jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siya Zhu | ‪Doğuhan Sarıtürk | Raymundo Arróyave
Source: Acta Materialia (article, 2025-01-22)
Field: Materials Science — Materials Chemistry

Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: Opportunities and challenges CALPHAD ; Materials science ; Phase diagram ; Phase (matter) ; Diagram ; Thermodynamics ; Systems engineering ; Metallurgy ; Computer science ; Engineering Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Brook Wander | Muhammed Shuaibi | John R. Kitchin | Zachary W. Ulissi | C. Lawrence Zitnick
Source: ACS Catalysis (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

CatTSunami: Accelerating Transition State Energy Calculations with Pretrained Graph Neural Networks Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 data set, a related but different task, is able to find transition states energetically similar (within 0.1 eV) to density functional theory (DFT) 91% of the time with a 28× speedup. This speaks to the generalizability of the models, having never been explicitly trained on reactions, the machine learned potential approximates the potential energy surface well enough to be performant for this auxiliary task. We introduce the Open Catalyst 2020 Nudged Elastic Band (OC20NEB) data set, which is made of 932 DFT nudged elastic band calculations, to benchmark machine learned model performance on transition state energies. To demonstrate the efficacy of this approach, we for the first time explicitly treated a large reaction network with 61 intermediates and 174 dissociation reactions at DFT resolution (40 meV). To find low energy transition states we densely enumerate many possible NEBs. Using DFT this would have taken 52 GPU years. With ML we realized a 1500× speedup for dense enumerations, using just 12 GPU days of compute. Similar searches for complete reaction networks could become routine using the approach presented here. Finally, we constructed an ammonia synthesis activity volcano and systematically found lower energy configurations of the transition states and intermediates on six stepped unary surfaces than had previously been reported. This scalable approach offers a more complete treatment of configurational space to improve and accelerate catalyst discovery. Artificial neural network ; Computer science ; State (computer science) ; Graph ; Catalysis ; Transition (genetics) ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Organic chemistry Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced Chemical Physics Studies
Authors: Jacob Wekalao | Ahmed Mehaney | Nassir Saad Alarifi | Mostafa R. Abukhadra | Hussein A. Elsayed
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2025-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-sensitivity graphene-gold metasurface optical biosensor for early melanoma detection optimized with machine learning using a one-dimensional convolutional neural network and binary encoding Convolutional neural network ; Sensitivity (control systems) ; Biosensor ; Encoding (memory) ; Computer science ; Graphene ; Binary number ; Materials science ; Artificial neural network ; Artificial intelligence ; Nanotechnology ; Pattern recognition (psychology) ; Optoelectronics ; Electronic engineering ; Mathematics ; Engineering ; Arithmetic Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Communication and Nanonetworks ; Advanced biosensing and bioanalysis techniques
Authors: Florian A. Rosenberger | Sophia C. Mädler | Katrine Holtz Thorhauge | Sophia Steigerwald | Malin Fromme | Mikhail Lebedev | Caroline A M Weiss | Marc Oeller | Maria Wahle | Andreas Metousis | Maximilian Zwiebel | Niklas A. Schmacke | Sönke Detlefsen | Peter Boor | Ondřej Fabián | Soňa Fraňková | Aleksander Krag | Pavel Strnad | Matthias Mann
Source: Nature (article, 2025-04-16)
Field: Chemistry — Spectroscopy

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood<sup>1-3</sup>. We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis<sup>4,5</sup>, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue. Proteomics ; Proteome ; Computational biology ; Biology ; Phenotype ; Transcriptome ; Cell ; Bioinformatics ; Genetics ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Endoplasmic Reticulum Stress and Disease ; Ubiquitin and proteasome pathways
Authors: Kuaibing Wang | Ruiying Fu | Chao Li | Xutian Yang | Qichun Zhang | Mingjun Ouyang
Source: Angewandte Chemie International Edition (review, 2025-07-18)
Field: Materials Science — Materials Chemistry

Metal‐Covalent Organic Frameworks: Design Strategy, Structure Feature, and Applications in Energy Storage Metal-covalent organic frameworks (MCOFs), which can integrate the properties of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), exhibit high stability, adjustable pore structures, and catalytic activity of metal sites owing to the synergistic interaction between metal sites and covalent backbones. In this regard, MCOFs have gained significant attention as promising electrode materials, where metal ions (M<sup>n+</sup>) function as molecular structure switches, providing MCOFs with diverse active sites and modifying their charge density by incorporating different M<sup>n+</sup>, thereby imparting unique energy-storage properties to MCOFs. Furthermore, by optimizing the synthesis strategies of MCOFs, their topological and dimensional structures can be regulated to ensure the stability of the MCOFs. In the challenging landscape of energy storage, MCOFs have surpassed the performance limitations of traditional COFs. Through precise atomic-level control of metal sites and innovative design of dynamic covalent chemistry, they can significantly enhance the performance of batteries, achieving remarkable performance in lithium-ion batteries (LIBs), lithium-sulfur batteries, and other applications. This review systematically summarizes the research advancements of MCOFs in high-performance energy storage devices, including lithium-ion, Li-CO<sub>2</sub>, and Zn-ion batteries. In addition, it examines the synthesis strategies, structural regulation, and structural characteristics of MCOFs to address the challenges encountered in various energy storage devices. Covalent bond ; Energy storage ; Nanotechnology ; Lithium (medication) ; Materials science ; Metal ; Covalent organic framework ; Chemistry ; Organic chemistry ; Physics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced battery technologies research
Authors: Abu Saad Ansari | Griszha Azzahra | Fairuz Gianirfan Nugroho | Momin M. Mujtaba | Abu Talha Aqueel Ahmed
Source: Catalysts (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Oxides and Metal Oxide/Carbon Hybrid Materials for Efficient Photocatalytic Organic Pollutant Removal Pharmaceuticals are increasingly significant contaminants in the environmental ecosystem, prompting the exploration of photocatalysis as a promising method for removing their pollutants. However, the application of semiconductor metal oxides as photocatalysts has been limited by issues such as rapid photocarrier recombination and high band gap energy. One emerging strategy to enhance the photocatalytic performance of metal oxides involves integrating them with carbon dots, which offer advantages including low toxicity, aqueous stability, increased surface area, cost effectiveness, biocompatibility, and chemical inertness. In this study, we conducted a critical review focusing on the nanocomposite development of metal oxide/carbon dots for the photocatalytic removal of pharmaceutical pollutants. Our study highlights that carbon dots can significantly enhance the photocatalytic efficiency of these metal oxides as photocatalytic materials by improving the adsorption of organic pollutants and enhancing light absorption in the visible spectrum. This review aims to provide insights for future research aimed at advancing the development of enhanced photocatalytic metal oxide/carbon dot nanocomposites. Photocatalysis ; Materials science ; Nanocomposite ; Oxide ; Adsorption ; Pollutant ; Nanotechnology ; Carbon fibers ; Chemical engineering ; Chemistry ; Catalysis ; Metallurgy ; Composite material ; Organic chemistry ; Composite number Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Wolfgang Wagermaier | Khashayar Razghandi | Peter Fratzl
Source: Advanced Materials (article, 2025-01-05)
Field: Materials Science — Biomaterials

A Bio‐Inspired Perspective on Materials Sustainability The article explores materials sustainability through a bio-inspired lens and discusses paradigms that can reshape the understanding of material synthesis, processing, and usage. It addresses various technological fields, from structural engineering to healthcare, and emphasizes natural material cycles as a blueprint for efficient recycling and reuse. The study shows that material functionality depends on both chemical composition and structural modifications, which emphasizes the role of material processing. The article identifies strategies such as mono-materiality and multifunctionality, and explores how responsivity, adaptivity, modularity, and cellularity can simplify material assembly and disassembly. Bioinspired strategies for reusing materials, defect tolerance, maintenance, remodeling, and healing may extend product lifespans. The principles of circularity, longevity, and parsimony are reconsidered in the context of "active materiality", a dynamic bio-inspired paradigm. This concept expands the traditional focus of material science from structure-function relationships to include the development of materials capable of responding or adapting to external stimuli. Concrete examples demonstrate how bio-inspired strategies are being applied in engineering and technology to enhance the sustainability of materials. The article concludes by emphasizing interdisciplinary collaboration as a key factor for developing a sustainable and resilient materials economy in harmony with nature's material cycles. Materials science ; Perspective (graphical) ; Sustainability ; Nanotechnology ; Engineering ethics ; Biochemical engineering ; Artificial intelligence ; Computer science ; Engineering ; Ecology ; Biology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Chemistry and Chemical Engineering
Authors: Zhilin Liu | Mingxiu Liu | Liujian Qi | Nan Zhang | Bin Wang | Xiaojuan Sun | Rongjun Zhang | Dabing Li | Shaojuan Li
Source: Light Science & Applications (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Versatile on-chip polarization-sensitive detection system for optical communication and artificial vision Polarization is an important attribute of light and can be artificially modulated as a versatile information carrier. Conventional polarization-sensitive photodetection relies on a combination of polarizing optical elements and standard photodetectors, which requires a substantial amount of space and manufacturing expenses. Although on-chip polarized photodetectors have been realized in recent years based on two-dimensional (2D) materials with low-symmetry crystal structures, they are limited by the intrinsic anisotropic property and thus the optional range of materials, the operation wavelength, and more importantly, the low anisotropic ratio, hindering their practical applications. In this work, we construct a versatile platform that transcends the constraints of material anisotropy, by integrating WSe<sub>2</sub>-based photodetector with MoS<sub>2</sub>-based field-effect transistor, delivering high-performance broadband polarization detection capability with orders of magnitude improvement in anisotropic ratio and on/off ratio. The polarization arises from hot electron injection caused by the plasmonic metal electrode and is amplified by the transistor to raise the anisotropic ratio from 2 to an impressive value over 60 in the infrared (IR) band, reaching the level of existing applications. Meanwhile, the system achieves a significant improvement in photosensitivity, with an on/off ratio of over 10<sup>3</sup> in the IR band. Based on the above performance optimization, we demonstrated its polarization-modulated IR optical communication ability and polarized artificial vision applications with a high image recognition accuracy of ~99%. The proposed platform provides a promising route for the development of the long-sought minimized, high-performance, multifunctional optoelectronic systems. Photodetector ; Photodetection ; Optoelectronics ; Polarization (electrochemistry) ; Materials science ; Extinction ratio ; Plasmon ; Optical communication ; Anisotropy ; Optics ; Transistor ; Computer science ; Wavelength ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Transition Metal Oxide Nanomaterials
Authors: Francisco Javier Alfosea-Simón | L. Burgos | Nuria Alburquerque
Source: Plants (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

Silver Nanoparticles Help Plants Grow, Alleviate Stresses, and Fight Against Pathogens The use of silver nanoparticles (AgNPs) has gained importance in agriculture in recent years thanks to their unique characteristics, including their antimicrobial capacity and their ability to promote plant growth. Due to these attributes, AgNPs are considered a promising solution for the future of agriculture, offering significant potential to address the challenges the sector confronts currently. However, it is important to adjust the application conditions, depending on the target and the crop used, to improve AgNP treatment efficiency. This review compiles recent advances in the use of AgNPs for crop production, both in and ex vitro. AgNPs promote growth and alleviate biotic and abiotic stresses through different ex vitro application methods. They are also efficiently used in vitro to improve plant culture and pathogen elimination. In addition, the safety and toxicity associated with their use are discussed. AgNPs are a novel tool with great potential for the agricultural sector, but it is still necessary to continue researching the mechanisms of AgNP action in order to optimize their application in each specific case. Silver nanoparticle ; Agriculture ; Plant growth ; Biotechnology ; Abiotic component ; Nanotechnology ; Biochemical engineering ; Biology ; Nanoparticle ; Engineering ; Materials science ; Ecology ; Agronomy Nanoparticles: synthesis and applications
Authors: Fan Li | Taisong Pan | Weihan Li | Zujun Peng | Dengji Guo | Xiang Jia | Taiqi Hu | Lingxiao Wang | Wei Wang | Min Gao | Guang Yao | Le Zuo | Mei Bi | Xiaolong Weng | Wenxuan Tang | Yuan Lin
Source: Nature Communications (article, 2025-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible intelligent microwave metasurface with shape-guided adaptive programming Empowering the reconfigurable metasurfaces (RM) with the capability to be mechanically deformable highlights the possibility to manipulate the electromagnetic (EM) wave across arbitrary surfaces. Such ambition is hampered by the absence of adaptivity to shape variation in current programming strategies for RM. Herein, we present a flexible intelligent surface platform (FISP) as a solution to achieve flexible RM with highly stable performance under dynamic deformation. The geometry acquisition module in FISP enables real-time awareness of RM's deformation with the conformal sensor array. By merging the actual shape of flexible RM into the input of the adaptive algorithm driven by the artificial neural network, the deformed flexible RM in FISP can be autonomously encoded by the bias voltage supply module to ensure robust performance under various deformation conditions. The versatility of FISP in manipulating EM waves is demonstrated by its applications in electromagnetic illusion, carpet cloaking, and data transmission, illustrating the prospects for seamlessly integrating flexible electronics and RM in the development of future EM metasurfaces. Cloaking ; Computer science ; Tensegrity ; Deformation (meteorology) ; Cloak ; Conformal map ; Topology (electrical circuits) ; Metamaterial ; Acoustics ; Physics ; Electrical engineering ; Optics ; Geometry ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Taha Sheheryar | Ye Tian | Bo Lv | Xiuqin Chu | Jinhui Shi
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A graphene-based tunable polarization insensitive terahertz metasurface absorber for multi-band high-efficiency applications A graphene-based polarization insensitive tunable terahertz absorber achieving eight distinct absorption peaks across 3.9–9.73 THz with 99.3% average absorption for advanced THz applications. Materials science ; Terahertz radiation ; Optoelectronics ; Graphene ; Polarization (electrochemistry) ; Terahertz metamaterials ; Optics ; Nanotechnology ; Far-infrared laser ; Laser Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Bo Wen | Jack Freestone | Michael Riffle | Michael J. MacCoss | William Stafford Noble | Uri Keich
Source: Nature Methods (article, 2025-06-16)
Field: Chemistry — Spectroscopy

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment A critical challenge in mass spectrometry proteomics is accurately assessing error control, especially given that software tools employ distinct methods for reporting errors. Many tools are closed-source and poorly documented, leading to inconsistent validation strategies. Here we identify three prevalent methods for validating false discovery rate (FDR) control: one invalid, one providing only a lower bound, and one valid but under-powered. The result is that the proteomics community has limited insight into actual FDR control effectiveness, especially for data-independent acquisition (DIA) analyses. We propose a theoretical framework for entrapment experiments, allowing us to rigorously characterize different approaches. Moreover, we introduce a more powerful evaluation method and apply it alongside existing techniques to assess existing tools. We first validate our analysis in the better-understood data-dependent acquisition setup, and then, we analyze DIA data, where we find that no DIA search tool consistently controls the FDR, with particularly poor performance on single-cell datasets. False discovery rate ; Computer science ; Data mining ; Proteomics ; Tandem mass spectrometry ; Software ; Mass spectrometry ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Preety Yadav | Neeraj Dhariwal | Amit Sanger | Sung Bum Kang | Vinod Kumar
Source: Nano Energy (review, 2025-01-27)
Field: Materials Science — Materials Chemistry

A review unveiling recent advances in the flexible-wearable futuristic thermoelectric device Materials science ; Wearable computer ; Thermoelectric effect ; Wearable technology ; Nanotechnology ; Engineering physics ; Thermoelectric materials ; Systems engineering ; Engineering ; Embedded system ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Pavithra Baskaran | Mani Rajasekar
Source: Chemical Engineering Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recent progress in thermoelectric devices and applications Thermoelectric effect ; Nanotechnology ; Materials science ; Thermoelectric materials ; Engineering physics ; Process engineering ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Xuefeng Bai | Song He | Yi Li | Ya-Bo Xie | Xin Zhang | Wenli Du | Jian‐Rong Li
Source: npj Computational Materials (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Construction of a knowledge graph for framework material enabled by large language models and its application Framework materials (FMs) have been extensively investigated with a plethora of literature documenting their unique properties and potential applications. Despite this, a comprehensive knowledge graph for this emerging field has not yet been constructed. In this study, by utilizing the natural language processing capabilities of large language models (LLMs), we have established a comprehensive knowledge graph (KG-FM). It covers synthesis, properties, applications, and other aspects of FMs including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs). The knowledge graph was constructed through the analysis of over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships. Subsequently, its application has been explored for enhancing data retrieval, mining, and the development of sophisticated question-answering systems. Especially when integrating the KGs with LLMs, resulted Qwen2-KG not only achieves a higher accuracy rate of 91.67% in question-answering than existing models but also provides precise information sources. Computer science ; Graph ; Knowledge graph ; Natural language processing ; Programming language ; Artificial intelligence ; Theoretical computer science Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Graph Neural Networks