a benchmark containing SD, PD, and mixed structures curated from the Crystallography Open Database. Experiments on Crystal Structure Prediction (CSP) and De Novo Generation (DNG) tasks demonstrate that DMFlow significantly outperforms state-of-the-art baselines adapted from ordered crystal generation. We hope our work provides a foundation for the AI-driven discovery of disordered materials. Generative grammar ; Matching (statistics) ; Benchmark (surveying) ; Discretization ; Representation (politics) ; Generative model ; Homogeneous space ; Flow (mathematics) ; Field (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: M. Scott Trimboli | Mohammed Alsubaie | Sirani M. Perera | K. G. Wang | Xianqi Li
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulation methods, such as phase-field models, offer high-fidelity results but are computationally expensive due to the need to solve complex partial differential equations at fine spatiotemporal resolutions. To address this challenge, we propose a deep learning-based framework that accelerates microstructure evolution predictions while maintaining high accuracy. Our approach utilizes a fully convolutional spatiotemporal model trained in a self-supervised manner using sequential images generated from simulations of microstructural processes, including grain growth and spinodal decomposition. The trained neural network effectively learns the underlying physical dynamics and can accurately capture both short-term local behaviors and long-term statistical properties of evolving microstructures, while also demonstrating generalization to unseen spatiotemporal domains and variations in configuration and material parameters. Compared to recurrent neural architectures, our model achieves state-of-the-art predictive performance with significantly reduced computational cost in both training and inference. This work establishes a robust baseline for spatiotemporal learning in materials science and offers a scalable, data-driven alternative for fast and reliable microstructure simulations. Generalization ; Convolutional neural network ; Artificial neural network ; Deep learning ; Baseline (sea) ; Differential evolution ; Statistical learning Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Model Reduction and Neural Networks
Authors: M. Scott Trimboli | Mohammed Alsubaie | Sirani M. Perera | K. G. Wang | Xianqi Li
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulation methods, such as phase-field models, offer high-fidelity results but are computationally expensive due to the need to solve complex partial differential equations at fine spatiotemporal resolutions. To address this challenge, we propose a deep learning-based framework that accelerates microstructure evolution predictions while maintaining high accuracy. Our approach utilizes a fully convolutional spatiotemporal model trained in a self-supervised manner using sequential images generated from simulations of microstructural processes, including grain growth and spinodal decomposition. The trained neural network effectively learns the underlying physical dynamics and can accurately capture both short-term local behaviors and long-term statistical properties of evolving microstructures, while also demonstrating generalization to unseen spatiotemporal domains and variations in configuration and material parameters. Compared to recurrent neural architectures, our model achieves state-of-the-art predictive performance with significantly reduced computational cost in both training and inference. This work establishes a robust baseline for spatiotemporal learning in materials science and offers a scalable, data-driven alternative for fast and reliable microstructure simulations. Generalization ; Convolutional neural network ; Artificial neural network ; Deep learning ; Baseline (sea) ; Differential evolution ; Statistical learning Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Model Reduction and Neural Networks