jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenyu Huang | Yunjiang Zhang | Shuyuan Li | Huimin Wang | Yaxin Wang | Shihao Wei | Shaorui Sun
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An explainable “family bucket” model for simultaneous prediction of K-edge XANES for multiple light transition metals X-ray near-edge structure (XANES) is a crucial bridge between the local structures and chemical properties of materials. Although there have been a number of studies devoted to the development of predictive K-edge XANES spectral models, existing methods are usually still limited to separate modeling for a specific absorbing element. Currently, there is a lack of a K-edge XANES spectra prediction model that can be broadly applied to a wide range of elements, which would enable data dispersed in terms of absorbing elements to be integrated and well utilized. In this work, we develop an innovative "family bucket" model based on a multi-head graph attention convolutional neural network by combining a multi-element mixed dataset with a crystal topology approach for the localized environment. The model is able to predict the K-edge XANES spectra for a wide range of light transition metals (periods 3 and 4) simultaneously. Moreover, it is demonstrated that the training scheme not only improves the accuracy of the model but also the efficiency of its training. In terms of interpretability, several fascinating insights were gained, uncovering the underlying mechanisms of the model for spectral prediction. We investigate the collective behavior of neurons by employing a range of responses to different samples as descriptive features. Notably, the analysis revealed that neurons in the neural network exhibit functional differentiation characteristics analogous to Brodmann areas in the cerebral cortex. The homology of data analysis indicates that the mutual learning of samples from different absorbing elements is occurring between close elements of the same period. Additionally, the attention scores of the samples are determined by both the absorbing element and its surrounding atomic environment. In conclusion, this research advances the understanding of the relationship between XANES spectra and material structures while providing valuable insights into neural networks, enhancing the comprehension of neuronal behavior. XANES ; Enhanced Data Rates for GSM Evolution ; Transition (genetics) ; Transition metal ; Chemistry ; Computer science ; Physics ; Artificial intelligence ; Organic chemistry ; Spectroscopy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Xianrui Zhong | Bowen Jin | Siru Ouyang | Yanzhen Shen | Qiao Jin | Yin Fang | Zhiyong Lu | Jiawei Han
Source: arXiv (Cornell University) (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Benchmarking Retrieval-Augmented Generation for Chemistry Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high-quality, domain-specific corpora and well-curated evaluation benchmarks. In this work, we introduce ChemRAG-Bench, a comprehensive benchmark designed to systematically assess the effectiveness of RAG across a diverse set of chemistry-related tasks. The accompanying chemistry corpus integrates heterogeneous knowledge sources, including scientific literature, the PubChem database, PubMed abstracts, textbooks, and Wikipedia entries. In addition, we present ChemRAG-Toolkit, a modular and extensible RAG toolkit that supports five retrieval algorithms and eight LLMs. Using ChemRAG-Toolkit, we demonstrate that RAG yields a substantial performance gain -- achieving an average relative improvement of 17.4% over direct inference methods. We further conduct in-depth analyses on retriever architectures, corpus selection, and the number of retrieved passages, culminating in practical recommendations to guide future research and deployment of RAG systems in the chemistry domain. The code and data is available at https://chemrag.github.io. PubChem ; Benchmarking ; Modular design ; Software deployment ; Inference ; Benchmark (surveying) ; Set (abstract data type) Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Topic Modeling
Authors: Matt Ball | Dragos Horvath | Thierry Kojey | Mikhail Kabeshov | Alexandre Varnek
Source: ChemRxiv (preprint, 2025-07-02)
Field: Materials Science — Materials Chemistry

Predicting Reaction Conditions: A Data-DrivenPerspective The selection of optimal reaction conditions is a critical challenge in synthetic chemistry, influencing the efficiency, sustainability, and scalability of chemical processes. While machine learning (ML) has emerged as a promising tool for predicting reaction conditions in computer-aided synthesis planning (CASP), existing approaches face many significant challenges, including data quality, sparsity, choice of reaction representation and method evaluation. Recent studies have suggested that these models may fail to surpass literature-derived popularity baselines, underscoring these problems. In this work, we provide a critical review of state-of-the-art ML techniques, identifying innovations which have addressed the key challenges facing researchers when modelling conditions. To illustrate how relevant reaction representations can improve existing models, we perform a case study of heteroaromatic Suzuki-Miyaura reactions, derived from US patent data (USPTO). Using Condensed Graph of Reaction-based inputs, we demonstrate how this alternative representation can enhance the predictive power of a model beyond popularity baselines. Finally, we propose future directions for the field beyond improving data quality, suggesting potential options to mitigate data issues prevalent in existing literature data. This perspective aims to guide researchers in understanding and overcoming current limitations in computational reaction condition prediction Perspective (graphical) ; Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Yerbolat Makhambetov | Azamat Burumbayev | Bauyrzhan Kelamanov | Sultan Kabylkanov | Onuralp Yücel | Armat Zhakan | Zhadiger Sadyk | S. K. Mukanov
Source: Acta Metallurgica Slovaca (article, 2025-12-08)
Field: Materials Science — Ceramics and Composites

Synthesis of silicon carbide from secondary silicon-containing materials This study presents an experimental approach to the synthesis of silicon carbide (SiC) using technogenic and biogenic wastes—microsilica and rice husk. The synthesis was carried out by carbothermal reduction in a self-designed and constructed laboratory resistance furnace. The starting materials were subjected to preliminary mechanical activation in a planetary centrifugal mill, which enhanced their reactivity. The morphology and elemental composition of the samples were examined using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The results demonstrated the formation of characteristic SiC crystals and confirmed their composition, close to stoichiometric. X-ray diffraction analysis revealed the presence of β-SiC (3C) and hexagonal polytypes (4H, 6H-SiC), as well as secondary phases of silicon dioxide (cristobalite, tridymite), reflecting the specific features of the reaction at high temperatures. It was established that mechanical activation promotes more intensive SiC formation and reduces the amount of residual oxide phases. The obtained results confirm the potential of utilising secondary silicon-containing resources for SiC synthesis, which is significant for the development of environmentally oriented technologies and for expanding the raw material base for high-value-added materials. Silicon carbide ; Carbothermic reaction ; Scanning electron microscope ; Raw material ; Silicon ; Carbide ; Hexagonal crystal system ; Oxide ; Diffraction Advanced ceramic materials synthesis ; Silicon Carbide Semiconductor Technologies ; Silicon Effects in Agriculture
Authors: Thomas Wingens | Michael V. Glazoff
Source: Proceedings of the ... ASM Heat Treating Society Conference (article, 2025-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advanced REE Magnets Require New Thermal Process Technologies Abstract Developing next-generation rare earth element (REE) magnets—particularly NdFeB-based systems—relies increasingly on innovative thermal process technologies to improve efficiency, sustainability, and material performance. Among these, hydrogen decrepitation (HD) has emerged as a critical enabler for both primary production and recycling of REE magnets. This paper examines the pivotal role of HD in transforming coarse-grained cast alloys into fine, friable powders with controlled particle morphology and minimal oxidation, providing an ideal precursor for subsequent sintering and grain boundary diffusion treatments. Compared to mechanical pulverization, the HD method uses less energy and takes less processing time. It also makes selected separation paths possible in magnet recycling, which supports circular economy initiatives. To maximize decrepitation kinetics and magnetic characteristics, we examine important variables such as alloy composition, temperature profiles, and hydrogen pressure. Furthermore, the integration of HD with advanced thermal cycles—including low-oxygen sintering and targeted annealing—is shown to enhance coercivity, thermal stability, and microstructural uniformity significantly. This paper concludes that a redefinition of thermal processing around hydrogen-based techniques is essential for scaling the production and sustainability of advanced REE magnets for electric mobility, wind energy, and high-performance applications. Experimental work of high quality is expensive, much more so than multiscale computational modeling, physics- and data-informed. This shift of paradigm toward using much more computational work, constructing digital twins, and modern AI/ML technologies will dramatically accelerate the process of improving the fundamental properties of REE magnets. They will result in the rapid discovery of new materials and industrial processes of their production, including sophisticated novel heat treatments. Sintering ; Magnet ; Thermal ; Microstructure ; Process (computing) ; Alloy ; Grain boundary ; Work (physics) ; Process integration Magnetic Properties of Alloys ; Superconducting Materials and Applications
Authors: Liangliang Wang | Ming Gao | Zhijiang Yang | Tengxin Huang | Mingtian Li | Ying–Jun Angela Zhang | Junjie Ding
Source: Research Square (preprint, 2025-11-04)
Field: Materials Science — Materials Chemistry

QGeoSEP: A Novel Multitask Learning Framework Integrating Semantic Features for Collaborative Prediction of Multiple Properties of EMs Property (philosophy) ; Set (abstract data type) ; Multi-task learning ; Decomposition ; Limit (mathematics) ; Test set ; Training set ; Ensemble learning Machine Learning in Materials Science ; Energetic Materials and Combustion ; Crystallography and molecular interactions
Authors: Shi Jian | Sun Xinwen | Zhang Jiancheng | Liu Xinhua | Zhang Haobo | Chang Peiyuan | Song Zilin | Jiang Zhichao | Dong Dexin
Source: Journal of Applied Polymer Science (article, 2025-12-27)
Field: Materials Science — Polymers and Plastics

Magnetically Targeted <scp>UV</scp> ‐Shielding Microcapsules for Self‐Healing of Electrical Treeing in Dielectric Polymers ABSTRACT Electrical treeing, an irreversible degradation process in dielectric polymers under high electrical stress, critically threatens the reliability of power equipment by propagating undetectable damage. To address this challenge, magnetically responsive microcapsules were developed for targeted electrical tree repair. These microcapsules are magnetically positioned within dielectric encapsulants and engineered with a higher dielectric constant than the host matrix to guide electrical trees toward microcapsule rupture. The core‐shell structure integrates: (i) a hybrid healing agent (photosensitive hyperbranched polyurethane acrylate/epoxy resin) and (ii) functional shells incorporating Fe 3 O 4 @SiO 2 nanoparticles for magnetic navigation and titanium dioxide/graphitic carbon nitride (TiO 2 /g‐C 3 N 4 ) for UV‐shielding. During electrical tree propagation, electroluminescence triggers polymerization of the released healing agent, enabling autonomous crack‐sealing. Optimized microcapsules achieve 75% encapsulation efficiency with uniform size distribution (~100 μm). After optimization, a doping concentration of 4 wt% was identified as optimal, at which the composite exhibited a peak tensile strength of 37.54 MPa (compared to 36.21 MPa for the neat resin) and a minimal reduction in breakdown voltage to 36.4 kV (from 37.2 kV). In situ optical microscopy confirms effective damage repair during tree growth, demonstrating a precision self‐healing strategy that extends service life without compromising intrinsic material properties. Electrical treeing ; Dielectric ; Polymer ; Electroluminescence ; Electrical breakdown ; Poling ; Nanoparticle ; Polymerization ; Epoxy Polymer composites and self-healing ; High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: Chenlong Zhao | Y. Wang | Shuo Cheng | Yuhang You | Yi Li | X. W. Xiu
Source: Materials (article, 2025-12-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergistically Enhanced Ta2O5/AgNPs SERS Substrate Coupled with Deep Learning for Ultra-Sensitive Microplastic Detection Herein, a high-performance Ta<sub>2</sub>O<sub>5</sub>/AgNPs composite Surface-Enhanced Raman Scattering (SERS) substrate is engineered for highly sensitive detection of microplastics. Through morphology modulation and band-gap engineering, the semiconductor Ta<sub>2</sub>O<sub>5</sub> is structured into spheres and composited with silver nanoparticles (AgNPs), facilitating efficient charge transfer and localized surface plasmon resonance (LSPR). This architecture integrates electromagnetic (EM) and chemical (CM) enhancement mechanisms, achieving an ultra-low detection limit of 10<sup>-13</sup> M for rhodamine 6G (R6G) with excellent linearity. Furthermore, the three-dimensional "pseudo-Neuston" network structure exhibits superior capture capability for microplastics (PS, PET, PMMA). To address spectral interference in simulated complex environments, a multi-scale deep-learning model combining wavelet transform, Convolutional Neural Networks (CNN), and Transformers is proposed. This model achieves a classification accuracy of 98.7% under high-noise conditions, significantly outperforming traditional machine learning methods. This work presents a robust strategy for environmental monitoring, offering a novel solution for precise risk assessment of microplastic pollution. Raman scattering ; Rhodamine 6G ; Deep learning ; Convolutional neural network ; Silver nanoparticle ; Plasmon ; Substrate (aquarium) ; Nanoparticle ; Semiconductor Gold and Silver Nanoparticles Synthesis and Applications ; Microplastics and Plastic Pollution ; Nanoparticles: synthesis and applications
Authors: Samira M. Alsawqaee | Eman Alzahrani | Esraa M. Bakhsh | Kalsoom Akhtar | Sher Bahadar Khan
Source: Catalysts (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Synthesis of Plant-Mediated Silver-Doped Metal Oxides Using Catharanthus roseus and Evaluation of Their Catalytic and Biological Activities This study explores the green synthesis of silver-doped lanthanum oxide (La/Ag), silver-doped yttrium oxide (Y/Ag), and silver-doped lanthanum–yttrium oxide (La/Y/Ag) nanocomposites using Catharanthus roseus extract as a natural reducing and stabilizing agent. The nanocomposites were characterized using various spectroscopic techniques to confirm their morphology, composition, crystallinity, and functional groups. La/Ag, Y/Ag, and La/Y/Ag exhibited significant catalytic activity in the reduction and degradation of methylene blue (MB), methyl orange (MO), acridine orange (AO), and 4-nitrophenol (4-NP). Optimization studies showed that La/Ag achieved complete MB reduction within 3 min, while La/Y/Ag reduced MO in 90 s. Both catalysts maintained high activity over multiple cycles, with only slight efficiency loss. In real water media, La/Ag and La/Y/Ag achieved reduction efficiencies of 98% and 97%, respectively. La/Ag also demonstrated excellent photocatalytic degradation of AO under UV light, achieving complete degradation in 80 min, and 98% degradation in tap and seawater samples. Additionally, the nanocomposites demonstrated broad-spectrum antimicrobial activity against bacterial and fungal pathogens, with varying inhibition levels across species. Acridine orange ; Catalysis ; Methyl orange ; Degradation (telecommunications) ; Nanocomposite ; Methylene blue ; Oxide ; Lanthanum Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; Nanomaterials for catalytic reactions
Authors: Kalamkar, Prathamesh | Letcher, Ned | Chami, Meissane | Lad, Sahger | Mohanty, Shayan | Pendse, Prasanna
Source: ArXiv.org (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

The Tokenization Bottleneck: How Vocabulary Extension Improves Chemistry Representation Learning in Pretrained Language Models The application of large language models (LLMs) to chemistry is frequently hampered by a "tokenization bottleneck", where tokenizers tuned on general-domain text tend to fragment chemical representations such as SMILES into semantically uninformative sub-tokens. This paper introduces a principled methodology to resolve this bottleneck by unifying the representation of natural language and molecular structures within a single model. Our approach involves targeted vocabulary extension-augmenting a pretrained LLM's vocabulary with chemically salient tokens, followed by continued pretraining on chemistry-domain text to integrate this new knowledge. We provide an empirical demonstration of the effectiveness of this strategy, showing that our methodology leads to superior performance on a range of downstream chemical tasks. Vocabulary ; Representation (politics) ; Salient ; Natural language ; Bottleneck ; Language model ; Lexical analysis ; Extension (predicate logic) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Ruijuan Qi | Tianhao Nian
Source: Preprints.org (preprint, 2025-10-30)
Field: Materials Science — Materials Chemistry

Data-Driven Understanding of Dynamic Structural Restructuring in Perovskite Fluorides for Efficient Oxygen Evolution Catalysis Electrochemical Oxygen Evolution Reaction (OER) is crucial for sustainable energy, but its sluggish kinetics demand efficient catalysts. Transition metal-based perovskite fluorides show promise due to dynamic structural restructuring (DSR) forming active oxyhydroxide layers. However, the microscopic mechanisms of DSR and its quantitative link to performance remain elusive with traditional methods. This study introduces a novel Hierarchical Ensemble Learning (HEAL) framework, integrating operando spectroscopy, electrochemical data, and theoretical calculations, to comprehensively understand DSR and optimize OER performance. The HEAL framework integrates three core modules: the Dynamic Restructuring Stage Identification Module (DRSIM) for accurate real-time DSR stage classification; the OER Activity Prediction and Optimization Module (OERPOM) for robust OER activity prediction; and the Microscopic Mechanism Interpretability Module (MMIM), leveraging Graph Neural Networks (GNNs) and SHAP value analysis to uncover critical physicochemical descriptors like F vacancy concentration and d-band center. Benchmarking against state-of-the-art models demonstrates HEAL's superior performance and interpretability. This data-driven approach offers unprecedented insights into complex electrocatalytic phenomena, providing a robust platform for rational design of high-performance catalysts. Interpretability ; Oxygen evolution ; Perovskite (structure) ; Restructuring ; Benchmarking ; Workflow ; Catalysis ; Identification (biology) Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Parashmoni Rajguru | Prarthana Bora | Chinmoy Bhuyan | Swapnali Hazarika
Source: Materials Advances (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Exploring covalent organic frameworks through the lens of computational chemistry This review provides insights into the existing and future research trends in experimental and hypothetical COFs, exploring the critical role of computational chemistry in their design and ubiquitous applications across diverse domains. Lens (geology) ; Computational model ; Covalent bond ; Context (archaeology) ; Non-covalent interactions Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes ; Heme Oxygenase-1 and Carbon Monoxide
Authors: Le Huy Khiem | Hua Ting | Nitesh V. Chawla
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AgentDrug: Utilizing Large Language Models in an Agentic Workflow for Zero-Shot Molecular Optimization Molecular optimization-modifying a given molecule to improve desired properties-is a fundamental task in drug discovery.While LLMs hold the potential to solve this task using natural language to drive the optimization, straightforward prompting achieves limited accuracy.In this work, we propose AgentDrug 1 , an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy.AgentDrug defines a nested refinement loop: the inner loop uses feedback from cheminformatics toolkits to validate molecular structures, while the outer loop guides the LLM with generic feedback and a gradient-based objective to steer the molecule toward property improvement.We evaluate AgentDrug on benchmarks with both singleand multi-property optimization under loose and strict thresholds.Results demonstrate significant performance gains over previous methods.With Qwen-2.5-3B,AgentDrug improves accuracy by 20.7% (loose) and 16.8% (strict) on six single-property tasks, and by 7.0% and 5.3% on eight multi-property tasks.With larger model Qwen-2.5-7B,AgentDrug further improves accuracy on 6 single-property objectives by 28.9% (loose) and 29.0% (strict), and on 8 multi-property objectives by 14.9% (loose) and 13.2% (strict). Workflow ; Language model ; Modeling language ; Work (physics) ; Data modeling ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management