jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrés Felipe Usuga | C. S. Praveen | Aleix Comas‐Vives
Source: ChemRxiv (preprint, 2025-09-11)
Field: Materials Science — Materials Chemistry

Explainable GNN Framework Guided by Local Chemical Features to Predict Binding Energies in Bimetallic Alloys Adsorption energies are key catalytic descriptors that reveal adsorbate-site interactions on heterogeneous catalysts. However, their computation via DFT is time-consuming, limiting high-throughput screening. This work presents a machine learning (ML) methodology based on graph representations of local adsorption sites, using a Graph Neural Network (GNN) with per-atom local descriptors derived from accessible physicochemical properties. The approach is evaluated on two bimetallic datasets. The first includes AB-type bimetallic flat surfaces with varying A:B ratios, predicting binding energies for small monodentate adsorbates (C, N, O, S, H) with MSEs of 0.073/0.181 eV (train/test). The second dataset comprises reaction energies of key intermediates for CO2 hydrogenation on Ni-Ga-based surfaces. The GNN model achieves an impressive performance (MSE: 0.001/0.002 (train/test) eV) on complex atomic configurations, even bidentate ones. Beyond predictive performance, clustering analysis provides an explainable framework, showing how structural and electronic descriptors can rationally guide catalyst design and deepen understanding of adsorbate-metal interactions. Bimetallic strip ; Computation ; Artificial neural network ; Denticity ; Catalysis ; Limiting ; Binding energy ; Cluster analysis Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Carbon dioxide utilization in catalysis
Authors: Tong Zhao | Yan Zeng
Source: Nature Chemical Engineering (article, 2025-05-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-driving nanoparticle synthesis Nanoparticle ; Self driving ; Nanotechnology ; Materials science ; Engineering ; Transport engineering Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Communication and Nanonetworks ; Micro and Nano Robotics
Authors: Soumyajit Sarkar | Soumya Chatterjee
Source: The European Physical Journal B (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Progress in two-dimensional magnets: from fundamentals to device concepts Spintronics ; Magnetism ; van der Waals force ; Leverage (statistics) ; Bridging (networking) ; Photonics ; Quantum dot ; Spin transistor 2D Materials and Applications ; Topological Materials and Phenomena ; Heusler alloys: electronic and magnetic properties
Authors: Zihan Liu | Yu Zhang | Xinyi Zhao | Yuai Duan | Zhongfeng Li | Tianyu Han | Tiandong Han
Source: Journal of Food Composition and Analysis (article, 2025-05-08)
Field: Chemistry — Spectroscopy

A nitrobenzylidene aminomaleonitrile Schiff base for banana ripening analysis based on selective vapofluorochromism Schiff base ; Ripening ; Chemistry ; Food science ; Stereochemistry Molecular Sensors and Ion Detection ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Xiaoyuan Wang | Wanying Zhang | Yibo Ai | Weidong Zhang
Source: Progress in Natural Science Materials International (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Data-driven framework for accelerated design of cerium alloy surrogates for uranium hydriding behavior Alloy ; Cerium ; Uranium ; Random forest ; Identification (biology) ; Inefficiency ; Feature (linguistics) Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Jihang Zhai | Danyang Xiong | Yueqing Zhang | Yaru Shi | Yu‐Cheng Gu | Xinmeng Chen | Shan He | Xiao He | Lianrui Hu
Source: The Journal of Physical Chemistry Letters (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

SFMMoE: A Semi-Empirical Descriptor-Augmented Multi-Expert Graph Neural Network for Accurate Prediction of Singlet Fission Properties Efficient identification of singlet fission (SF) candidates remains a significant challenge in the development of high-performance organic photovoltaic materials due to the high computational cost of accurately evaluating excited-state energetics. Here, we present SFMMoE, a graph neural network (GNN) that integrates a multiexpert multigating (MMoE) architecture with 2-HOP message passing. By integrating local topological information from molecular graphs with global molecular descriptors derived from semiempirical methods, SFMMoE enables simultaneous prediction of five key excited-state properties, including two thermodynamic criteria critical to SF: Δ<i>E</i><sub>gap1</sub> = Δ<i>E</i><sub>S1</sub> - 2Δ<i>E</i><sub>T1</sub> and Δ<i>E</i><sub>gap2</sub> = Δ<i>E</i><sub>T2</sub> - 2Δ<i>E</i><sub>T1</sub>. The model achieves a mean square error below 0.04 eV across all tasks, outperforming traditional machine learning and testing of GNN baselines. This work demonstrates that integrating multitask learning with expert specialization and graph-descriptor feature fusion substantially improves the prediction accuracy of excited-state energetics, enabling large-scale, low-cost virtual screening of SF materials with quantum-chemical accuracy. To facilitate broader access, a freely available and user-friendly online prediction server is provided at http://tech.iawnix.xyz/SFMMoE. Artificial neural network ; Graph ; Identification (biology) ; Feature (linguistics) ; Key (lock) ; Fusion ; Sensor fusion ; Mean squared error Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Graph Neural Networks
Authors: Stephanos Karafiludis | Jacob Standl | Tom William Ryll | A.J. Schwab | Carsten Prinz | Jakob B. Wolf | Sabine Kruschwitz | Franziska Emmerling | Christoph Völker | Tomasz M. Stawski
Source: Crystal Growth & Design (article, 2025-09-16)
Field: Chemistry — Inorganic Chemistry

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials. Chemical space ; Automation ; Set (abstract data type) ; Work (physics) ; Key (lock) ; Phosphate Inorganic Chemistry and Materials ; Bone Tissue Engineering Materials ; MXene and MAX Phase Materials
Authors: Projesh Saha | Bibhas Pandit | Sabyasachi Pramanik | Bhupendra Shrestha
Source: Journal of Applied Pharmaceutical Research (review, 2025-06-30)
Field: Chemistry — Analytical Chemistry

A comprehensive review on the applications of chemometrics in analytical chemistry Background: This article presents a review of the various applications of chemometrics in analytical chemistry. Chemometrics is essential to analytical chemistry because it provides sophisticated methods for extracting, analyzing, and interpreting chemical data. To maximize analytical procedures, enhance data reliability, and extract insights, this field combines statistical and mathematical techniques with chemical research. Chemometrics provides analytical chemists with the means to manage the massive datasets generated by contemporary analytical methods, such as spectroscopy and chromatography. Methodology: This review combines data from previous research articles that have elaborated and described the various applications of chemometrics in the analytical chemistry sector. Results and Discussion: The combination of chemometrics with artificial intelligence and machine learning offers more advanced analytical and predictive modelling possibilities. It is anticipated that these developments will transform analytical chemistry by enhancing researchers' ability to manage complex datasets and gain deeper insights from their investigations. This is especially important in industries where precise data interpretation is critical, such as pharmaceuticals, ecological surveillance, and food safety. Conclusion: Chemometrics is essential to contemporary analytical chemistry because it provides methods and instruments that enhance quality control, facilitate innovative research advancements, and improve data analysis capabilities. The accuracy and efficacy of chemical analyses are expected to continue to improve as this sector develops. Chemometrics ; Computer science ; Biochemical engineering ; Field (mathematics) ; Management science ; Chemistry ; Data science ; Machine learning ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Orsi, Markus | Frei, Angelo
Source: Open Access CRIS of the University of Bern (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds. Machine learning has experienced a drastic rise in interest and applications in all fields of chemistry, enabling researchers to leverage large chemical datasets to gain novel insights. The success of machine learning-driven projects in chemistry hinges on three key factors: access to robust and comprehensive datasets, a well-defined objective, and effective molecular representations that convert chemical structures into machine-readable formats. Transition metal complexes have lagged behind their organic counterparts on all three of these avenues. The large diversity of structures, coordination numbers and modes have made its translation to a machine-readable format an ongoing challenge. Here we introduce ELECTRUM, an electron configuration-based universal metal fingerprint for transition metal compounds. Its lightweight implementation enables the straightforward conversion of any transition metal complex into a simple fingerprint. Utilising a novel dataset generated from the Cambridge Structural Database (CSD), we demonstrate that ELECTRUM effectively captures the structural diversity of transition metal complexes. By plotting nearest-neighbor relationships in ELECTRUM space, we reveal meaningful clustering in two-dimensional representations. Furthermore, we use the ELECTRUM encoding to train machine learning models on the prediction of metal complex coordination numbers from ligand structures and metal identity alone. We show that on a subset of this data, we can train models to predict the oxidation state of metal complexes. These case studies showcase the potential of ELECTRUM as an easy-to-implement fingerprint for metal complexes. We rely on the community to further test, validate, and improve it. Transition metal ; Fingerprint (computing) ; Leverage (statistics) ; Cluster analysis ; Identity (music) ; Key (lock) ; Metal Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ziyang Wu | Zhangcheng Liang | Hang Wei | Peixin Wang | Weiming Li | Hanbin Chen | Baodong Zheng
Source: Food Research International (article, 2025-12-22)
Field: Chemistry — Organic Chemistry

Machine learning-enabled flavoromics decoding the molecular basis of aroma profiles in jasmine essential oil under different extraction processes Aroma ; Odor ; Essential oil ; Flavor ; Extraction (chemistry) ; Citronellol ; Yield (engineering) ; Supercritical fluid extraction Edible Oils Quality and Analysis ; Fermentation and Sensory Analysis ; Essential Oils and Antimicrobial Activity
Authors: Linus C. Erhard | Johannes Schörghuber | Aleix Comas‐Vives | Georg K. H. Madsen
Source: ArXiv.org (preprint, 2025-09-22)
Field: Chemistry — Physical and Theoretical Chemistry

How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces Copper is a highly promising catalyst for the electrochemical CO$_2$ reduction reaction (CO2RR) since it is the only pure metal that can form highly added-value products such as ethylene and ethanol. Since the CO2RR takes place in aqueous solution, the detailed atomic structure of the water-copper interface is essential for unraveling the key reaction mechanisms. In this study, we investigate copper-water interfaces exhibiting nanometer-scale roughnesses. We introduce two molecular dynamics protocols to create rough copper surfaces, which are subsequently brought into contact with water. From these interfaces, we sample additional training configurations from machine-learning-interatomic-potential-driven molecular dynamics simulations containing hundreds of thousands of atoms. An active learning workflow is developed to identify regions with high spatially resolved uncertainty and convert them into DFT-feasible cells through a modified amorphous matrix embedding approach. Finally, we analyze the local environments at the interface using unsupervised machine-learning techniques. Unique environments emerge on the rough copper surfaces absent from model systems, including stacking-fault-induced configurations and undercoordinated corner atoms. Notably, corner atoms consistently feature chemisorbed water molecules in our simulations, indicating their potential importance in catalytic processes. Copper ; Embedding ; Workflow ; Molecular dynamics ; Interface (matter) ; Catalysis ; Metal ; Amorphous solid Electrostatics and Colloid Interactions ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Ana Kostov | Aleksandra Milosavljević | Ivan Jovanović
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

DTA thermodynamic behaviour of the ternary system Ga-Ge-Sb Finding new types of materials, usually by modernizing classic materials by introducing and combining some of the new components within the already existing standard ones, based on improved and innovated classical materials, leads to the production of materials that are characterized by a complex of new features that enable a wide range of their practical applications. It is widely known that rare metals such as gallium and germanium possess special features that are necessary for further progress in electronics. These materials are used in electrical engineering, radio engineering and electronics, in optical and electronic devices and instruments as functional materials for transistors, diodes, integral circuits, optical cables, conductors, rectifiers, etc. in atomic and astrophysical testing, aerospace industry, laser technology, sensors of neural networks, atomic reactors and the semiconductor industry. Given the wide range of applications and use of these materials in almost all-important areas and techniques, there is a need for a more complete knowledge of the properties of not only these elements, but also the systems of which they are integral parts, from the aspect of their fuller description and thermodynamic determination. Therefore, in this paper, research in the ternary system Ga-Ge-Sb will be presented in order to complete the thermodynamic description of the system. Its practical significance and application are mostly in the semiconductor industry, from the point of view of defining the thermodynamic quantities of the components constituting this system, as well as the results characterization of the thermodynamic behavior of the investigated alloys by DTA analysis of this system. Characterization (materials science) ; Ternary operation ; Material properties ; Semiconductor ; Thermodynamic system ; Thermodynamic process ; Range (aeronautics) ; Ternary numeral system ; Complex system Phase-change materials and chalcogenides ; Semiconductor materials and interfaces ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Jihang Zhai (22620326) | Danyang Xiong (12962632) | Yueqing Zhang (2931549) | Yaru Shi (2497831) | Yu-cheng Gu (9655208) | Xinmeng Chen (5796239) | Shan He (121220) | Xiao He (223527) | Lianrui Hu (1486087)
Source: Figshare (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

SFMMoE: A Semi-EmpiricalDescriptor-Augmented Multi-ExpertGraph Neural Network for Accurate Prediction of Singlet Fission Properties Efficient identification of singlet fission (SF) candidates remains a significant challenge in the development of high-performance organic photovoltaic materials due to the high computational cost of accurately evaluating excited-state energetics. Here, we present SFMMoE, a graph neural network (GNN) that integrates a multiexpert multigating (MMoE) architecture with 2-HOP message passing. By integrating local topological information from molecular graphs with global molecular descriptors derived from semiempirical methods, SFMMoE enables simultaneous prediction of five key excited-state properties, including two thermodynamic criteria critical to SF: Δ<i>E</i><sub>gap1</sub> = Δ<i>E</i><sub>S1</sub> – 2Δ<i>E</i><sub>T1</sub> and Δ<i>E</i><sub>gap2</sub> = Δ<i>E</i><sub>T2</sub> – 2Δ<i>E</i><sub>T1</sub>. The model achieves a mean square error below 0.04 eV across all tasks, outperforming traditional machine learning and testing of GNN baselines. This work demonstrates that integrating multitask learning with expert specialization and graph–descriptor feature fusion substantially improves the prediction accuracy of excited-state energetics, enabling large-scale, low-cost virtual screening of SF materials with quantum-chemical accuracy. To facilitate broader access, a freely available and user-friendly online prediction server is provided at http://tech.iawnix.xyz/SFMMoE. Artificial neural network ; Graph ; Identification (biology) ; Feature (linguistics) ; Key (lock) ; Fusion ; Sensor fusion ; Mean squared error Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Graph Neural Networks
Authors: N. M. Anoop Krishnan
Source: Physics and Chemistry of Glasses European Journal of Glass Science and Technology Part B (article, 2025-08-18)
Field: Materials Science — Ceramics and Composites

Combining machine learning, simulations and experiments for improved glass design The design of novel glasses with tailored properties remains a grand challenge in materials science, traditionally requiring extensive trial-and-error experimentation. This talk presents a comprehensive framework that integrates machine learning, atomistic simulations, and targeted experiments to accelerate the discovery and optimization of glass compositions. We demonstrate how large-scale machine learning models trained on experimental databases can predict glass properties across nearly the entire periodic table, enabling rapid screening of promising compositions. These predictions are refined through physics-informed graph neural networks that capture atomic-scale interactions and energy conservation principles, providing deeper insights into structure-property relationships. The framework is enhanced by automated knowledge extraction from scientific literature using our materials-specific language model (MatSciBERT) and natural language processing techniques. We showcase practical applications including the development of radiation-resistant nuclear waste glasses, scratch-resistant display glasses, and specialty glasses for space applications. The talk concludes by highlighting how this integrated approach has led to the discovery of novel glass compositions with superior properties, validated through experimental synthesis and characterization. Our results demonstrate that the synergistic combination of artificial intelligence, computational modeling, and experimental validation can significantly accelerate the glass design process while reducing resource requirements and environmental impact. Process (computing) ; Artificial neural network ; Resource (disambiguation) ; Design of experiments ; Graph ; Chemical space Glass properties and applications ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Aravindh Nivas Marimuthu (21394170) | Brett A. McGuire (1278318)
Source: Figshare (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Machine LearningPipeline for Molecular Property PredictionUsing ChemXploreML We present ChemXploreML, a modular desktop application designed for machine learning-based molecular property prediction. The framework’s flexible architecture allows integration of any molecular embedding technique with modern machine learning algorithms, enabling researchers to customize their prediction pipelines without extensive programming expertise. To demonstrate the framework’s capabilities, we implement and evaluate two molecular embedding approachesMol2Vec and VICGAE (Variance-Invariance-Covariance regularized GRU Auto-Encoder)combined with state-of-the-art tree-based ensemble methods (Gradient Boosting Regression, XGBoost, CatBoost, and LightGBM). Using five fundamental molecular properties as test casesmelting point, boiling point, vapor pressure, critical temperature (CT), and critical pressurewe validate our framework on a data set from the CRC Handbook of Chemistry and Physics. The models achieve excellent performance for well-distributed properties, with <i>R</i><sup>2</sup> values up to 0.93 for CT predictions. Notably, while Mol2Vec embeddings (300 dimensions) delivered slightly higher accuracy, VICGAE embeddings (32 dimensions) exhibited comparable performance yet offered significantly improved computational efficiency. ChemXploreML’s modular design facilitates easy integration of new embedding techniques and machine learning algorithms, providing a flexible platform for customized property prediction tasks. The application automates chemical data preprocessing (including UMAP-based exploration of molecular space), model optimization, and performance analysis through an intuitive interface, making sophisticated machine learning techniques accessible while maintaining extensibility for advanced cheminformatics users. Pipeline (software) ; Modular design ; Embedding ; Property (philosophy) ; Cheminformatics ; Workbench ; Pipeline transport ; Boosting (machine learning) ; Preprocessor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks