jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingyu Liu | Xueqiong Su | Lishan Yang | Jiansen Ding | Jin Wang | Xing Yi Ling | Yong Pan | Zhijun Wang | Wei Zhao | Yang Bu
Source: Inorganics (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Comparation of Graph Neural Networks and Traditional Machine Learning for Property Prediction in All-Inorganic Perovskite Materials Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain limited. Therefore, we evaluate 11 conventional ML models alongside the graph neural network-based Crystal Graph Convolutional Neural Network (CGCNN) for predicting three key properties—formation energy (Ef), band gap (Eg), and energy above hull (Eh)—across a dataset comprising single perovskites, double perovskites, and their combined structures. The results demonstrate that for single perovskites, CGCNN exhibits gains of over 20% in the root mean square error (RMSE) relative to the second-best model (Gradient Boosting Regression), achieving values of 0.205 eV/atom (Ef), 0.718 eV (Eg), and 0.167 eV/atom (Eh). Prediction accuracy for double perovskites is significantly enhanced by training CGCNN on a combined dataset, particularly for Eh, where the coefficient of determination (R2) improves approximately 68.1-fold compared to models trained exclusively on double-perovskite data. Feature importance analysis via one-shot, permutation-based, and recursive feature elimination (RFE) methods reveals that optimal model performance requires retention of at least the top 20 critical features. Furthermore, feature utilization patterns of CGCNN across different prediction tasks are visualized. This work provides actionable guidelines for model selection and feature engineering in perovskite property prediction, establishing a benchmark for future ML-driven materials discovery. Artificial neural network ; Boosting (machine learning) ; Graph ; Convolutional neural network ; Feature selection ; Predictive modelling ; Benchmark (surveying) ; Feature (linguistics) ; Feature engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Hua‐Qun Zhou | Dong-Ling Kuang | Jieying Hu | Lai-Hon Chung | Jun He
Source: Catalysts (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Rational Design and Advancement of Metal-Free Covalent Organic Frameworks for Photocatalytic Organic Transformation Covalent organic frameworks (COFs) stand at the forefront of reticular chemistry, weaving crystalline porosity with unparalleled designability and functional tunability. Their expansive channels and modular architectures have driven rapid advances in photocatalytic organic transformations, providing a platform for sunlight-driven reactions with high selectivity and sustainability. This review spotlights the rational engineering of metal-free COFs—from pore-size orchestration to chromophore integration—as versatile platforms for selective C–H activation, cross-coupling, and beyond. We survey landmark advances since 2016, dissecting the structure–activity relationships that drive efficiency under visible light, while unveiling challenges such as charge recombination and scalability issues. By establishing in-depth correlations between the structure of metal-free COFs and their photocatalytic performance, this work offers new opportunities to forge ahead in synthetic chemistry. Rational design ; Photocatalysis ; Expansive ; Modular design ; Chromophore ; Scalability Covalent Organic Framework Applications ; Radical Photochemical Reactions ; Advanced Photocatalysis Techniques
Authors: Molla Asmare Alemu | Addisu Alemayehu Assegie
Source: iScience (article, 2026-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advancements in biomass-origin carbon structures for next-generation supercapacitors The urgent global need for efficient, clean, and sustainable energy storage technologies has underscored the importance of supercapacitors as vital components in future energy systems. Their high power density, excellent cycle life, and rapid charge-discharge capabilities make them particularly attractive for diverse sustainable applications. Among emerging materials, biomass-derived carbon has gained significant attention due to its renewability, hierarchical porosity, and tunable surface chemistry, especially when enhanced through heteroatom modification. These distinctive features improve electrochemical performance, positioning biocarbons as promising candidates for next-generation supercapacitor electrodes. This review highlights recent advances (2020-2025) in biocarbon-based supercapacitors, focusing on how structural engineering, porous architectures, and chemical doping influence charge retention, energy density, and conductivity. Beyond performance metrics, it emphasizes the environmental and circular-economy benefits of recycling biowastes for sustainable energy solutions. The paper concludes with reflections on current challenges, practical considerations for scaling, and future research directions to advance biomass-derived carbon electrodes. Supercapacitor ; Carbon fibers ; Carbon nanotube ; Energy storage ; Solid-state chemistry Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Aerogels and thermal insulation
Authors: Zummurd Al Mahmoud | Babak Safaei | Mohammed Asmael | Jana Petrů | Saeid Sahmani
Source: Applied Materials Today (article, 2026-02-25)
Field: Materials Science — Polymers and Plastics

Inspecting the epoxy/Nd2O3 composite coating electrochemical behavior using electrochemical impedance spectroscopy analysis with implementing exploratory data analysis and physics-guided machine learning prediction Dielectric spectroscopy ; Support vector machine ; Coating ; Analytical Chemistry (journal) ; Electrochemistry ; Electrical impedance ; Dielectric ; Kriging ; Taguchi methods Conducting polymers and applications ; Corrosion Behavior and Inhibition ; High voltage insulation and dielectric phenomena
Authors: Igor Seibel-Geraschenko | Josef Slowik | Franziska Scheffler | Alexis Duval | N. M. Anoop Krishnan | Lothar Wondraczek
Source: ChemRxiv (article, 2026-04-05)
Field: Materials Science — Ceramics and Composites

High-Throughput Screening for Accelerated Oxide Glass Discovery Despite millennia of research and development, glass discovery remained largely reliant on manual, trial-and-error methodologies. A major impediment for accelerated glass discovery is its dependence on the processing conditions that is rarely documented, often complicating meaningful comparisons across independent studies. Here, a high-throughput screening platform is presented that allows for up to one hundred cm3-scale glass specimens to be produced per run through multicomponent stereolithographic printing, and be evaluated automatically via Raman spectroscopy without any sample preparation. The approach is demonstrated through its application to mixtures of representative yet chemically diverse glass systems, specifically, borosilicate, aluminosilicate and rare-earth doped materials. The proposed strategy provides a scalable, accessible, systematic and quantitative framework for accelerated glass exploration with relevance across both academic and industrial research environments. Conventional laboratory synthesis throughput is exceeded by two orders of magnitude, while the reproducibility and compositional control that accelerated discovery demands are preserved. Aluminosilicate ; Raman spectroscopy ; Reproducibility ; Silicate glass ; Throughput ; Sample (material) Glass properties and applications ; Cultural Heritage Materials Analysis ; Mineralogy and Gemology Studies
Authors: Avery F. Hill | Andrea Ruiz-Escudero | M. M. Montemore
Source: ACS Catalysis (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Few-Shot Ensemble Learning for Catalysis and Application to Trimetallics for Oxygen Reduction Machine-learned interatomic potentials (MLIPs) are increasingly used to accelerate catalyst discovery, but their accuracy and utility are often unclear, particularly when applying them to different computational setups or design spaces than those of the training data. This hinders their effective use in catalyst screening. Here, we improve accuracy and provide reliable uncertainty quantification through an ensemble-based, few-shot transfer learning strategy. The framework applies a bias-correction procedure to an ensemble of catalysis-focused MLIPs using a small number of density functional theory (DFT) labels from the target setup and design space. Applied to OH adsorption on bimetallic alloys, the approach reduces root mean squared errors (RMSEs) by 60% on average after incorporating just one additional DFT-calculated adsorption energy. For H adsorption on single-atom alloys, even the zero-shot ensemble is more accurate than any of the MLIPs, with further improvements when three to five DFT calculations are used for bias correction. The corrected ensemble yields well-calibrated uncertainty estimates, with miscalibration areas of just 0.039 and 0.088 for the two data sets. In a proof-of-concept screening campaign, the method identified a promising trimetallic candidate catalyst for the oxygen reduction reaction using only four total DFT-calculated adsorption energies. Taken together, these results demonstrate that few-shot bias correction enables reliable transfer of MLIP predictions across mismatched alloy search spaces and DFT methodologies, providing a practical route to accurate, uncertainty-aware catalyst screening with high efficiency. Bimetallic strip ; Catalysis ; Density functional theory ; Adsorption ; Reduction (mathematics) ; Ensemble learning ; Mean squared error ; Computational complexity theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Yili Shen | Xiangliang Zhang
Source: arXiv (Cornell University) (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Driving Reaction Trajectories via Latent Flow Matching Recent advances in reaction prediction have achieved near-saturated accuracy on standard benchmarks (e.g., USPTO), yet most state-of-the-art models formulate the task as a one-shot mapping from reactants to products, offering limited insight into the underlying reaction process. Procedural alternatives introduce stepwise generation but often rely on mechanism-specific supervision, discrete symbolic edits, and computationally expensive inference. In this work, we propose LatentRxnFlow, a new reaction prediction paradigm that models reactions as continuous latent trajectories anchored at the thermodynamic product state. Built on Conditional Flow Matching, our approach learns time-dependent latent dynamics directly from standard reactant-product pairs, without requiring mechanistic annotations or curated intermediate labels. While LatentRxnFlow achieves state-of-the-art performance on USPTO benchmarks, more importantly, the continuous formulation exposes the full generative trajectory, enabling trajectory-level diagnostics that are difficult to realize with discrete or one-shot models. We show that latent trajectory analysis allows us to localize and characterize failure modes and to mitigate certain errors via gated inference. Furthermore, geometric properties of the learned trajectories provide an intrinsic signal of epistemic uncertainty, helping prioritize reliably predictable reaction outcomes and flag ambiguous cases for additional validation. Overall, LatentRxnFlow combines strong predictive accuracy with improved transparency, diagnosability, and uncertainty awareness, moving reaction prediction toward more trustworthy deployment in high-throughput discovery workflows. Trajectory ; Task (project management) ; Matching (statistics) ; Trustworthiness ; Sequence (biology) ; Product (mathematics) ; Generative model ; Probabilistic logic Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Graph Neural Networks
Authors: Irina K. Goncharova | Dmitry N. Kholodkov | Roman Aleksandrovich Novikov | Gleb Kubrin | V. G. Lakhtin | Irina P. Beletskaya | Ashot V. Arzumanyan
Source: ChemRxiv (article, 2026-03-30)
Field: Chemistry — Organic Chemistry

Recyclable Pt-catalyzed biphase hydrosilylation on water: from functionalized monomers to polymer materials This study proposes ways to resolve the main challenges of the important industrial hydrosilylation process using biphase “on water” catalytic systems. The Pt/H2O-catalytic system consists of a commercially available Pt-precatalyst, K2PtCl4, and the most naturally abundant and environmentally benign solvent, H2O. It is easily prepared by mixing them without the use of ligands or an inert atmosphere, at r.t. for 2 min. The use of affordable KBr as an additive significantly increases the efficiency of the method for a range of substrates. Mechanistic studies revealed that during hydrosilylation, single-atom complexes (mainly in Pt/H2O) and cluster (mainly, in Pt/KBr/H2O) Pt0-complexes are generated, which are responsible for the high catalytic activity of these systems. Pt/H2O- and Pt/KBr/H2O-catalytic systems are highly efficient in the hydrosilylation of a wide range of hydrosilanes, including oligomeric and polymeric ones, as well as aliphatic and aromatic alkenes and alkynes containing N-, O-, S-, Si-, and Hal-containing functional groups. The method allows for the synthesis of products in high yields with anti-Markovnikov selectivity, in air at r.t. Using the Pt/KBr/H₂O-catalytic system, cured silicone materials were obtained both as monoliths and thin films, from PDMS derivatives with Si–H-groups with linear and cyclic vinylsiloxanes, various dienes, and, more importantly, by gaseous acetylene and butadiene. Pt-free (monomer, polymer, and cured) products can be isolated from the biphasic catalytic system by simple decantation. The possibility of multiple catalyst recycling (demonstrated over 15 cycles), combined with low Pt-loadings (0.01–0.1 mol%) and high product yields (up to 99%), allows for achieving low E-factor (≤0.07) and high TON (>104). We also constructed a tube-in-tube flow reactor, which enables reaction scale-up with greater efficiency than in batch and the automatic separation of products and the catalytic system. Hydrosilylation ; Catalysis ; Monomer ; Silicone ; Polymer ; Inert ; Acetylene Organoboron and organosilicon chemistry ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Beibei Wang | Juan Wang | Liping Li | Dengwu Wang
Source: Frontiers in Materials (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Data-driven AI approaches for screening high-efficiency, stable, and lead-free perovskite photovoltaic materials: a review With the global increase in energy demand and environmental awareness, it has become crucial to develop new types of energy materials that are efficient, stable and environmentally friendly. Lead-free perovskite materials have garnered attention due to their unique crystal structure (ABX 3 ) and photoelectric properties, particularly demonstrating great potential for applications such as photovoltaics, photodetectors, catalysis, and display lighting. However, the lead toxicity of traditional lead-containing perovskite materials limits their large-scale commercialization. Therefore, the research on stable and non-toxic lead-free perovskite materials has become a current hot topic in scientific research. In recent years, artificial intelligence technology has brought about a transformation in the study of perovskite materials. This review focuses on the application of AI in lead-free perovskite research, including data collection, preprocessing, feature extraction, model training and prediction, reverse design and experimental verification. This paper aims to leverage AI technologies to drive data-informed and inverse-designed discovery processes, thereby improving the efficiency and success rate of lead-free perovskite materials screening, development, and performance optimization. Perovskite (structure) ; Leverage (statistics) ; Photovoltaic system ; Energy transformation ; Efficient energy use ; Emerging technologies Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Xinyu Wang | Fei Dou | Jinbo Bi | Minghu Song
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

SIGMA: Structure-Invariant Generative Molecular Alignment for Chemical Language Models via Autoregressive Contrastive Learning Linearized string representations serve as the foundation of scalable autoregressive molecular generation; however, they introduce a fundamental modality mismatch where a single molecular graph maps to multiple distinct sequences. This ambiguity leads to \textit{trajectory divergence}, where the latent representations of structurally equivalent partial graphs drift apart due to differences in linearization history. To resolve this without abandoning the efficient string formulation, we propose Structure-Invariant Generative Molecular Alignment (SIGMA). Rather than altering the linear representation, SIGMA enables the model to strictly recognize geometric symmetries via a token-level contrastive objective, which explicitly aligns the latent states of prefixes that share identical suffixes. Furthermore, we introduce Isomorphic Beam Search (IsoBeam) to eliminate isomorphic redundancy during inference by dynamically pruning equivalent paths. Empirical evaluations on standard benchmarks demonstrate that SIGMA bridges the gap between sequence scalability and graph fidelity, yielding superior sample efficiency and structural diversity in multi-parameter optimization compared to strong baselines. Generative model ; String (physics) ; Generative grammar ; Inference ; Scalability ; Graph ; Redundancy (engineering) ; Autoregressive model ; Boosting (machine learning) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Adetayo Abdulmumin Adebisi | Abdulsamad Teniola Muyideen | Umar Shehu | Kamilu Adeyemi Bello
Source: Research Square (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Predictive Simulation of Mechanical Properties and Iron-Carbon Phase Diagram Region for Steel Using Materials Informatics Phase diagram ; Carbide ; Ultimate tensile strength ; Alloy ; Phase (matter) ; Artificial neural network ; Materials informatics ; Stability (learning theory) ; Visualization Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Cultural Artifacts
Authors: Mashael M Alshaikh | Wanisa Abdussalam-Mohammed | Ajaya Bhattarai
Source: Nanotechnology Reviews (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Review of thermal conductivity of gold nanoparticle composites Abstract Nanocomposites are defined as a combination of two or more components, wherein at least one of these components is on the nanoscale. Applications requiring increased thermal conductivity are demonstrating interest in nanocomposites based on conductive nanoparticles, such as gold nanoparticles (AuNPs), that are currently the subject of intense investigation due to their amazing thermal, electronic, and optical properties. Because of their remarkable stability, high conductivity, and high ability to create strong chemical interactions with groups, including organic ligands or polymers. In addition, adjustable production, simple surface modification, nontoxicity, low resistance, and strong light interaction. All these numerous advantages made AuNPs ideally suited for a variety of application areas, including environmental science, biomedicine, sensors, and nano-electronics. Various factors affect the properties of AuNPs, including particle size and shape, concentration, temperature, surface functionalization, covalent functionalization, and base fluid material are responsible for the variation of thermal conductivity. There is a main gap in a broad analysis, offering a cohesive view on the preparation of thesis materials, their properties, their characteristics, the highest challenges, and future perspectives. This review aims to fill this gap and summarizes the recent developments in the thermal properties of AuNPs of nanocomposites and the mechanisms by which AuNPs enhance thermal conductivity and the heat transfer properties of various composite matrices, such as graphene and polymers. In addition, it discusses how interfacial thermal resistance is reduced and highlights how the size, shape, concentration, and distribution of the nanoparticles affect the overall thermal conductivity of the composites. Comparative evaluation of Au nanocomposite with other NPs in enhancing conductivity and challenges and future directions will be carried out as well. Thermal conductivity ; Nanocomposite ; Nanoparticle ; Graphene ; Composite number ; Particle (ecology) ; Thermal resistance ; Colloidal gold ; Thermal Thermal properties of materials ; Chemical and Physical Properties of Materials ; Aerogels and thermal insulation
Authors: N. V. Kiryanov | K. D. Martinson
Source: Nanosystems Physics Chemistry Mathematics (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Effect of fuel-to-oxidizer ratio on the structural and magnetic properties of Zn0.5Mn0.5Fe2O4 nanoferrites synthesized via glycine-nitrate combustion Nanostructured Zn 0.5 Mn 0.5 Fe 2 O 4 ferrites were synthesized by the glycine–nitrate solution combustion method with the fuel-to-oxidizer ratio f varied from 0.4 to 1.6 in order to clarify the influence of redox conditions on structure and magnetic properties. X-ray diffraction confirms the formation of single-phase cubic spinel for all compositions, with the crystallite size changing from ∼ 8 to 108 nm and the minimum values of both crystallite size and lattice parameter (8.420 Å) obtained under fuel-deficient conditions ( f = 0.4); the lattice microstrain does not exceed 0.5 %. SEM observations reveal 3 – 5 µ m agglomerates composed of 30 – 190 nm particles, while EDX analysis shows cation ratios close to the nominal composition. Magnetic measurements at 300 K demonstrate typical soft-magnetic behavior with saturation magnetization ranging from 16.1 to 68.3 emu/g, residual magnetization from 1.8 to 20.3 emu/g and coercive force from 34.7 to 85.6 Oe, all efficiently tuned by the fuel content. The highest saturation magnetization is achieved near the stoichiometric regime ( f ≈ 0.8 – 1.0), whereas fuel-rich mixtures result in increased coercivity due to microstructural refinement and lattice strain. The established correlations between combustion conditions, structural parameters and magnetic response show that controlled variation of the fuel ratio is an effective tool for tailoring Zn–Mn ferrite nanopowders for low-loss soft-magnetic applications. Coercivity ; Crystallite ; Spinel ; Lattice constant ; Magnetization ; Ferrite (magnet) ; Diffraction ; Stoichiometry Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Clay minerals and soil interactions