jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lin He | Xiaoqi Cui | Binxiu Dai | Jiawei Chen | P. P. Su | Zhuo Su | Huihui Hu | Yibin Jiang | Cheng Wang
Source: ACS Central Science (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Reasoning Language Model as Rule Finder: A Case Study on 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. Metal-organic framework ; Computer science ; Natural language processing ; Rule-based system ; Artificial intelligence ; Chemistry ; Data mining ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials
Authors: Konstantin S. Jakob | Aron Walsh | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery Recent computational materials discovery efforts have led to an enormous number of predictions of previously unknown, potentially stable inorganic, crystalline compounds. In particular, both high-throughput screenings and generative models have benefited tremendously from recent advances in computational resources and available data. However, these efforts are currently limited to predicting pristine crystalline materials. As a consequence, many of these predictions cannot be realized in experiments, where kinetic effects, defects, and crystallographic disorder can be crucial. To address this shortcoming, the current work aims to introduce disorder into computational materials discovery with machine learning (ML) based classification models. Trained on the inorganic crystal structure database (ICSD), these classifiers capture the chemical trends of crystallographic disorder and estimate the prevalence of disorder in computational databases produced by the Materials Project or Graph Networks for Materials Science (GNoME) initiatives. This opens the door toward disorder-aware computational materials discovery workflows, bridging the gap between prediction and experiment. Bridging (networking) ; Materials science ; Crystallography ; Computer science ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ying Xu | Ziyu Li | Qi Yang | Xi‐Guan Zhao | Qian Li | Sheng‐Gui He
Source: The Journal of Physical Chemistry A (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Machine Learning Study of Methane Activation by Gas-Phase Species The activation and transformation of methane have long posed significant challenges in scientific research. The quest for highly active species and a profound understanding of the mechanisms of methane activation are pivotal for the rational design of related catalysts. In this study, by assembling a data set encompassing a total of 134 gas-phase metal species documented in the literature for methane activation via the mechanism of oxidative addition, machine learning (ML) models based on the backpropagation artificial neural network algorithm have been established with a range of intrinsic electronic properties of these species as features and the experimental rate constants of the reactions with methane as the target variables. It turned out that the satisfactory ML models could be described in terms of four key features, including the vertical electron detachment energy (VDE), the absolute value of the energy gap between the highest occupied molecular orbital of CH<sub>4</sub>, and the lowest unoccupied molecular orbital of the metal species (|Δ<i>E</i><sub>H'-L</sub>|), the maximum natural charge of metal atoms (<i>Q</i><sub>max</sub>), and the maximum electron occupancy of valence s orbitals on metal atoms (<i>n</i><sub>s_max</sub>), based on the feature selection complemented with manual intervention. The stability and generalization ability of the constructed model was validated using a specially designed data-splitting strategy and newly incorporated data. This study proved the feasibility and discussed the limitations of the ML model, which is described by four key features to predict the reactivity of metal-containing species toward methane through oxidative addition mechanisms. Furthermore, a careful preparation of the training data set that covers the full expected range of target and feature values aiming to achieve good predictive accuracy is suggested as a practical guideline for future research. Methane ; Atomic orbital ; Valence (chemistry) ; Chemistry ; Metal ; Chemical physics ; Catalysis ; Computational chemistry ; Electron ; Physics ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Surafel Alayou | Mekdes Mengesha | Getachew Tizazu
Source: Measurement (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Application of machine learning models for predicting zinc oxide nanoparticle size Nanoparticle ; Zinc ; Machine learning ; Materials science ; Particle size ; Computer science ; Artificial intelligence ; Nanotechnology ; Metallurgy ; Engineering ; Chemical engineering Nanoparticles: synthesis and applications ; Water Quality Monitoring and Analysis ; Spectroscopy and Chemometric Analyses
Authors: U. Saleem | Shaama Mallikarjun Sharada
Source: The Journal of Physical Chemistry C (article, 2025-04-26)
Field: Materials Science — Materials Chemistry

Anomalous Diffusion of Metal Atoms on Oxide Surfaces: A Machine Learning Molecular Dynamics Study of Pt<sub>1</sub>/TiO<sub>2</sub> We investigate the high-temperature evolution of the metal–support interface in atomically dispersed catalysts to probe the dynamical evolution of the metal atom. Since high computational costs limit the time scales of ab initio molecular dynamics (AIMD) trajectories to a few picoseconds, we deploy a machine learned interatomic potential (MLIP), trained on AIMD data, using the FLARE program. We generate multiple trajectories spanning at least 50 ps each for atomically dispersed Pt on rutile TiO2 (110), initiated at different positions on the stoichiometric surface. We find that the motion of Pt is subdiffusive (not Brownian) in most cases, quantified by the parameters constituting the fractional Fokker-Planck equation. The subdiffusive behavior originates in strong interactions of Pt with bridging oxygen atoms of the support. Several trajectories show that Pt mobility is quenched when it coordinates with two bridging oxygen atoms to form a near-linear O–Pt–O complex. Diffusion that resembles Brownian motion is observed only at the highest simulation temperature examined (1000 K) for sites at which Pt is far from a bridging oxygen. The study therefore shows that in the absence of surface defects or adsorbates, the thermal stability of the metal atom is determined by coordination with bridging oxygen atoms. Oxide ; Diffusion ; Metal ; Molecular dynamics ; Materials science ; Chemical physics ; Dynamics (music) ; Physical chemistry ; Nanotechnology ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Metallurgy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Jianguo Zhang | Jiachen Sun | Jinghua Wu | Qiang Wang | Fupeng Wang
Source: Infrared Physics & Technology (article, 2025-03-19)
Field: Chemistry — Spectroscopy

Deep learning based spectral overlap separation for dual gases CO2/H2O detection with a 2004 nm laser source Dual (grammatical number) ; Laser ; Separation (statistics) ; Materials science ; Optics ; Source separation ; Dual diagnosis ; Computer science ; Optoelectronics ; Artificial intelligence ; Physics ; Machine learning Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Laser Design and Applications
Authors: R. Alagulakshmi | R. Ramalakshmi | V. Arumugaprabu | Geetha Palani
Source: International Journal of Material Forming (article, 2025-08-12)
Field: Materials Science — Polymers and Plastics

A comparative analysis on usage of ANN and SVR algorithms for predicting the mechanical properties of natural fiber-based composites using experimental data Algorithm ; Materials science ; Fiber ; Computational intelligence ; Artificial neural network ; Composite material ; Computer science ; Artificial intelligence Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Hirokatsu Miyata | K. Manabe | Yoshiyuki Sugahara | Yusuke Asakura | Jie Wang | Watcharop Chaikittisilp | Yusuke Yamauchi
Source: ACS Applied Materials & Interfaces (article, 2025-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-Learning-Guided Multiscale Design of Nitrogen-Doped Carbon for Enhanced Supercapacitor Performance Hierarchical structural control of nanomaterials across multilength scales is critical for optimizing mass and charge transport in energy-related applications. In this study, we combine machine learning analysis and rational synthesis to design nitrogen-doped carbonaceous hybrid materials with multiscale architectures tailored for supercapacitor electrodes. Data-driven insights from the literature reveal that, in addition to surface area, the presence of pyrrolic nitrogen strongly correlates with enhanced capacitance. Guided by this finding, we synthesized nanohybrids through the heterogeneous self-assembly of dopamine and block copolymers on graphene oxide (GO) nanosheets, followed by polymerization and thermal carbonization. Dopamine serves as both the carbon and nitrogen sources and facilitates the formation of mesoporous structures. The resulting hybrids feature atomic-scale nitrogen doping (10<sup>-10</sup> m), mesoporous carbon networks (10<sup>-8</sup> m), and macroscopic sheet-like morphology (10<sup>-6</sup> m) with tunable thickness. Uniform coating of mesoporous N-doped carbon on both sides of the reduced GO increases the accessible surface area while maintaining ion mobility. This work demonstrates a data-informed strategy for designing hierarchical carbon-based materials and highlights the synergy between machine learning and multiscale structural engineering for high-performance energy storage applications. Materials science ; Supercapacitor ; Carbon fibers ; Nanotechnology ; Doping ; Chemical engineering ; Capacitance ; Composite material ; Optoelectronics ; Composite number ; Electrode ; Physical chemistry Supercapacitor Materials and Fabrication ; Mesoporous Materials and Catalysis ; Nanomaterials for catalytic reactions
Authors: Chunhong Ma | Yuying Zhang
Source: Symmetry (article, 2025-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Progress in High-Entropy Alloy-Based Microwave Absorbing Materials The rational design of high-performance microwave absorbers with broadband coverage, superior attenuation, and environmental durability is critical for addressing challenges in both defense and civilian technologies. High-entropy alloys (HEAs) exhibit atomic-scale asymmetric arrangements, demonstrating exceptional potential for microwave absorption through their unique lattice distortion, high entropy, sluggish diffusion, and “cocktail effect”. This critical review article provides an overview of the progress made in the development and understanding of HEA-based microwave absorbing materials. Initially, the microwave dissipation mechanisms for HEAs were analyzed, where atomic-scale distortions enhance polarization loss and broaden resonance bandwidth. Subsequently, key synthesis techniques like mechanical alloying and carbothermal shock are discussed, highlighting non-equilibrium processing for phase engineering. Building on these foundations, the discussion then progresses to evaluate four principal material design approaches: (1) compositionally-tuned powders, (2) multifunctional core–shell structures, (3) phase-controlled architectures, and (4) two-dimensional/porous configurations, each demonstrating distinct performance advantages. Finally, the discussion concludes by addressing current challenges in quantitative property modeling and industrial scalability while outlining future directions, including machine learning-assisted design and flexible integration, providing comprehensive guidance for developing next-generation high-performance microwave absorbing materials. Microwave ; Materials science ; Scalability ; Computer science ; Broadband ; Nanotechnology ; Engineering physics ; Mechanical engineering ; Process engineering ; Telecommunications ; Engineering Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties
Authors: Vlad Cărare | Fabian L. Thiemann | Joe D. Morrow | David J. Wales | Edward O. Pyzer‐Knapp | Luke Dicks
Source: npj Computational Materials (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Global properties of the energy landscape: a testing and training arena for machine learned potentials Abstract Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics, critical for reaction rate calculations, remains largely unexplored. We introduce Landscape17, a dataset of complete kinetic transition networks (KTNs) for the six molecules of the rMD17 dataset, computed using hybrid-level density functional theory. Each KTN contains minima, transition states, and approximate steepest-descent paths, along with energies, forces, and Hessian eigenspectra at stationary points. We develop a comprehensive test suite to evaluate the MLIPs’ ability to reproduce these reference landscapes and apply it to state-of-the-art architectures. Our results reveal limitations in current MLIPs: all models considered miss over half of the DFT transition state paths and generate stable unphysical structures throughout the potential energy surface. Data augmentation with pathway configurations improves reproduction of DFT potential energy surfaces, resulting in significant improvement in global kinetics. However, these models still produce many spurious stable structures, indicating that current MLIP architectures face underlying challenges in capturing the topology of molecular potential energy surfaces. The Landscape17 benchmark provides a straightforward, lightweight, but demanding test of MLIPs for kinetic applications, and we propose this test for validation of next-generation MLIPs targeting reaction discovery and rate prediction. Spurious relationship ; Benchmark (surveying) ; Kinetic energy ; Energy (signal processing) ; Hessian matrix ; Potential energy ; Current (fluid) ; Face (sociological concept) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: K. Shibata | Fernando Garcia‐Escobar | Tomoya Tashiro | Lauren Takahashi | Keisuke Takahashi
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Designing and synthesizing perovskites with targeted bandgaps <i>via</i> tailored descriptors Descriptors that govern the bandgaps of perovskite-type oxides are identified by analyzing experimentally reported materials, focusing on compositional, structural, and electronic features relevant to solar energy conversion. These descriptors form the basis of a machine learning model that predicts bandgaps across a wide chemical space. Several compositions with targeted optical properties are predicted and subsequently synthesized. Structural and optical characterization studies confirm the formation of the predicted phases and the bandgap. Thus, this work demonstrates that the descriptor-driven, data-guided workflow accelerates the discovery of photoactive perovskites for solar energy conversion and visible-light-driven applications. Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Guoqi Zhang | Wencai Jiang | Xiao‐Mei Li | Renjie Li | Yi Feng | Ri-Ge Li Nai | Huilin Cheng | Zhi-Bo Hou | Xiaojun Luo | Yan Zhao
Source: Food Chemistry (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Machine learning intelligently selects feature values to construct a sensor array based on tri-functional Mn-doped covalent organic polymer nanozymes for identifying flavonoid compounds Random forest ; Feature (linguistics) ; Construct (python library) ; Flavonoid ; Identification (biology) ; Sensor array ; Covalent bond ; Pattern recognition (psychology) Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Francisco Javier López-Flores | Alejandra Pérez-Nava | Diana Katherine Cóndor-Arboleda | Bernardo A. Frontana-Uribe | J. Betzabe González‐Campos | José María Ponce‐Ortega
Source: Computers in Biology and Medicine (article, 2025-06-20)
Field: Materials Science — Polymers and Plastics

Analysis of an electrically responsive drug delivery system for ibuprofen on-demand release using a machine learning approach This study aims to optimize ibuprofen-based Drug Delivery Systems (DDSs) to address their short half-life and enhance controlled release. Advanced machine learning techniques, including Artificial Neural Networks, Random Forest, and CatBoost, were employed to model and predict the behavior of DDSs under various experimental conditions. Ibuprofen was loaded into a conducting polymer matrix based on PEDOT, and drug release was achieved through programmable electrical stimuli. Data preprocessing included the normalization of numerical variables and One-Hot Encoding for categorical variables. The predictive models, optimized via Optuna for hyperparameter tuning, demonstrated robust performance, achieving high R<sup>2</sup> values for the released concentration and kinetic parameters of release. SHAP (SHapley Additive exPlanations) analysis provided insights into the influence of key variables, such as diffusion time, stimulus type, and electrode type, that significantly impacted drug release behavior. The results demonstrated the potential of machine learning to optimize DDSs, improving predictive accuracy and enabling personalized and efficient approaches. This study contributes to the design of more advanced and sophisticated pharmaceutical formulations aligned with the principles of personalized medicine and on-demand delivery. Categorical variable ; Computer science ; Machine learning ; Artificial intelligence ; Hyperparameter ; Drug delivery ; Random forest ; Ibuprofen ; Artificial neural network ; Nanotechnology ; Materials science ; Pharmacology ; Medicine Conducting polymers and applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Miruna-Ioana Belciu | Alin Velea
Source: Molecules (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials design, we are able to systematically select, prepare, and optimize appropriate compositions for desired applications in a manner that was unachievable before. This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. Additionally, these algorithms served as inner models for an ensemble logistic regression estimator that achieved a superior R2 value of 0.8985. SHAP feature analysis of the second-best model, CatBoostRegressor (R2 = 0.8920), revealed the importance of elemental density, atomic weight, ground state atomic gap, and fraction of p valence electrons in tuning the value of the refractive index of a chalcogenide compound. Chalcogenide ; Chalcogenide glass ; Refractive index ; Amorphous solid ; Estimator ; Materials science ; Computer science ; Valence electron ; Artificial intelligence ; Valence (chemistry) ; Machine learning ; Electron ; Optoelectronics ; Chemistry ; Mathematics ; Physics ; Statistics Phase-change materials and chalcogenides ; Glass properties and applications ; Chalcogenide Semiconductor Thin Films