jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gonghao Lian | Xiaoming Liu | Qiang Wang | Chunguang Shen | Yi Wang | Wangzhong Mu
Source: International Journal of Minerals Metallurgy and Materials (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence-assisted non-metallic inclusion particle analysis in advanced steels using machine learning: A review Abstract The detection and characterization of non-metallic inclusions are essential for clean steel production. Recently, imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence (AI)-based machine learning (ML) has developed rapidly. This technique has achieved impressive results in the field of inclusion classification in process metallurgy. The present study surveys the ML modeling of inclusion prediction in advanced steels, including the detection, classification, and feature prediction of inclusions in different steel grades. Studies on clean steel with different features based on data and image analysis via ML are summarized. Regarding the data analysis, the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters. Regarding the image analysis, the focus is placed on the classification of different types of inclusions via deep learning, in comparison with data analysis. Finally, further development of inclusion analyses using ML-based methods is recommended. This work paves the way for the application of AI-based methodologies for ultraclean-steel studies from a sustainable metallurgy perspective. Inclusion (mineral) ; Non-metallic inclusions ; Field (mathematics) ; Characterization (materials science) ; Feature (linguistics) ; Process (computing) ; Image processing Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Microstructure and Mechanical Properties of Steels
Authors: Hoda M Hammad | Anna M. Duraj‐Thatte
Source: Advanced Intelligent Systems (article, 2026-01-06)
Field: Materials Science — Biomaterials

AI Guided Protein Design for Next‐Generation Autogenic Engineered Living Materials Engineered living materials (ELM) enable the fabrication of innovative, self‐regenerating, and self‐healing functional materials with new physicochemical and biological properties for diverse applications in sensing, therapeutics, and structural engineering. Through advances in protein engineering, microbial cells can be programmed to produce, secrete, and assemble proteinaceous scaffolds, forming a robust and customizable platform for functional materials. This perspective outlines a transformative vision for expanding the design space of autogenic ELM via AI‐guided protein engineering. Specifically, it highlights how de novo protein design can generate entirely new structural building blocks and establish direct links between scaffold composition and macroscopic material performance. Current scaffold engineering strategies, including N‐terminal, C‐terminal, and coupled terminal fusion, as well as core modifications, that have enabled initial functionalization and mechanical programmability of the ELM scaffold are reviewed. However, these methods often face limitations due to fibril destabilization and misfolding of appended domains, restricting material tunability. Recent breakthroughs in deep‐learning‐based structure prediction and protein sequence design now allow for generating de novo protein building blocks that expand beyond natural sequence and structure constraints. These developments set the stage for a new generation of genetically programmable protein scaffolds with customizable mechanical properties and advanced functionality. Scaffold ; Protein design ; Protein engineering ; Synthetic biology ; Biocompatible material ; Sequence (biology) Silk-based biomaterials and applications ; Supramolecular Self-Assembly in Materials ; Biochemical and Structural Characterization
Authors: Zhixia Xu | Yue Hua | Xianghong Kong | Haotian Wu | Jie Chang | Baofei Wan | Yang Shen | Tie Jun Cui
Source: PhotoniX (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Band folding unlocks high-density hidden modes for sub-terahertz cancer cell phenotyping Abstract Metamaterial-based biosensors (meta-biosensors) offer a versatile platform for label-free detection of biological analytes. However, conventional multi-mode designs often rely on intricate configurations with uncertain mode excitability under free-space incidence. Here, we propose a band-folding-enabled meta-biosensor that supports high density of free-space- excitable modes. Guided by a Fourier-series theoretical framework developed to predict the coupling strengths, numerous symmetry-protected hidden modes are converted into radiative resonances by introducing perturbations into a hexagonal superlattice. We apply this multi- mode platform to phenotype different biological cells at sub-terahertz frequencies. Supported by histopathological validation, we demonstrate that the dense accumulation of intracellular biomass and enlarged nuclei in malignant cells induce a distinct volumetric permittivity contrast. This mechanism could enable rapid differentiation of cancerous phenotypes from the normal counterparts. Our work bridges metamaterial device physics with biological structural insights, broadening the horizon for mode-multiplexed metamaterials in phenotypic screening. Metamaterial ; Folding (DSP implementation) ; Coupling (piping) ; Mechanism (biology) ; Cancer cell ; Permittivity ; Phenotype Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Gasser Abdelal | Chi-Wai Chan | Seán McLoone
Source: Applied Sciences (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Critical Review of Recent Advances in AI-Enhanced SEM and EDS Techniques for Metallic Microstructure Characterization This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Interpretability ; Characterization (materials science) ; Convolutional neural network ; Microstructure ; Troubleshooting ; Key (lock) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Microstructure and Mechanical Properties of Steels
Authors: Aurelien Personnaz | Sihyun Sung | Raphaël Bourgeas | Sruthi Unni | Florine Dupeux | Bukunmi Adediran | Rosicler L. Barbosa | Anne-Sophie Humm | Euan Colaco-Osorio | José Antonio Márquez
Source: IUCrJ (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

AXIS: a Lab-in-the-Loop machine learning approach for automated detection of macromolecular crystals Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robotic systems for crystal growth and diffraction analysis is widespread and high-throughput protein-to-structure pipelines for ligand and fragment screening are revolutionizing the field. However, the identification of crystals is still largely carried out through manual inspection, sometimes involving tens of thousands of images, which represents a bottleneck in an otherwise highly automated process. Here we describe AXIS, an AI-based Crystal Identification System combining the DINOv2 computer vision model, state-of-the-art transfer learning and MARCO, the largest crystallization dataset available to date, for automated crystal detection. AXIS can operate with both visible and UV light images and integrates a Lab-in-the-Loop approach combining ML and expert inputs for iterative learning and specialization. AXIS enables automated annotation of large crystallization image datasets with performance and accuracy comparable to that of human experts, and the Lab-in-the-Loop approach introduced here enables efficient adaptation to local conditions, facilitating widespread application, which has been a major limitation to date. AXIS can help to correct human errors in image annotation and removes critical bottlenecks, particularly in the context of extensive crystallization screens or high-throughput applications like fragment and ligand screening, unlocking the potential for higher levels of automation that are key in both fundamental and translational research. Automation ; Bottleneck ; Context (archaeology) ; Identification (biology) ; Annotation ; Protein crystallization ; Machine vision Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Sk Mujaffar Hossain | Namitha Anna Koshi | Seung‐Cheol Lee | G.P Das | Satadeep Bhattacharjee
Source: Small Methods (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode materials. In this work, we present a fully connected deep neural network (FCDNN) model, built using PyTorch, to estimate the average voltage of cathode materials across Li-ion, Na-ion, and other alkali-metal-ion batteries. The model is trained on an extensive dataset from the Materials Project, incorporating a wide range of specific structural, physical, chemical, electronic, thermodynamic, and battery descriptors, ensuring a comprehensive representation of material properties. Our model exhibits strong predictive performance, as corroborated by first-principles density functional theory (DFT) calculations. The close alignment between the FCDNN predictions and the DFT outcomes highlights the robustness and accuracy of our machine learning framework to effectively select and identify viable battery materials. Using this validated model, we successfully proposed novel Na-ion battery compositions, with their predicted behavior confirmed by rigorous computational assessment. By seamlessly integrating data-driven prediction with first-principles validation, this study presents an effective framework that significantly accelerates the discovery and optimization of advanced battery materials, contributing to the development of more reliable and efficient energy storage technologies. Battery (electricity) ; Robustness (evolution) ; Voltage ; Artificial neural network ; Range (aeronautics) ; Representation (politics) ; Energy storage ; Deep learning Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Yu Jin | Hang-Biao Lv | Shisheng Zheng | Jian-Feng Li
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Feature engineering methods for machine learning in heterogeneous catalysis Machine learning is revolutionizing the field of heterogeneous catalysis, transitioning from a supporting tool to a central force in materials discovery and mechanistic understanding. At the heart of this transformation lies feature engineering, which bridges the catalyst structure with predictive modeling capabilities. In this review, we provide a systematic overview of the evolution of feature engineering in heterogeneous catalysis. This progression spans hand-crafted descriptors, symbolic regression methods, graph-based features that capture intricate chemical and geometric relationships, topological data features encoding multiscale structural invariants, and most recently, multimodal representations that integrate textual data and structure into unified feature spaces. Despite these advancements, several challenges remain in feature engineering, including the underdevelopment of multimodal representations, limited model interpretability, and the absence of cross-scale structural descriptors. Emerging strategies aimed at addressing these issues are discussed in detail. We hope that this review will inspire further innovation in feature engineering methodologies tailored to the continued advancement of heterogeneous catalysis. Feature (linguistics) ; Feature engineering ; Field (mathematics) ; Feature learning ; Transformation (genetics) ; Feature extraction ; Model transformation Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: M.V. Jyothirmai | Shrish Nath Upadhyay | S Rama Gokula Krishnan | Sudipta Roy | Jayant K. Singh
Source: Journal of Power Sources (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

From density functional theory to machine learning: Emerging paradigms in energy materials discovery Density functional theory ; Pipeline (software) ; Multiscale modeling ; Ab initio ; Workflow ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Flexibility (engineering) ; Inverse ; Property (philosophy) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Klara Bonneau | Aldo S. Pasos‐Trejo | Michael Plainer | Luca Sagresti | Jacopo Venturin | Iryna Zaporozhets | Alessandro Caruso | Edoardo Rolando | Andrea Guljas | Leon Klein | Maximilian Schebek | Filippo Albani | Raquel López‐Ríos de Castro | Zakariya El Machachi | Lorenzo Giambagli | Cecilia Clementi
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them ABSTRACT Molecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery. This article is categorized under: Data Science &gt; Artificial Intelligence/Machine Learning Molecular and Statistical Mechanics &gt; Molecular Mechanics Molecular and Statistical Mechanics &gt; Molecular Dynamics and Monte‐Carlo Methods Deep learning ; Set (abstract data type) ; Field (mathematics) ; Statistical mechanics ; Computational model ; Scientific field Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Yujing Wei | John L. Weber | James M. Stevenson | Zachary K. Goldsmith | Xiaowei Xie | Leif D. Jacobson | Richard A. Friesner
Source: Journal of Chemical Theory and Computation (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

An Accurate Charge-Aware Machine-Learning Interatomic Potential for the Reduction of Li-Ion Battery Electrolytes in Solution Machine learning interatomic potentials (MLIPs), also known as machine learning force fields (MLFFs), offer scalable means of simulating complex systems and processes at ab initio level accuracy. One such process is the critical yet still poorly understood formation of the solid electrolyte interphase (SEI) at the anode of a Li-ion battery (LIB) during the first charge cycle, where electrochemical reduction of the electrolyte leads to the generation of decomposition products. MLIPs are uniquely poised to atomistically describe these electrochemical processes, as they are not as affected by the same limitations in bonding and electron transfer as classical force fields. Nonetheless, training MLIPs to run accurate dynamics of a condensed phase with two different oxidation states, such as in electrochemistry, is challenging for many architectures. In this work, we show that by using MPNICE, a message passing MLIP architecture with iterative charge equilibration, we are able to accurately (within 1 kcal/mol) train models along two potential energy surfaces (reduced and unreduced) for LIB-relevant electrolyte systems. Importantly, we demonstrate strategies for sampling and training to examples of anion radicals of these species, which often are not centered on any atom (off-center radicals, or OCRs). We additionally discuss well-known limitations of global charge equilibration (Qeq) algorithms in erroneously delocalizing charge, and test methods to alleviate the impact on resulting dynamics. Simulations using these models reveal new insights into electrolyte reduction and considerations for the realistic simulation of electron transfer processes in the condensed phase. Electrolyte ; Battery (electricity) ; Reduction (mathematics) ; Charge (physics) ; Electrochemistry ; Molecular dynamics ; Scalability ; Electron transfer ; Anode Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Xinyi Wang | Chaoqi Wang | Miao Yang | Xijie Wang | Yuezhong Zuo | Z Zhang | Yimo Wu | Jingfeng Han | Baojun Li | Yingxu Wei | Limin Ren | Y. G. Wei | Zhongmin Liu | Peng Tian | Zheng Liu
Source: CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

Machine-learning-aided discovery of methanol-to-olefins zeolite catalysts with ultra-high initial selectivity Selectivity ; Zeolite ; Catalysis ; Olefin fiber ; Product distribution ; Workflow Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Evgeny Moerman | Adil Kabylda | Almaz Khabibrakhmanov | Alexandre Tkatchenko
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

MBD-ML: Many-body dispersion from machine learning for molecules and materials Van der Waals (vdW) interactions are essential for describing molecules and materials, from drug design and catalysis to battery applications. These omnipresent interactions must also be accurately included in machine-learned force fields. The many-body dispersion (MBD) method stands out as one of the most accurate and transferable approaches to capture vdW interactions, requiring only atomic $C_6$ coefficients and polarizabilities as input. We present MBD-ML, a pretrained message passing neural network that predicts these atomic properties directly from atomic structures. Through seamless integration with libMBD, our method enables the immediate calculation of MBD-inclusive total energies, forces, and stress tensors. By eliminating the need for intermediate electronic structure calculations, MBD-ML offers a practical and streamlined tool that simplifies the incorporation of state-of-the-art vdW interactions into any electronic structure code, as well as empirical and machine-learned force fields. van der Waals force ; Artificial neural network ; Dispersion (optics) ; London dispersion force ; Molecule ; Electronic structure Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Krystian Mistewicz
Source: Energies (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Machine Learning for the Prediction of Chalcohalide Energy Band Gaps: A Tool for Identifying New Photovoltaic and Photosensitive Materials Chalcohalide compounds have been recognized as outstanding photoabsorbers attractive for use in photocatalysis, solar cells, second harmonic generation, and photodetectors. The reliable experimental band gaps (Eg) of chalcohalides were collected manually from original research papers. The features for machine learning (ML) were generated using information on the chemical composition of chalcohalide material and fundamental properties of chemical elements. The minimum, maximum, and weighted mean of these parameters were used as train data. Different regression models of traditional machine learning and deep learning were trained and cross-validated. The hyperparameter tuning was performed to gain the ML performance. The quality of regression was evaluated by calculating the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The best results (RMSE = 0.47 eV, MAE = 0.34 eV, R2 = 0.71) were obtained for the Extreme Gradient Boosting Regression (XGBR) algorithm. Additional validation was accomplished by predicting the energy band gap values of eight new chalcohalides recently reported in the literature within the last few years. The average relative error of Eg prediction for these compounds attained a low value of 16% in the case of the most efficient XGBR model. The developed ML method can be applied as an alternative way to complex and time-consuming Density Functional Theory (DFT) calculations or expensive experimental measurements. Mean squared error ; Regression ; Hyperparameter ; Boosting (machine learning) ; Harmonic mean ; Photovoltaic system ; Extreme learning machine ; Gradient boosting ; Linear regression Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties