jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengwei Yang | Linhan Wu | Bing He | Maxim Avdeev | Siqi Shi | Yue Liu
Source: npj Computational Materials (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Structure divide-and-conquer: dual graph representation for accurate ionic transport barrier prediction of inorganic compounds Despite the effectiveness of most graph-based representation methods in capturing the crystal geometry characteristics, they fall short in intuitively describing phenomena such as ionic transport behavior which are often determined by the atom-unoccupied regions in the mobile sublattice (namely interstitial network). Here, we develop a Structure Divide-and-Conquer Graph Representation method based on graph neural network (SDCGNNdk), for unveiling structure-activity relationships of transport barriers by incorporating Domain Knowledge (e.g., site energy information, thresholds for ion accessibility, etc.), where crystal geometry and interstitial network topology are combined to construct a dual-structure crystal graph. For driving the proposed model, we construct a graph-based dataset for the prediction of activation energy ($${E}_{a}$$), i.e., the energy barrier hindering ionic transport, covering over 18,000 ionic compounds from the Inorganic Crystal Structure Database (ICSD), including Li+, Na+, K+, Ag+, Cu(2,3)+, Mg2+, Zn2+, Ca2+, Al3+, F−, and O2−. SDCGNNdk achieves high prediction performance of $${E}_{{\rm{a}}}$$ with R2 of 91.30%, outperforming conventional GNNs by more than 20% on average and offering insights into structure-activity relationships by quantifying the contributions of crystal geometry and interstitial network characteristics to transport barriers. This work provides an accurate graph representation and GNN framework, demonstrating potential for extension to predicting other properties relevant to interstitial network of inorganic compounds. Ionic bonding ; Representation (politics) ; Graph ; Crystal structure ; Crystal (programming language) ; Topology (electrical circuits) ; Ion ; Construct (python library) ; Energy minimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Hao Li | Qiao Liu | Zhiguo Han | Tao Lu | Zeyu Li
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-01-24)
Field: Chemistry — Analytical Chemistry

MoistureVision: Rapid non-destructive prediction of cotton seed moisture using hyperspectral imaging and machine learning Rapid, non-destructive and accurate prediction of cotton seed moisture content is important for assessing seed vigour and improving storage capacity. In this study, a prediction approach for cotton seed moisture content was developed based on machine learning (ML) and hyperspectral imaging. Using the cultivar Jinken 1161 as the experimental material, spectral data in the range of 935-1720 nm were acquired. Outliers were removed using the Isolation Forest algorithm, and the samples were divided into calibration and prediction sets using the spectral-physicochemical value coordinate algorithm. The raw spectra were pre-processed using four methods, including Savitzky-Golay (SG) smoothing and standard normal variate transformation, before constructing traditional ML models [partial least square regression and multiple linear regression (MLR)] and deep learning (DL) models [convolutional neural network and long short-term memory network]. To reduce data redundancy and improve computational efficiency, feature wavelengths related to moisture content were selected using the successive projection algorithm and the least absolute shrinkage and selection operator (LASSO). Comparative analysis of different algorithmic combinations identified the optimal model, which was subsequently applied to hyperspectral images for pixel-wise prediction. This application enabled visualisation of the spatial distribution of moisture within individual cotton seeds. The results showed that, given the current sample size, ML models outperformed DL models. The SG-LASSO-MLR model achieved the best performance, with a prediction correlation coefficient (R2 p), root mean square error of prediction and residual predictive deviation of 0.9557, 0.908 and 4.77, respectively. These findings provide a feasible and effective technical solution for rapid and non-destructive detection of cotton seed moisture content. These outcomes offer valuable insights for seed quality evaluation and intelligent crop monitoring. Hyperspectral imaging ; Mean squared error ; Smoothing ; Artificial neural network ; Outlier ; Linear regression ; Water content ; Residual ; Partial least squares regression ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Hui Zhan | Jie Liu | Sen-Hua Zhan | Bo Wu | Tongfei Shi
Source: Chinese Journal of Polymer Science (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Physics-informed Neural Network-based Prediction of Multi-factor Coupled Thermal-oxidative Aging Behavior in Polyamide66-Glass Fiber Composites Transferability ; Nonlinear system ; Polyamide ; Artificial neural network ; Fiber ; Durability ; Accelerated aging Machine Learning in Materials Science ; Thermal properties of materials ; Polymer crystallization and properties
Authors: A Götz | Daniel Marchand | Leander Michels | Jaakko Akola
Source: Communications Materials (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Molecular dynamics study of graphite 2-D nucleation from paramagnetic Fe-C melt Abstract The nucleation and growth of graphite in Fe-C alloys (cast iron) can both be related to the 2-D nucleation of a single atomic layer of graphene. Here, we use molecular dynamics (MD) simulations to study the nucleation of graphite from molten iron. A neural-network potential is developed for the Fe-C system using a pragmatic approach to treat the paramagnetic nature of liquid iron, and its thermodynamic behavior is validated against data from thermochemical software. The critical nucleus size and free-energy barrier for nucleation are computed using MD simulations in combination with enhanced sampling methods, and the results are compared with continuum models based on adaptations of classical nucleation theory. 2-D nucleation is found to be a feasible mechanism for graphite nucleation and growth at a carbon supersaturation of 5 at.% or higher. At low supersaturation the nucleation rate declines, and other mechanisms such as interaction with substrates other than graphite, solute elements, or defects are necessary to facilitate the formation of new layers. Nucleation ; Supersaturation ; Graphite ; Molecular dynamics ; Classical nucleation theory ; Paramagnetism Graphite, nuclear technology, radiation studies ; Microstructure and Mechanical Properties of Steels ; Fiber-reinforced polymer composites
Authors: Rachel Rubinstein | Neta Shvil | Yossi Yovel | Amir Ayali | Ben M. Maoz
Source: Analytical Chemistry (article, 2026-02-19)
Field: Chemistry — Organic Chemistry

Detection of Solid-Phase Explosives Using an Electroantennogram-Based Biohybrid Sensor with Active Sniffing Effective detection of hazardous compounds such as explosives is a critical objective in the fields of security and environmental monitoring. However, these materials, especially in their solid phase, present considerable analytical challenges due to their low vapor pressure and limited volatility under ambient conditions. In this study, we present a biohybrid sensing system that integrates an electroantennogram (EAG) recording from the antenna of the desert locust (<i>Schistocerca gregaria</i>), a bioinspired active sniffing mechanism, and machine learning assisted classification. This system enables noncontact detection of low-volatility compounds, without the need for heating, solvent extraction, or chemical preprocessing. It can reliably detect explosives such as trinitrotoluene (TNT), hexogen (RDX), and gunpowder and can discriminate TNT and RDX from nonexplosive solid odorants. A detection threshold of 2.67 pg was achieved for solid-phase TNT, which matches or even goes below previously reported detection approaches. These findings highlight the potential of insect-based biohybrid sensors as practical, low-cost tools for real-world chemical sensing, with promising applications in hazardous material monitoring. Explosive material ; Sniffing ; Trinitrotoluene ; Pentaerythritol tetranitrate ; Hazardous waste ; Chemical sensor ; Chemical Warfare Agents Polydiacetylene-based materials and applications ; Advanced Sensor and Energy Harvesting Materials ; Mechanical and Optical Resonators
Authors: Wenlong Xi | Zixuan Wu | Patrick H.‐L. Sit
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Adsorption energy prediction and mechanistic analysis of metal atoms on MXene surfaces based on DFT and machine learning MXenes ; Interpretability ; Adsorption ; Density functional theory ; Support vector machine ; Artificial neural network ; Hybrid functional ; Random forest ; Test set MXene and MAX Phase Materials ; Advancements in Battery Materials ; Boron and Carbon Nanomaterials Research
Authors: Taiqiao Liu | Tongtong Wang | Zeyuan Li | E Zhou | Junlei Zhao | Jiaren Feng | Xiaoyu Fei | Yu Guo | Flyura Djurabekova | Sheng Liu | Zhaofu Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anisotropic Kinetics of Ion-Irradiation-Induced Phase Transition in Gallium Oxide Radiation-tolerant semiconductors have traditionally been engineered by the principle of suppressing defect accumulation and amorphization, based on the assumption that radiation damage is inherently stochastic. Here we show that, in monoclinic $β$-\ce{Ga2O3}, a promising ultrawide-bandgap semiconductor, surface crystallographic orientation deterministically governs radiation tolerance through highly anisotropic kinetics of the $β$-to-$γ$ phase transition. Using machine-learning molecular dynamics coupled with a local configurational-entropy descriptor, we quantitatively map anisotropic $β$-to-$γ$ transition kinetics, showing that the critical dose, transition-layer depth, and kinetic stability of the $γ$-phase are fundamentally governed by surface orientation. Under ion irradiation, non-channeling surfaces such as (100), (001), and (-201) undergo severe surface amorphization, whereas the strongly channeling (010) surface resists damage accumulation and promotes subsurface $γ$-phase nucleation. During thermal annealing recovery process, these initial states follow two distinct recovery pathways: the channeling (010) surface reverts directly from $γ$-to-$β$, whereas non-channeling surfaces follow a sequential amorphous-to-$γ$-to-$β$ transition pathway. This work establishes surface orientation as a fundamental design principle for achieving radiation tolerance through controlled polymorphic transitions, providing a universal framework for engineering functional materials capable of withstanding extreme irradiation environments. Anisotropy ; Monoclinic crystal system ; Phase transition ; Irradiation ; Annealing (glass) ; Kinetics ; Radiation damage ; Oxide ; Surface engineering ; Work (physics) Ga2O3 and related materials ; Machine Learning in Materials Science ; Ion-surface interactions and analysis
Authors: Luneng Zhao | Hongsheng Liu | Yuan Chang | Xiaoran Shi | Jijun Zhao | Feng Ding | Junfeng Gao
Source: Nature Communications (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Intermediates of forming transition metal dichalcogenide heterostructures revealed by machine learning simulations Two-dimensional (2D) transition metal dichalcogenide (TMD) van der Waals heterostructures (vdWHs) hold promise for high-performance electronics, but their large-scale synthesis remains limited by size constraints and alloying contaminations. Recently, a two-step vapor deposition method was reported for growing wafer-size TMD vdWHs with minimal impurities. In this study, we develop a machine learning potential (MLP) that captures the atomic-scale dynamic growth process of bilayer MoS<sub>2</sub>/WS<sub>2</sub> vdWHs under feasible growth conditions. Our simulations uncover a crucial metastable SMMS (M = Mo or W) intermediate structure that facilitates metal atom swap and alloying. Eliminating the alloying contamination requires preventing the embedding of bare metal atoms. The results also show that the SMMS structure exhibits favorable electronic properties and emerges as a low Schottky barrier contact electrode for MoS<sub>2</sub> field-effect transistors (FETs). Heterojunction ; Transition metal ; Metastability ; Schottky barrier ; Chemical vapor deposition ; Bilayer ; Atom (system on chip) ; van der Waals force 2D Materials and Applications ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: Yuming Gao | Juncheng Hong | Pengxuan Wang | Chenyi Guo | Haoyu Yan | Zhe Wang | Jiaming Ma | Qian Zhang | Dawei Tang | Bo Jiang
Source: Advanced Energy Materials (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Interpretable Descriptor Enabling Prediction of Propylene Yield for Propane Dehydrogenation Catalysts ABSTRACT Given that the present yield of propylene (C 3 H 6 ) is relatively low, developing an efficient direct dehydrogenation of propane (PDH) catalyst that exhibits a high yield of C 3 H 6 is therefore immensely beneficial for the chemical industry. By extracting physical meaning, machine learning, combined with an interpretable descriptor, is an effective method for speeding up catalyst design. Existing descriptors, however, are only capable of predicting selectivity, rendering the direct prediction of the yield of C 3 H 6 challenging. Furthermore, the current descriptors primarily screen transition metal elements, thereby neglecting other potentially high‐performance metals beyond the transition metal zone. Hence, constructing a universal descriptor that is capable of screening all metallic elements and predicting the yield of C 3 H 6 is highly desirable but challenging. Herein, a universal descriptor that incorporates frontier orbital occupancy to describe the electronic contributions of metals from different blocks is proposed, thereby enabling the comprehensive screening of all metallic elements, concurrently predicting C 3 H 6 yield. The screening process predicted that potential catalysts, IrGa@NC, composed of both post‐transition and transition metals, achieved the C 3 H 6 yield of 51.1%, outperforming state‐of‐the‐art catalysts. This work provides insights into the complicated PDH reaction and the development of catalysts with full‐period metallic elements. Dehydrogenation ; Catalysis ; Yield (engineering) ; Propane ; Transition metal ; Metal Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Abdullah Khamis Al Saidi | Mazhar Ul-Islam | Md. Wasi Ahmad | Refat Al-Shannaq | Adeeb Shehzad
Source: Results in Chemistry (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Carbon dots for fluorescence bioimaging: Advances in synthesis, optical properties, and multimodal applications Carbon dots (CDs) are an emerging class of carbon nanomaterials distinguished by exceptional optical properties, biocompatibility, and tunable surface chemistry, making them highly attractive for next-generation biomedical imaging. Unlike conventional semiconductor quantum dots, CDs are nontoxic, free of heavy metals, resistant to photobleaching, and readily synthesized via green, scalable routes. Advances in size control, surface modification, and heteroatom doping have enabled near-infrared emission, essential for deep-tissue imaging with minimal autofluorescence. This review summarizes recent progress in top-down and bottom-up synthetic strategies, emphasizing the mechanism property relationships that link structural design to photophysical performance. Surface passivation, oxidation, and doping are shown to govern fluorescence lifetime, quantum yield, and spectral tunability. Cutting-edge applications are highlighted, including high-resolution cellular imaging, targeted diagnostics, and theranostics, with integration into multimodal platforms such as MRI, CT, and photoacoustic imaging. Critical challenges such as reproducibility, spectral heterogeneity, and translational biosafety are analyzed alongside emerging solutions, including machine-learning-guided synthesis and ligand-mediated targeting. Collectively, CDs are positioned not merely as safer fluorophore alternatives but as programmable nanoplatforms unifying safety, sensitivity, and multifunctionality, poised to redefine diagnostic and therapeutic strategies across oncology, neurology, and regenerative medicine. Quantum dot ; Biological imaging ; Fluorescence ; Nanomaterials ; Carbon fibers ; Fluorophore ; Carbon quantum dots Carbon and Quantum Dots Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Lin Huang | Xinyu Zhang | Shuxing Li | Rongjun Xie
Source: npj Computational Materials (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Text mining-assisted machine learning prediction and experimental validation of emission wavelengths The data-driven discovery of luminescent materials is hindered by a critical paradox: while decades of research have generated extensive data, the unstructured textual format of such data in literature precludes its systematic reuse. While text mining enables conversion to structured datasets, general natural language processing (NLP) tools fail at domain-specific challenges, such as fragmented host-dopant syntax and incomplete representations of property relationships. To address these limitations, we developed a novel NLP pipeline specifically tailored to the phosphor domain, integrating three key algorithms: chemically-aware parsing, frequency-driven subject recovery, and context-aware disambiguation algorithms. Using this approach, we successfully extracted 6,400 material-emission relationships from 16,659 scientific papers, with F1 scores of 0.956 for phosphor formula extraction and 0.851 for relation extraction. Additionally, a machine learning model trained on the extracted dataset predicted Eu2+ emission wavelengths, with a coefficient of determination (R2) of 0.91. Guided by predictions, we synthesized novel phosphors (e.g., CaGd2S4: Eu2+), the experimental emission spectra of these materials deviated by around 10 nm from predictions, verifying the robustness of the “data extraction-model prediction-experimental validation” workflow. This study bridges computational discovery and experimental validation, offering an open framework and tools to accelerate luminescent materials innovation. Robustness (evolution) ; Pipeline (software) ; Syntax ; Noisy data ; Experimental data ; Information extraction ; Natural language Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; History and advancements in chemistry
Authors: Houssam Metni | Laura Ruple | Lauren N Walters | Luca Torresi | Jonas Teufel | Henrik Schopmans | Jona Östreicher | Yumeng Zhang | Marlen Neubert | Y. Koide | Kevin Steiner | Paul Link | Lukas Bär | Mariana Petrova | Gerbrand Ceder | Pascal Friederich
Source: PubMed (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Generative Models for Crystalline Materials. Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery. Generative grammar ; Generative model ; Work (physics) ; Software ; Crystal (programming language) ; Scientific modelling Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Houssam Metni | Laura Ruple | Lauren N. Walters | Luca Torresi | Jonas Teufel | Henrik Schopmans | Jona Östreicher | Yumeng Zhang | Marlen Neubert | Yuri Koide | Kevin Steiner | Paul Link | Lukas Bär | M. Petrova | Gerbrand Ceder | Pascal Friederich
Source: Advanced Materials (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Generative Models for Crystalline Materials Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery. Generative grammar ; Generative model ; Work (physics) ; Crystal (programming language) ; Software ; Scientific modelling Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: P G Kubendran Amos
Source: Research Square (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

A Leakage-Controlled Uncertainty-Gated Interpretable and Accessible Lightweight Screening Pipeline for Cryogenic Alloy Discovery Pipeline (software) ; Workflow ; Ranking (information retrieval) ; Feature (linguistics) ; Interface (matter) ; Random forest ; Pipeline transport Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Aluminum Alloy Microstructure Properties