jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aydın Özcan | François‐Xavier Coudert | Sven M. J. Rogge | Greta Heydenrych | Fan Dong | Antonios P. Sarikas | Seda Keskın | Guillaume Maurin | George E. Froudakis | Stefan Wuttke | İlknur Eruçar
Source: Journal of the American Chemical Society (review, 2025-06-24)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research After the development of the famous "Transformer" network architecture and the meteoric rise of artificial intelligence (AI)-powered chatbots, large language models (LLMs) have become an indispensable part of our daily activities. In this rapidly evolving era, "all we need is attention" as Google's famous transformer paper's title [Vaswani et al., <i>Adv. Neural Inf. Process. Syst.</i> <b>2017</b>, 30] implies: We need to focus on and give "attention" to what we have at hand, then consider what we can do further. What can LLMs offer for immediate short-term adaptation? Currently, the most common applications in metal-organic framework (MOF) research include automating literature reviews and data extraction to accelerate the material discovery process. In this perspective, we discuss the latest developments in machine-learning and deep-learning research on MOF materials and reflect on how their utilization has evolved within the LLM domain from this standpoint. We finally explore future benefits to accelerate and automate materials development research. Chemistry ; Metal-organic framework ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Brandon M. Wood | Misko Dzamba | Xiang Fu | Meng Gao | Muhammed Shuaibi | Luis Barroso-Luque | Kareem Abdelmaqsoud | Vahe Gharakhanyan | John R. Kitchin | Daniel S. Levine | Kyle Michel | Anuroop Sriram | Taco Cohen | Abhishek Das | Ammar Rizvi | Sushree Jagriti Sahoo | Zachary W. Ulissi | C. Lawrence Zitnick
Source: ArXiv.org (preprint, 2025-06-30)
Field: Materials Science — Materials Chemistry

UMA: A Family of Universal Models for Atoms The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, Meta FAIR presents a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts. We develop empirical scaling laws to help understand how to increase model capacity alongside dataset size to achieve the best accuracy. The UMA small and medium models utilize a novel architectural design we refer to as mixture of linear experts that enables increasing model capacity without sacrificing speed. For example, UMA-medium has 1.4B parameters but only ~50M active parameters per atomic structure. We evaluate UMA models on a diverse set of applications across multiple domains and find that, remarkably, a single model without any fine-tuning can perform similarly or better than specialized models. We are releasing the UMA code, weights, and associated data to accelerate computational workflows and enable the community to continue to build increasingly capable AI models. Workflow ; Scaling ; Set (abstract data type) ; Scaling law ; Linear scale ; Empirical modelling ; Energy (signal processing) ; Model building Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Xu Liu | Yihan Zhang | Yifan Xie | L. L. Wang | Liyu Gan | Jialei Li | Jiahe Li | Hongli Zhang | Linjiang Chen | Weiwei Shang | Jun Jiang | Gang Zou
Source: Nature Communications (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Design of circularly polarized phosphorescence materials guided by transfer learning It is highly desirable that artificial circularly polarized phosphorescent materials with high luminescence asymmetry factor (g<sub>lum</sub>), narrowband emission and tunable chiral phosphorescent performance can be constructed. Especially, precise control and simultaneous independent switching of circularly polarized fluorescent and phosphorescent performance for the same molecules remain a formidable challenge. Herein, we propose a strategy to customized design of circularly polarized phosphorescent materials based on large language models and transfer learning methods, which not only enables efficient identification of suitable synthesis precursors, but also provides valuable guidance for experimental procedures. We demonstrate the significant advantages of transfer learning with limited chemical data, and precisely fabricate films with high g<sub>lum</sub> (1.86), narrow full-width at half-maximum (49 nm) and customized circularly polarized phosphorescent performance with targeted spectral position. The inverse customization of materials with user-specified circularly polarized fluorescent/phosphorescent performance can be achieved, favoring their application in multicolor display and multidimensional information encryption. Phosphorescence ; Circular polarization ; Materials science ; Physics ; Optics ; Fluorescence Luminescence and Fluorescent Materials ; Perovskite Materials and Applications ; Synthesis and Properties of Aromatic Compounds
Authors: Qiuling Tao | Jinxin Yu | Xiangyu Mu | Xue Jia | Rongpei Shi | Zhifu Yao | Cuiping Wang | Haijun Zhang | Xingjun Liu
Source: Science China Materials (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Machine learning strategies for small sample size in materials science Materials science ; Sample size determination ; Sample (material) ; Nanotechnology ; Engineering physics ; Statistics ; Thermodynamics ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: David A. King | Dong-Jin Kim | Peichen Zhong | Bingqing Cheng
Source: Nature Communications (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Machine learning of charges and long-range interactions from energies and forces Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of material and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and dispersion forces. We recently introduced the Latent Ewald Summation (LES) method, which captures long-range electrostatics without explicitly learning atomic charges or charge equilibration. We benchmark LES on diverse and challenging systems, including charged molecules, ionic liquids, electrolyte solutions, polar dipeptides, surface adsorption, electrolyte/solid interfaces, and solid-solid interfaces. Here we show that LES can reproduce the exact atomic charges for classical systems with fixed charges and can infer dipole and quadrupole moments, as well as the dipole derivative with respect to atomic positions, for quantum mechanical systems. Moreover, LES can achieve better accuracy in energy and force predictions compared to methods that explicitly learn from charges. Electrostatics ; Dipole ; Charge (physics) ; Benchmark (surveying) ; Quadrupole ; Limiting ; Surface (topology) ; Quantum Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Ravikumar Jayabal
Source: Journal of Mechanical Science and Technology (review, 2025-03-01)
Field: Materials Science — Materials Chemistry

Hydrogen energy storage with artificial intelligent-powered strategies for a sustainable future: A review Hydrogen storage ; Energy storage ; Sustainable energy ; Engineering ; Systems engineering ; Computer science ; Hydrogen ; Environmental science ; Renewable energy ; Electrical engineering ; Chemistry ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research
Authors: Hao Zou | Haochen Zhao | Mingming Lu | Jiong Wang | Zeyu Deng | Jianxin Wang
Source: Nature Communications (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration Machine learning offers a promising avenue for expediting the discovery of new compounds by accurately predicting their thermodynamic stability. This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods. However, most existing models are constructed based on specific domain knowledge, potentially introducing biases that impact their performance. Here, we propose a machine learning framework rooted in electron configuration, further enhanced through stack generalization with two additional models grounded in diverse domain knowledge. Experimental results validate the efficacy of our model in accurately predicting the stability of compounds, achieving an Area Under the Curve score of 0.988. Notably, our model demonstrates exceptional efficiency in sample utilization, requiring only one-seventh of the data used by existing models to achieve the same performance. To underscore the versatility of our approach, we present three illustrative examples showcasing its effectiveness in navigating unexplored composition space. We present two case studies to demonstrate that our method can facilitate the exploration of new two-dimensional wide bandgap semiconductors and double perovskite oxides. Validation results from first-principles calculations indicate that our method demonstrates remarkable accuracy in correctly identifying stable compounds. Stability (learning theory) ; Expediting ; Computer science ; Generalization ; Domain (mathematical analysis) ; Machine learning ; Chemical space ; Artificial intelligence ; Resource (disambiguation) ; Chemistry ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Yixin Yu | Mingzhen Zhang | Kelong Fan
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering Nanozymes are a class of nanomaterials that possess catalytic functions similar to those of natural enzymes. Due to their tunable catalytic activity and unique nanoscale properties, these materials exhibit significant potential for applications in biomedical diagnostics, industrial catalysis, and environmental remediation. However, the marked heterogeneity in their catalytic performance and complex multidimensional structure-activity relationships pose challenges to traditional trial-and-error experimental paradigms, which suffer from low efficiency in rational design and prolonged development cycles. With the rapid advancement of artificial intelligence (AI) technologies, nanozyme research is undergoing a transformative shift from empirical exploration to a fourth-generation research paradigm characterized by "data-driven and theory-computing" approaches. Here, the deep integration of machine learning (ML) is reshaping the entire nanozyme research and development workflow, offering new opportunities for rational design and intelligent applications. This review begins by systematically introducing the fundamental classifications and algorithmic principles of ML, elucidating its technical advantages in nanozyme research, and proposing a universal ML-assisted research framework tailored to the unique challenges of nanozyme studies. Through representative case studies, we delve into groundbreaking advancements in the use of ML in predicting catalytic activity, optimizing structures, and enabling smart applications of nanozymes. Finally, we address critical challenges in current ML-assisted nanozyme research-such as data quality and model interpretability-and propose future optimization strategies to advance nanozyme studies toward greater efficiency, precision, and intelligence, aiming to provide novel insights for paradigm innovation in materials science, fostering the evolution of next-generation research methodologies. Serendipity ; Rational design ; Engineering ; Computer science ; Nanotechnology ; Philosophy ; Materials science ; Epistemology Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Genghao Zheng | Shuting Zhang | Yuanwei Wang | Anbo Pan | B.S. Xu | Yantao Xu | Xiaochun Zhang
Source: Composites Part B Engineering (article, 2025-03-08)
Field: Materials Science — Biomaterials

Preparation of a multifunctional bio-based adhesive inspired by the structure of dragonfly wings Materials science ; Adhesive ; Composite material ; Dragonfly ; Polymer science ; Ecology ; Biology Silk-based biomaterials and applications
Authors: Ignacio Rojas Rojas Santelices | Sandra Cano | Fernando Moreira | Álvaro Peña
Source: Sensors (review, 2025-02-28)
Field: Chemistry — Analytical Chemistry

Artificial Vision Systems for Fruit Inspection and Classification: Systematic Literature Review Fruit sorting and quality inspection using computer vision is a key tool to ensure quality and safety in the fruit industry. This study presents a systematic literature review, following the PRISMA methodology, with the aim of identifying different fields of application, typical hardware configurations, and the techniques and algorithms used for fruit sorting. In this study, 56 articles published between 2015 and 2024 were analyzed, selected from relevant databases such as Web of Science and Scopus. The results indicate that the main fields of application include orchards, industrial processing lines, and final consumption points, such as supermarkets and homes, each with specific technical requirements. Regarding hardware, RGB cameras and LED lighting systems predominate in controlled applications, although multispectral cameras are also important in complex applications such as foreign material detection. Processing techniques include traditional algorithms such as Otsu and Sobel for segmentation and deep learning models such as ResNet and VGG, often optimized with transfer learning for classification. This systematic review could provide a basic guide for the development of fruit quality inspection and classification systems in different environments. Computer science ; Multispectral image ; Machine vision ; Artificial intelligence ; Sorting ; Image processing ; RGB color model ; Segmentation ; Machine learning ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Pravin D. Patil | Niharika Gargate | Manishkumar S. Tiwari | Shamraja S. Nadar
Source: Chemical Engineering Journal (article, 2025-02-25)
Field: Chemistry — Inorganic Chemistry

Designable metal–organic frameworks for enzyme immobilization: The reality of controlled architecture Metal-organic framework ; Architecture ; Immobilized enzyme ; Chemistry ; Enzyme ; Chemical engineering ; Combinatorial chemistry ; Engineering ; Organic chemistry ; Adsorption ; Art Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Optimization and Search Problems
Authors: Hasan Eskalen | Mustafa Kavgacı | Hakan Yaykaşlı | Şükrü Özğan | Mehmet Gök | U. Perişanoğlu | E. Kavaz Perişanoğlu
Source: Ceramics International (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Improved physical, optical, and radiation shielding characteristics of SrO-doped borate glasses: Insights from experiments and machine learning Materials science ; Radiation shielding ; Boron ; Electromagnetic shielding ; Doping ; Radiation ; Borate glass ; Nanotechnology ; Engineering physics ; Optoelectronics ; Optics ; Composite material ; Nuclear physics ; Physics Radiation Shielding Materials Analysis ; Nuclear materials and radiation effects ; Luminescence Properties of Advanced Materials
Authors: Begüm Sarac | Seydanur Yücer | Fatih Çiftçi
Source: Small (review, 2025-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MOF‐Based Bioelectronic Supercapacitors Metal-organic frameworks (MOFs) represent a highly promising material class for bioelectronic supercapacitors, characterized by their adjustable structures, extensive surface areas, and superior electrochemical properties. This research explores the synthesis and incorporation of MOF-based materials into bioelectronic devices aimed at energy storage and biosensing applications. The focus is on improving the electrochemical performance of MOFs while preserving their structural integrity through functionalization with biocompatible polymers and conductive materials. The resulting MOF-based bioelectronic supercapacitors exhibit significant improvements in specific capacitance, energy density, and cycling stability. Additionally, the inclusion of bioreceptors allows for the simultaneous detection of biochemical signals alongside energy storage, thus enabling innovative applications in wearable electronics and health monitoring systems. These results suggest that MOF-based supercapacitors have the capacity to fulfill energy storage needs while also advancing bioelectronics by merging energy and sensing capabilities. Supercapacitor ; Bioelectronics ; Nanotechnology ; Energy storage ; Materials science ; Electrochemical energy storage ; Capacitance ; Conductive polymer ; Biosensor ; Electronics ; Wearable technology ; Biocompatible material ; Wearable computer ; Polymer ; Computer science ; Electrode ; Electrical engineering ; Chemistry ; Engineering ; Biomedical engineering ; Embedded system Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; MXene and MAX Phase Materials
Authors: Wei Chen | Chunwen Sun
Source: Energy Materials (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Recent advances in high temperature solid oxide electrolytic cells Solid oxide electrolytic cells (SOECs) with oxygen ion- or proton-conducting electrolytes have received extensive attention in recent years as a kind of energy storage technology. SOECs achieve the synthesis of chemicals such as hydrogen, CO or syngas by electrolyzing water, CO2 or both at high temperatures. This review presents the basic structure and electrochemical principle of SOECs, then introduces the recent research progress of cathodes, anodes and electrolytes in SOECs, and particularly points out the current challenges of SOEC materials, such as inactivation at high temperatures and decay due to long-term operation. We summarize various strategies to improve the properties of different electrode materials, including doping, in situ exsolution and microstructure modification. Moreover, the advantages and disadvantages of different SOEC stack structures (planar and tubular) are also outlined. Finally, the future development trends in novel materials and engineering design of SOECs are proposed. Oxide ; Electrolyte ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Engineering ; Electrode Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Magnetic and transport properties of perovskites and related materials
Authors: S. Harish | P. Uma Sathyakam
Source: Journal of Electronic Materials (article, 2025-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dunn’s Method for Distinguishing Charge Storage Mechanisms in Supercapacitors: A Status Quo Review Supercapacitor ; Capacitance ; Energy storage ; Cyclic voltammetry ; Capacitive sensing ; Dielectric spectroscopy ; Status quo ; Electrical impedance Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Ziyi Zhao | Jiayi Cao | Boyu Zhu | Xinru Li | Lin Zhou | Bin Su
Source: Biosensors (review, 2025-02-13)
Field: Materials Science — Materials Chemistry

Recent Advances in MXene-Based Electrochemical Sensors MXene is a new family of two-dimensional nanomaterials with outstanding electrical conductivity, tunable structure, biocompatibility, and a large surface area. Thanks to these unique physicochemical properties, MXene has been used for constructing electrochemical sensors (MECSens) with excellent performance. In particular, the abundant surface termination of MXene can contribute to greatly enhancing the analytical sensitivity and selectivity of MECSens. Recently, MECSens have been widely applied in many fields including clinical diagnosis, infectious disease surveillance, and food security. However, not all MXene materials are suitable for building electrochemical sensors. In this article, we present an overview of different MECSens that have been developed so far. We begin with a short summary of the preparation and characterization of MECSens. Subsequently, the electrochemical performance, detection strategies, and application scenarios of MECSens are classified and briefly discussed. The article ends with a short conclusion and future perspectives. We hope this article will be helpful for designing and constructing MECSens with outstanding activity for electrochemical analysis. Nanotechnology ; Nanomaterials ; Computer science ; Biocompatibility ; Electrochemistry ; Materials science ; Characterization (materials science) ; Chemistry MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; 2D Materials and Applications
Authors: Qingbai Tian | Xing Xu | Xiaoguang Duan
Source: Environmental Science & Technology (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Application-Oriented Advanced Oxidation Processes: Research Priorities for Upscaling and Deployment Software deployment ; Computer science ; Environmental science ; Process management ; Business ; Software engineering Catalytic Processes in Materials Science ; Advanced oxidation water treatment ; Environmental remediation with nanomaterials
Authors: Chunyu Du | Yue Hu | Xiao Xiao | Farid Manshaii | Lirong Liang | Jun Chen | Guangming Chen
Source: Nano-Micro Letters (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

High-Reliability Thermoreceptors with Minimal Temporal and Spatial Variations Through Photo-Induced Patterning Thermoelectrics The development of bionic sensing devices with advanced physiological functionalities has attracted significant attention in flexible electronics. In this study, we innovatively develop an air-stable photo-induced n-type dopant and a sophisticated photo-induced patterning technology to construct high-resolution joint-free p-n integrated thermoelectric devices. The exceptional stability of the photo-induced n-type dopant, combined with our meticulously engineered joint-free device architecture, results in extremely low temporal and spatial variations. These minimized variations, coupled with superior linearity, position our devices as viable candidates for artificial thermoreceptors capable of sensing external thermal noxious stimuli. By integrating them into a robotic arm with a pain perception system, we demonstrate accurate pain responses to external thermal stimuli. The system accurately discerns pain levels and initiates appropriate protective actions across varying intensities. Our findings present a novel strategy for constructing high-resolution thermoelectric sensing devices toward precise biomimetic thermoreceptors. Thermoreceptor ; Reliability (semiconductor) ; Thermoelectric materials ; Materials science ; Thermoelectric effect ; Neuroscience ; Biology ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing