jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinglang Zhang | Jiaxin Li | Guanhua Zhao | Qilong Wang | Yu‐Guo Guo | Chunpeng Yang
Source: Journal of the American Chemical Society (article, 2025-10-24)
Field: Chemistry — Inorganic Chemistry

Mining Solid-State Electrolytes from Metal–Organic Framework Databases through Large Language Models and Representation Clustering Metal-organic frameworks (MOFs) are attracting increasing attention as solid-state electrolytes (SSEs) due to their three-dimensional porous diffusion paths for Li<sup>+</sup> migration. However, their development is hindered by their inherent complexity and the absence of design guidelines. Large language models (LLMs) and machine learning, as emerging artificial intelligence (AI) technologies, can significantly accelerate the development of MOF SSEs by analyzing data and identifying potential materials. Herein, we use LLMs and representation clustering to intelligently mine MOF SSEs from 11,393 candidate MOF materials, subsequently verified by physicochemical characterizations and electrochemical demonstration. Specifically, we adopt an interactive iteration text mining framework based on LLMs to extract information on MOF SSEs, constructing a specialized data set for structural and electrochemical properties of MOF SSEs, with high precision and recall. Each property is projected into a representation space, and representation clustering is performed on samples to mine promising MOF SSEs from a candidate MOF data set. As a typical result, NOTT-400 is successfully identified through the clustering analysis, exhibiting high Li<sup>+</sup> conductivity (2.23 × 10<sup>-4</sup> S cm<sup>-1</sup>) and a wide electrochemical stability window (0-4.79 V), confirming both the material feasibility and the reliability of the entire AI-driven approach. The AI-assisted mining of novel MOF SSEs, along with their design principles, creates a new paradigm for accelerating materials discovery. Cluster analysis ; Representation (politics) ; Property (philosophy) ; Reliability (semiconductor) ; Set (abstract data type) ; Stability (learning theory) ; External Data Representation ; Data modeling Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Lucas W. Souza | Nathan D. Ricke | Braden C. Chaffin | Mike Fortunato | Shutian Jiang | Cihan Soylu | Thomas C. Caya | Sii Hong Lau | Karlheinz Wieser | Abigail G. Doyle | Kian L. Tan
Source: Journal of the American Chemical Society (article, 2025-05-22)
Field: Chemistry — Organic Chemistry

Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides When developing machine learning models for yield prediction, the two main challenges are effectively exploring condition space and substrate space. In this article, we disclose an approach for mapping the substrate space for Ni/photoredox-catalyzed cross-electrophile coupling of alkyl bromides and aryl bromides in a high-throughput experimentation (HTE) context. This model employs active learning (in particular, uncertainty querying) as a strategy to rapidly construct a yield model. Given the vastness of substrate space, we focused on an approach that builds an initial model and then uses a minimal data set to expand into new chemical spaces. In particular, we built a model for a virtual space of 22,240 compounds using less than 400 data points. We demonstrated that the model can be expanded to 33,312 compounds by adding information around 24 building blocks (<100 additional reactions). Comparing the active learning-based model to one constructed on randomly selected data showed that the active learning model was significantly better at predicting which reactions will be successful. A combination of density function theory (DFT) and difference Morgan fingerprints was employed to construct the random forest model. Feature importance analysis indicates that key DFT features that are related to the reaction mechanism (e.g., alkyl radical LUMO energy) were crucial for model performance and predictions on aryl bromides outside the training set. We anticipate that combining DFT featurization and uncertainty-based querying will help the synthetic organic community build predictive models in a data-efficient manner for other chemical reactions that feature large and diverse scopes. Chemistry ; Aryl ; Electrophile ; Alkyl ; Coupling (piping) ; Photochemistry ; Computational chemistry ; Organic chemistry ; Catalysis Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods
Authors: Yangjie Liu | Yao Guo | Yu Zhang | Lihong Xu | Junxiang Chen | Xiang Hu | Zhenhai Wen
Source: Angewandte Chemie International Edition (article, 2025-06-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orchestrating Penta‐Element Interplay in Gradient‐Porous Carbons for Advanced Sodium‐Ion Hybrid Capacitors Sodium-ion hybrid capacitors (SIHCs) offer a cutting-edge synergy between battery-level energy density and supercapacitor-like power density, yet face critical challenges in balancing the kinetic and capacity mismatch between Faradaic anodes and capacitive cathodes. Herein, we present a penta-element doped gradient-porous carbon (PE-GPC) with a nanosphere architecture, engineered with high-entropy principles and a gradual pore density variation to enhance mass transport and charge storage. Operando spectroscopy and machine learning potentials unveil a concerted penta-element interplay: thiophene-like S configurations mediate dynamic redox processes, enabling pseudocapacitive Na⁺ and anion storage, while fluorine functionalities foster a self-rejuvenating NaF-rich solid electrolyte interphase (SEI), stabilizing long-term cycling. Meanwhile, the synergistic N/B/P triad engineers a hierarchical defect network that enhances electronic conductivity and fine-tunes ion adsorption energetics. This orchestrated interplay empowers the SIHC full cell with a high energy density of 196 Wh kg<sup>-1</sup>, a formidable power density (10.4 kW kg<sup>-1</sup>), and an impressive 88.2% capacity retention after 9000 cycles. By establishing a high-entropy stabilization paradigm, this work paves the way for multi-ion storage architectures, offering a universal strategy to bridge the charge-transfer imbalance in advanced energy devices. Capacitor ; Sodium ; Porosity ; Element (criminal law) ; Ion ; Materials science ; Chemical engineering ; Chemistry ; Composite material ; Electrical engineering ; Engineering ; Voltage ; Organic chemistry ; Metallurgy ; Political science Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Linxue Zhen | Zhichun Bi | Xinyue Zhang | Peize Jin | Donghui Song | Hui Huang | Yongxin Li
Source: Talanta (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

A novel sensor array combined with machine learning for the effective differentiation of multiple classes of antibiotics based on copper-based nanozymes with laccase-like activity Chemistry ; Laccase ; Copper ; Combinatorial chemistry ; Nanotechnology ; Biochemistry ; Enzyme ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Jichao Fan | Ruiyang Chen | Minhan Lou | Haoyu Xie | Nina Hong | Benjamin Hillam | Jacques Doumani | Yingheng Tang | Weilu Gao
Source: Nature Communications (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

A Programmable Wafer-scale Chiroptical Heterostructure of Twisted Aligned Carbon Nanotubes and Phase Change Materials The ability to design and dynamically control chiroptical responses in solid-state matter at a wafer scale enables new opportunities in various areas. Here, we present a full stack of computer-aided designs and experimental implementations of a dynamically programmable, unified, scalable chiroptical heterostructure containing wafer-scale twisted aligned one-dimensional carbon nanotubes and non-volatile phase change materials. We develop a software infrastructure based on high-performance machine learning frameworks, including differentiable programming and derivative-free optimization, to efficiently optimize the tunability of both reciprocal and nonreciprocal circular dichroism responses, which are experimentally validated. Further, we demonstrate the heterostructure scalability regarding stacking layers and the dual roles of aligned carbon nanotubes - the layer to produce chiroptical responses and the Joule heating electrode to electrically program phase change materials. This heterostructure platform is versatile and expandable to a library of one-dimensional nanomaterials, phase change materials, and electro-optic materials for exploring novel chiral phenomena and photonic and optoelectronic devices. Materials science ; Heterojunction ; Carbon nanotube ; Wafer ; Photonics ; Nanotechnology ; Stacking ; Optoelectronics ; Joule heating ; Stack (abstract data type) ; Joule (programming language) ; Computer science ; Electrical engineering ; Physics Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Synthesis and Properties of Aromatic Compounds
Authors: Abdullah Tahir | Yuhao Wang | Jingwei Li | Yunfeng Tian | Francesco Ciucci
Source: Advanced Functional Materials (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of High‐Performance PCFC Cathodes: Computational‐Experimental Optimization of Cobalt‐Substituted Ba<sub>0.95</sub>La<sub>0.05</sub>FeO<sub>3‐δ</sub> Abstract Developing high‐performance, cost‐effective protonic ceramic fuel cell (PCFC) cathodes involves navigating complex compositional landscapes to optimize multiple competing properties. This study presents a novel integrated methodology that combines computational screening with machine learning potentials, targeted experimental validation, and Bayesian optimization to accelerate the design of Co‐substituted Ba 0.95 La 0.05 FeO 3‐δ (BLF) cathodes. Utilizing Bayesian optimization, BLFC15 (15% Co) is identified as the optimal composition, achieving a 58% reduction in area‐specific resistance at 500 °C compared to pristine BLF. This integrated approach, which includes mechanistic studies through density functional theory (DFT) calculations and experimental characterization, illustrates the effectiveness of computational screening and Bayesian optimization in accelerating materials discovery and efficiently optimizing complex material properties for enhanced PCFC cathode performance. Materials science ; Cobalt ; Cathode ; Metallurgy ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Yian Liu | Qingfubo Geng | Wenfeng Zhan | Zhaoxin Geng
Source: Engineering Applications of Artificial Intelligence (article, 2025-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A cascaded deep neural network for design and verification of surface lattice resonance metasurfaces biosensors Computer science ; Artificial neural network ; Biosensor ; Lattice (music) ; Surface (topology) ; Artificial intelligence ; Acoustics ; Nanotechnology ; Materials science ; Physics ; Geometry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Molecular Communication and Nanonetworks
Authors: Sida Sun | Shigeo Maruyama | Yan Li
Source: Journal of the American Chemical Society (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFFs), which can study the interface dynamics under near ab initio accuracy, provide a powerful technique to reveal the formation mechanism of SWCNTs. Here, we develop a cobalt-carbon MLFF and perform growth simulations on a cobalt catalyst to investigate the chirality preference of the growth of SWCNTs under the vapor-liquid-solid (VLS) regime. Through microkinetic modeling, we reproduce the observed growth and defect kinetics, demonstrating their dependence on the chirality. It is observed that while the initial chirality assignment is likely related to the configurational degeneracy of the nanotube caps, pentagon defects immediately form and resolve after nucleation. Such processes, which we name diameter control mechanisms, not only control the diameter toward an optimum but also shift the chirality distribution drastically. Our simulation shows a preference toward the (6,5) chirality, which is also widely observed experimentally. Our work offers a microkinetic modeling workflow for the chirality-dependent kinetics of the SWCNTs, highlighting the important contribution of the defect kinetics to the chirality origination. Chemistry ; Chirality (physics) ; Carbon nanotube ; Kinetics ; Nanotechnology ; Chemical physics ; Classical mechanics ; Physics ; Quantum mechanics Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Sourav Bej | Eun‐Bum Cho
Source: Environmental Research (review, 2025-01-28)
Field: Chemistry — Spectroscopy

State-of-the-art progress and prospect of metal-organic frameworks and composites for photoelectrochemical amino-drugs sensing Metal-organic framework ; Materials science ; Metal ; Composite material ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Adsorption Molecular Sensors and Ion Detection ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Mainak Das | Wan Sieng Yeo | Agus Saptoro
Source: Vibrational Spectroscopy (review, 2025-06-10)
Field: Chemistry — Analytical Chemistry

A review of machine learning in hyperspectral imaging for food safety Manual detection methods such as human visual inspection are not quantitative and could lead to inconsistencies in food safety assessments. Conversely, traditional laboratory techniques offer quantitative assessments, but they involve expensive equipment, are time-consuming, and are destructive to the samples. To address these limitations, advances in non-destructive monitoring techniques with the implementation of machine learning (ML) algorithms can be alternative solutions. For instance, hyperspectral imaging technology, which combines spatial and spectral data to acquire a data-rich hypercube, can be integrated with ML models to assess food safety without damaging the samples. Different from the existing review studies on ML models, this review domain focuses more on staple foods and how these ML algorithms can quantify the chemical constituents in staple food sources. This study aims to differentiate the various ML models employed in food safety and discusses the challenges and future directions for effectively quantifying samples like adulterants in foods to ensure food safety. In addition, a bibliometric analysis of ML algorithms was also conducted to understand the research trends in hyperspectral imaging and ML. Besides, this review study also addresses different image-sensing technologies and contributes to research pursuing ML and deep learning for food safety purposes in agriculture. • Vis-NIR, NIR, and SWIR are the most common HSI spectral ranges for staple foods. • Pairing ML and DL with HSI technology has been used in the staple food industry. • PCA, PLS, SVM, and ANN are common ML models, whereas CNN is a common DL approach. • DL handled complex patterns and large datasets but came with computational load. • Upon integrating kernel functions, ML models work better with non-linear datasets. Hyperspectral imaging ; Food safety ; Computer science ; Artificial intelligence ; Computer vision ; Pattern recognition (psychology) ; Environmental science ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Currency Recognition and Detection
Authors: Zhiyao Lü | Yun Fan | Zhaoxu Sun | Xiaodong He | Chuchu Yang | Hang Yin | Jinze Zhang | Guangping Song | Yongting Zheng | Yuelei Bai
Source: Journal of Advanced Ceramics (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti <sub>2</sub>SnN To explore the MAX phase with experimental value over a wider range, a data-driven machine learning (ML) model was trained to rapidly predict the stability of MAX phases via a random forest classifier (RFC), support vector machine (SVM), and gradient boosting tree (GBT), where the deemed significant descriptors were compiled from the literature and the stability of 1804 combinations of MAX phases was collected. Using this well-trained model, 190 new MAX phases were screened from 4347 MAX phases, 150 of which met the criteria for thermodynamic and intrinsic stability on the basis of first-principles calculations. Additionally, with the help of the ML model, the mean number of valence electrons and the valence electron deviation are the two most critical factors influencing stability. Additionally, one of these predicted MAX phases, Ti₂SnN, was experimentally synthesized through Lewis acid substitution reactions at 750 °C, with interesting A-site deintercalation and self-extrusion. First-principles calculations revealed that Ti₂SnN has lower elastic properties, higher damage tolerance and fracture toughness, and a higher coefficient of thermal expansion (CTE). Stability (learning theory) ; Composition (language) ; Materials science ; Artificial intelligence ; Machine learning ; Computer science MXene and MAX Phase Materials ; Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science
Authors: Siu Ting Tai | Chen Wang | Ruihuan Cheng | Yue Chen
Source: Journal of Chemical Theory and Computation (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Revisiting Many-Body Interaction Heat Current and Thermal Conductivity Calculations Using the Moment Tensor Potential/LAMMPS Interface The definition of heat current operator for systems for nonpairwise additive interactions and its impact on related lattice thermal conductivity (κ<sub><i>L</i></sub>) via molecular dynamics (MD) simulation are ambiguous and controversial when migrating from empirical potential models to machine learning potential (MLP) models. Herein, we study and compare the significance of many-body interaction with heat current computation in one of the most popular MLP models, the moment tensor potential (MTP). Nonequilibrium MD simulations and equilibrium MD simulations among four different materials were performed, and inconsistencies in energy conservation between the simulation thermostat and the pairwise calculator were found. A new virial stress tensor expression with a many-body heat current description was integrated inside the MTP, and we uncovered the influence of the modification that could alter the κ<sub><i>L</i></sub> results by 29-64% using the equilibrium MD computational approach. Our work demonstrates the importance of a many-body description during thermal analysis in MD simulations when MLPs are of concern. Moment (physics) ; Thermal conductivity ; Interface (matter) ; Current (fluid) ; Tensor (intrinsic definition) ; Conductivity ; Computer science ; Materials science ; Thermodynamics ; Physics ; Chemistry ; Physical chemistry ; Mathematics ; Classical mechanics ; Geometry Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Muhammet Kayfeci | Fevzi Bedir | Osman Özkaraca | Aleksandar Georgiev | Ali Keçebaş
Source: Applied Thermal Engineering (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Enhancing hydrogen storage efficiency in LaNi4.75Al0.25-based metal hydride reactors using advanced machine learning techniques Hydride ; Hydrogen storage ; Hydrogen ; Nuclear engineering ; Process engineering ; Metal ; Materials science ; Waste management ; Computer science ; Engineering ; Metallurgy ; Chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Yuchao Song | Surendra Balraadjsing | Willie J.G.M. Peijnenburg | Martina G. Vijver
Source: NanoImpact (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting the dissolution of metal-based nanoparticles by means of QSPRs and the effect of data augmentation Particle dissolution is a critical process in the environmental fate assessment of metal-based nanoparticles (MNPs). Numerous attempts have been made previously to adequately quantify dissolution (kinetics), however, existing dissolution data and models are generally limited to a few nanomaterials or specific time points. Hence, they only capture phases of the process. This study aimed to develop a Quantitative Structure-Property Relationship (QSPR) model to predict the ion release (in %) of MNPs for different time points and water chemistry conditions. Furthermore, many machine learning models are frequently plagued by a lack of data and recently data augmentation has been suggested as a method to mitigate this issue. Therefore, we also investigated the effects of data augmentation on QSPRs. Following data collection from literature, QSPR models were generated and results indicate models with adequate performance (R<sup>2</sup> > 0.7). Results also demonstrated significant improvements in model performance with increasing amounts of applied data augmentation. However, a deeper evaluation of the results also highlighted that data augmentation can lead to misleading and overoptimistic model evaluation. Thus, proper model assessment is necessary when evaluating QSPRs. Variable importance analysis results revealed that the "initial concentration" and features related to the size and shape of MNPs were the most critical factors in the dissolution process. The predictive models generated here for MNP dissolution can improve nanomaterial testing efficiency and guide experimental design. Dissolution ; Nanoparticle ; Materials science ; Nanotechnology ; Chemical engineering ; Engineering Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications ; Recycling and Waste Management Techniques