jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Dolz | Sara Pibernat | Ángel Morales‐García | Francesc Viñes | Francesc Illas
Source: npj 2D Materials and Applications (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

Accurate Prediction of Adsorption and Diffusion Energies of Single Metal Atoms Supported on MXenes from Machine Learning Single atom catalysts (SACs) are frontier composites maximizing the active phase activity, but require stabilization. This study conducted a high-throughput analysis of 54 pristine MXenes as supports for the 30 3d, 4d, and 5d transition metals (TMs), exploring 1620 cases. First-principles calculations on MXene models showed patterns in the adsorption energies, Eads, of the TM single-atom (SA), revealing high Eads, except for d5 or d10 TM electronic configurations. The SA diffusion barriers, Eb, revealed easy diffusions, although in some cases high Eb inhibited aggregation or dispersion. Random forest regressor (RFR) machine learning predicted Eads with a mean absolute error (MAE) of 0.25 eV, and a regression coefficient of 0.99, showing that the TM cohesive energy is key in Eads prediction. Here, the RFR model reported a MAE of 0.1 eV, with few MXene and SA properties being important. Our findings provide insights to use MXenes as support for SACs or TM clusters. MXenes ; Random forest ; Diffusion ; Adsorption ; Transition metal ; Mean absolute error ; Phase (matter) MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction ; Nanomaterials for catalytic reactions
Authors: S. Mondal | S. Ray | Aritra Acharyya | Rudra Sankar Dhar | Arindam Biswas | Hiroaki Satoh | Gurudas Mandal | V. A. Maksimenko | В. В. Криштоп
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8–20.1% of the computational time. Mean square errors are observed to be on the order of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–4</sup>–10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–6</sup>, demonstrating the models’ high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices. Artificial neural network ; Terahertz radiation ; Computer science ; Materials science ; Optoelectronics ; Biological system ; Artificial intelligence Spectroscopy and Laser Applications ; GaN-based semiconductor devices and materials ; Terahertz technology and applications
Authors: Azzam Alfarraj | Monther Rashed Alfuraidan | Abdul Malik P. Peedikakkal | Ibrahim O. Sarumi
Source: Journal of Chemical Information and Modeling (article, 2025-09-29)
Field: Chemistry — Inorganic Chemistry

Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal–Organic Frameworks Hydrogen is a clean and high-energy fuel, yet its safe and efficient storage remains a key obstacle to widespread adoption. Metal-organic frameworks (MOFs), with their high surface area and tunable porosity, have emerged as promising candidates for solid-state hydrogen storage. In this work, we introduce a graph-based machine learning framework for predicting hydrogen uptake in MOFs by integrating spectral graph theory with data-driven modeling. Molecular structures are represented as weighted graphs from which we extract 20 graph-based descriptors─including Laplacian spectral features, degree statistics, and Zagreb indices─that capture both topological and geometric characteristics of the framework. These interpretable descriptors are used to train multiple regression models on a data set of 3300 MOFs from the Cambridge Structural Database. The XGBoost regressor achieved the highest performance in predicting hydrogen uptake, with a coefficient of determination (<i>R</i><sup>2</sup>) of 0.737, RMSE of 0.850% wt, and MAE of 0.433% wt for gravimetric uptake (UG); and a coefficient of determination (<i>R</i><sup>2</sup>) of 0.698, RMSE of 4.467 g H<sub>2</sub>/L, and MAE of 3.045 g H<sub>2</sub>/L for volumetric uptake (UV). Beyond accurate prediction, the framework enables inverse materials design by identifying graph-based motifs that contribute to improved storage capacity. This integration of chemical graph theory with machine learning provides a scalable, interpretable, and computationally efficient pathway for the discovery of next-generation MOFs tailored for hydrogen storage and other clean energy applications. Mean squared error ; Graph ; Obstacle ; Hydrogen storage ; Graph theory ; Linear regression ; Regression ; Key (lock) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Harsh Mishra | Prathamesh Deepak Ladhe | Shikhar Misra
Source: ACS Applied Optical Materials (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Optical Property Prediction of Thin Films Using Spectral Data Extraction from Scientific Literature The integration of machine learning (ML) into materials science has demonstrated significant potential in accurately and efficiently predicting material properties. This study presents a data-driven approach for extracting refractive index spectra from line plots in scientific literature, transforming previously underutilized graphical data into a structured data set for predictive modeling. Advanced data extraction tools, including ChartOCR and Plot2Spectra, were employed alongside rigorous feature engineering to curate a high-quality data set comprising 1,770 samples. The preprocessing pipeline encompassed spectral data extraction, normalization, and the incorporation of key features, such as elemental composition, synthesis parameters, and wavelength-dependent properties. An XGBoost model was trained and evaluated, achieving a mean absolute percentage error (MAPE) of 0.149, highlighting the effectiveness of this approach. These findings underscore the potential of ML-driven methodologies for enhancing optical property predictions and automating data extraction, thereby facilitating broader applications in materials research. Preprocessor ; Pipeline (software) ; Property (philosophy) ; Data set ; Data pre-processing ; Set (abstract data type) ; Feature extraction ; Term (time) ; Data integration ; Data extraction Machine Learning in Materials Science ; Nonlinear Optical Materials Studies ; Phase-change materials and chalcogenides
Authors: Xiurong Yang | Ying Wang | Ling Pan | Ruihui Wang | Yi Wang | Honglei Xia | Siwei Song | Qinghua Zhang
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Graph-based intermolecular interaction prediction enables rational design of co-crystals An interaction energy-driven criterion for co-crystal screening was developed, leveraging a high-precision graph neural network-based interaction energy predictor to significantly enhance screening efficiency. Intermolecular force ; Rational design ; Graph ; Computer science ; Intermolecular interaction ; Materials science ; Chemistry ; Theoretical computer science ; Nanotechnology ; Molecule ; Organic chemistry Crystallography and molecular interactions ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Yifei Wang | Enqi Sun | Zhe Zhang | Yahui Chen | Xia Cao | Ning Wang
Source: Chemical Engineering Journal (article, 2025-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecularly engineered, 1300 °C-stable aerogel with integrated flame retardancy, EMI shielding, and self-powered sensing for AI-predictive battery safety and rescue systems Aerogel ; EMI ; Robustness (evolution) ; Electromagnetic shielding ; Battery (electricity) ; Electrostatic discharge ; Resilience (materials science) ; Microgrid Supercapacitor Materials and Fabrication ; Gas Sensing Nanomaterials and Sensors ; Advanced Battery Technologies Research
Authors: Elena Trukhan | Efim Mazhnik | Artem R. Oganov
Source: npj Computational Materials (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Acceleration of crystal structure relaxation with deep reinforcement learning Abstract We introduce a Deep Reinforcement Learning (DRL) model for crystal structure relaxation and compare different types of neural network architectures and reinforcement learning algorithms for this purpose. Numerical experiments are conducted on Al-Fe structures, with potential energy surfaces generated using EAM potentials. We examine the influence of parameter settings on model performance and benchmark the best-performing models against classical optimization algorithms. Additionally, the model’s capacity to generalize learned interaction patterns from smaller atomic systems to more complex systems is assessed. The results demonstrate the potential of DRL models to enhance the efficiency of structure relaxation compared to classical optimizers. Reinforcement learning ; Relaxation (psychology) ; Benchmark (surveying) ; Acceleration ; Artificial neural network ; Reinforcement ; Crystal structure ; Energy (signal processing) Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hussein Ali Kadhim Kyhoiesh | Azal S. Waheeb | Karrar Hazim Salem | Ashraf Y. Elnaggar | Islam H. El Azab | M.H.H. Mahmoud | Dunya J. Kassid | H. Salman
Source: Materials Chemistry and Physics (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

A data driven investigation of photostability and formation energies in arsenic-containing compounds: machine learning-gaussian process output analysis Arsenic ; Gaussian process ; Process (computing) ; Gaussian ; Materials science ; Chemistry ; Artificial intelligence ; Computer science ; Statistical physics ; Biological system ; Machine learning ; Computational chemistry ; Physics ; Metallurgy Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Ionic liquids properties and applications
Authors: Renai Chen | Zachary Miller | Tammie Gibson | Vedant Mehta | Massimiliano Fratoni | Alex Levinsky | Galen T. Craven
Source: Journal of Nuclear Materials (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

Machine learning models for volumetric swelling in uranium nitride Uranium ; Swelling ; Nitride ; Materials science ; Nuclear engineering ; Radiochemistry ; Metallurgy ; Environmental science ; Chemistry ; Composite material ; Engineering Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Yongxian Wu | Ray Luo
Source: Journal of Chemical Theory and Computation (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Grid-Context Convolutional Model for Efficient Molecular Surface Construction from Point Clouds Accurate and efficient molecular surface representation is essential in computational chemistry, impacting applications such as enzymology, rational drug design, and molecular recognition. Traditional approaches, including solvent-accessible surface (SAS) and solvent-excluded surface (SES), are widely used but often suffer from computational inefficiencies and limited adaptability to complex molecular structures. More recently, grid-independent machine learning methods have accelerated surface construction; however, these approaches rely solely on local single-grid information and fail to capture grid-context interactions, leading to inaccuracies in modeling complex interior surface regions. In this work, we introduce Con2SES, a novel framework that explicitly incorporates grid-context interactions to improve the accuracy and robustness of molecular surface construction. By leveraging learnable convolutional operators in both two-dimensional (2D) and three-dimensional (3D) space, Con2SES mitigates the limitations of grid-independent methods and ensures a more continuous and accurate surface construction. Extensive evaluations on protein molecules and diverse molecular collections demonstrate that Con2SES significantly outperforms grid-independent approaches in accuracy while maintaining computational efficiency comparable to that of the fastest grid-independent method, achieving approximately 99% accuracy and a 28-fold speedup compared to the classical SES implementation in the AMBER software package. Furthermore, Con2SES has been integrated into the AMBER platform and released as an open-source tool, providing a high-performance solution for large-scale molecular surface analysis. Computer science ; Context (archaeology) ; Grid ; Point cloud ; Point (geometry) ; Surface (topology) ; Data science ; Artificial intelligence ; Geography ; Mathematics Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Vu Khac Hoang Bui | Vinh Van Tran | Hai Le Tran | Ha Tran Huu | Minh‐Ky Nguyen | Jin Hur
Source: Environmental Research (review, 2025-08-27)
Field: Materials Science — Materials Chemistry

Layered double hydroxides and layered double oxides: Multifunctional catalysts for the detection, degradation, and transformation of phenolic compounds in environmental applications Layered double hydroxides ; Degradation (telecommunications) ; Catalysis ; Double layered ; Transformation (genetics) ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Computer science ; Composite material Layered Double Hydroxides Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Shweta Sharma | Natan Kaminsky | Kira Radinsky | Lilac Amirav
Source: Journal of Chemical Information and Modeling (article, 2025-05-28)
Field: Chemistry — Electrochemistry

Predicting Oxidation Potentials with DFT-Driven Machine Learning We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (<i>E</i><sub>HOMO</sub>), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (<i>E</i><sub>ox</sub>). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear correlation between <i>E</i><sub>HOMO</sub> and experimental <i>E</i><sub>ox</sub> values, achieving an exceptional correlation coefficient (<i>R</i><sup>2</sup>) of 0.977 and a low root-mean-square error (RMSE) of 0.064. The correlation highlights the accuracy of OxPot as a machine learning (ML)-ready resource for <i>E</i><sub>ox</sub> prediction. To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. This analysis offers valuable insights into the key molecular descriptors that influence <i>E</i><sub>ox</sub> predictions, thereby enhancing model interpretability and guiding the design of more effective predictive models. Furthermore, the computational efficiency of the methodology ensures rapid predictions of <i>E</i><sub>ox</sub> for additional chemically similar molecules, thereby increasing its applicability for large-scale molecular screening and broader applications in chemical research. Computer science ; Artificial intelligence ; Machine learning Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Water Quality Monitoring and Analysis
Authors: Yonghui Gao | Limin Shang | Jing Liu | Zhiling Zhu
Source: The Journal of Physical Chemistry Letters (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials present significant challenges in identifying new agents that are both efficient and cost-effective with broad-spectrum activity. In response, this study applied machine learning for the first time to discover antimicrobial inorganic nanomaterials. Information on over 2,000 antimicrobial nanomaterials was extracted from more than 8,000 papers. An unsupervised machine learning analysis was conducted to assess data distribution and explore the relationships between material features and antimicrobial activity in high-dimensional space. A series of machine learning models were trained. Through the evaluation of six performance metrics, five key features were identified from 27 dimensions. To further quantify the structure-activity relationships, a genetic programming-symbolic classification model was employed to generate a precise mathematical formula with a prediction accuracy of 0.83. Using this formula, 43 new antimicrobial inorganic nanomaterials were predicted. Of these, four nanomaterials were synthesized and their antibacterial properties were experimentally validated. This work not only provides a next generation approach for designing antimicrobial inorganic nanomaterials but also opens new avenues for applying machine learning in materials science. Antimicrobial ; Nanomaterials ; Nanotechnology ; Chemistry ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Antimicrobial Peptides and Activities
Authors: Tao Sun | Rui Wang | Renquan Guan | Li Wang | Tian Zhong | Chunbo Liu | Xueying Cheng | Qianrong Fang
Source: Chemical Science (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Design strategies and application progress of covalent organic frameworks in photocatalytic oxidation reactions Photocatalytic generation of reactive oxygen species (ROS) presents a sustainable alternative to traditional oxidation methods, offering precise control over reaction pathways for diverse applications. Covalent organic frameworks (COFs), with their crystalline porous structures and tunable electronic properties, are ideal platforms for maximizing photocatalytic ROS efficiency and selectivity. This review systematically explores the intrinsic connection between COF architecture and ROS activity, framed by a "structure-ROS-substrate" paradigm. We detail how rational design strategies-from foundational band energy engineering and π-conjugation control to functional group integration and pore microenvironment regulation-precisely modulate the material's electronic structure, charge carrier dynamics, and mass transport to govern ROS speciation and concentration. These structural innovations underpin the remarkable performance of COFs in photocatalytic oxidation, including selective organic transformations, sustainable H<sub>2</sub>O<sub>2</sub> production, and efficient degradation of recalcitrant pollutants. The application scope is further extended to biomedical fields, leveraging ROS for antibacterial therapy and photodynamic effects. Finally, we discuss prevailing challenges in quantitative ROS detection, operational stability, and scalable synthesis, while outlining future opportunities in machine-learning-guided design and tandem catalytic systems. This review aims to establish fundamental design principles for the next generation of COF-based photocatalysts, bridging molecular-level engineering to macroscopic oxidative efficacy. Rational design ; Photocatalysis ; Catalysis ; Covalent bond ; Oxygen evolution ; Scope (computer science) ; Design elements and principles ; Scalability ; Sustainable energy Covalent Organic Framework Applications ; Oxidative Organic Chemistry Reactions ; Advanced oxidation water treatment
Authors: K. Radhakrishnan | Aditya Kumar
Source: Renewable and Sustainable Energy Reviews (article, 2025-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances and challenges in biomass-derived supercapacitors Supercapacitor ; Biomass (ecology) ; Energy storage ; Environmentally friendly ; Carbon fibers ; Capacitor Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Shirong Qiang | Lulu Liu | Siqi Li | Shuang Wang | X. T. Huang | Jiaxin Yang | Jiayu Song | Yue Zhang | Yongxiang Huang | Qiaohui Fan
Source: Toxics (article, 2025-04-13)
Field: Chemistry — Inorganic Chemistry

Prediction of the Non-Reducing Biomineralization of Nuclide–Microbial Interactions by Machine Learning: The Case of Uranium and Bacillus subtilis <i>Bacillus subtilis</i> exhibits a great affinity to soluble U(VI) through non-reducing biomineralization. The pH value, temperature, initial uranium concentration, bacterial concentration, and adsorption time are recognized as the five environmental sensitive factors that can regulate the degree of non-reductive biomineralization. Most of the current studies have focused on the regulatory mechanisms of these factors on uranium non-reductive mineralization. However, there are still few reports on the importance of these factors in influencing non-reductive mineralization, as well as on how to regulate these factors to increase the efficiency of non-reductive mineralization and enhance the enrichment of <i>Bacillus subtilis</i> on uranium. In this work, a deep learning neural network model was constructed to effectively predict the effects of changes in these five environmental sensitivity factors on the non-reducing mineralization of <i>Bacillus subtilis</i> to uranium. Accuracy (99.6%) and R<sup>2</sup> (up to 0.89) confirm a high degree of agreement between the predicted output and the observed values. Sensitivity analysis shows that in this model, pH value is the most important influencing factor. However, under different pH values, temperature, initial uranium concentration, adsorption time, and bacterial concentration have different effects. When the pH value is lower than 6, the most important factor is temperature, and once the pH value is greater than 6, the initial concentration is the most important factor. The results are expected to provide a theoretical basis for regulating the enrichment degree of U(VI) by <i>Bacillus subtilis</i>, achieving the maximum long-term stable fixation of U(VI), and understanding the environmental chemical behavior of uranium under different conditions. Bacillus subtilis ; Uranium ; Biomineralization ; Mineralization (soil science) ; Chemistry ; Uraninite ; Environmental chemistry ; Adsorption ; Chemical engineering ; Bacteria ; Geology ; Materials science ; Metallurgy Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping ; Radioactivity and Radon Measurements