jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiazheng Wang | Qiang Lou | Zhengjie Xu | Yufeng Jin | Guangcan Luo | Hang Zhou
Source: Materials Today Electronics (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Accelerating ionic liquid research in perovskite solar cells through machine learning:Opportunities and challenges In recent years, there have been continuous and remarkable efforts from both academic and industry to improve the efficiency and stability of perovskite solar cells (PSCs). Among all the efforts, Ionic liquids (IL), a class of compounds with asymmetric organic cations and various anions, stand out as one of the most promising additives and interface modification layer for realizing high performance PSCs due to their unique physicochemical properties. Nonetheless, due to the variety of ionic liquids, searching an effective and optimum IL passivation materials for PSCs requires a huge amount of time and efforts in conventional trial-and-error experiments. In this context, machine learning (ML) offers powerful capabilities to handle complex, nonlinear problems, potentially accelerating the discovery and optimization of IL for PSCs applications. This review provides a comprehensive overview of the current applications of IL in PSCs, and summarizes the opportunities and key challenges in combining ML methods for IL research in PSCs. With the proposed ML frameworks, it is expected that a more predictive ML piloted research process would accelerate the discovery and optimization of IL in PSCs. Ionic liquid ; Perovskite (structure) ; Materials science ; Engineering physics ; Chemical engineering ; Nanotechnology ; Computer science ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Machine Learning and ELM ; Advanced Memory and Neural Computing
Authors: Pin Wu | Haiwang Huang | Qingcheng Yang | Bo Qian | Yongxin Gao | Yiguo Yang | Huiran Zhang | Qiang Zhen
Source: Computational Materials Science (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method Phase (matter) ; Microstructure ; Field (mathematics) ; Deep learning ; Materials science ; Artificial intelligence ; Computer science ; Biological system ; Statistical physics ; Physics ; Metallurgy ; Biology ; Mathematics Solidification and crystal growth phenomena ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: María Agustina Guitar | U.P. Nayak | Guiomar Riu Perdrix | Jeffrey M. Wheeler | J.J. Roa
Source: Materials Science and Engineering A (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Tracking phase-level properties in heat-treated high-chromium cast irons using mechanical microscopy Determining individual microstructural components in multicomponent and multiscale materials can be a challenging task. High chromium cast irons (HCCI), particularly in heat-treated states, exhibit complex microstructures that include austenite or martensite on a meso-scale, as well as hard and micro-sized eutectic carbides, and secondary carbides on a sub-micron or nanometric length scale. Assessing the mechanical properties and quantities of these phases requires small scale characterization over relatively large areas. In the current work, the mechanical properties of each individual constitutive phase were determined by using high-speed nanoindentation maps as a function of four different heat treatments. An area of 400 × 300 μm containing 30 000 indentations on each sample was analyzed. Statistical deconvolution and machine-learning clustering methods were used to determine the hardness and elastic modulus of the different microstructural components. The results illustrate how the microstructure and mechanical properties of the various phases evolve through heat treatment, supporting the notion that secondary carbides reinforce and strength the HCCI matrix, rather than acting as an individual component. Materials science ; Cast iron ; Metallurgy ; Chromium ; Phase (matter) ; Optical microscope ; Tracking (education) ; Microscopy ; Composite material ; Scanning electron microscope ; Optics ; Chemistry ; Physics Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Yaoyao Fan | Zheli Wang | Xueying Yao | Wenqian Huang | Qingyan Wang | Xi Tian | Liping Chen | Long Yuan
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-07-20)
Field: Chemistry — Analytical Chemistry

Efficient wheat variety identification using Raman hyperspectral imaging in combination with deep learning Chemistry ; Hyperspectral imaging ; Raman spectroscopy ; Identification (biology) ; Chemical imaging ; Nanotechnology ; Artificial intelligence ; Remote sensing ; Botany ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Yue Sun | Wenhe Xie | Fangying Shi | Jichun Li | Hongxiu Yu | Yongjian Jiang | Deng Chun-hui | Yonghui Deng
Source: Advanced Materials (article, 2025-12-05)
Field: Chemistry — Spectroscopy

Mesoporous High‐Entropy Oxides Nanoplatform Decodes Paired‐Plasma Metabolic Fingerprinting of Pancreatic Cancer Accurate detection of small molecule metabolites in vivo is critical for rapid screening of disease biomarkers and health monitoring. Matrix-assisted laser desorption/ ionization mass spectrometry (MALDI-MS) has emerged as a promising platform for metabolic profiling, but its capability is hindered by the limited light absorption and energy transfer of conventional matrix materials. In this work, a high-efficiency metabolic detection platform based on high-entropy oxide particles (mHEO) with an interconnected mesoporous structure and tailored compositions is established. Owing to their abundant active sites and excellent light utilization efficiency, the mHEO particles show significantly improved photothermal and photochemical properties with an eightfold localized enhanced electromagnetic field and higher surface temperatures (616 °C) than nonporous HEOs (336 °C). As a result, MALDI-MS based on the mHEO matrix exhibits high sensitivity, good reproducibility (Coefficient of Variation < 10%), and ultralow detection limits with 1-3 orders of magnitude lower than their endogenous concentrations. Furthermore, the mHEO-based MALDI-MS platform is applied to analyze paired arterial/venous blood samples from pancreatic cancer (PC) patients with the assistance of machine learning. Four tumor microenvironment-associated metabolites are identified as a potential biomarker panel of PC, achieving a robust pancreas-venous plasma classification, which allows the timely screening and targeted treatment of PC. Photothermal therapy ; Pancreatic cancer ; Mesoporous material ; Mesoporous silica ; In vivo ; Graphene ; Matrix (chemical analysis) ; Biomarker ; Oxide ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Advanced Nanomaterials in Catalysis ; Ion-surface interactions and analysis
Authors: Wanli DU | G.M. Chen | Zixuan Zhang | Xinsong WANG | Shunchuan Yang | X.-G. Chen | Donglin Su
Source: Chinese Journal of Aeronautics (article, 2025-01-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SCS-Net: A DNN-based electromagnetic shielding effectiveness analysis method for slotted composite structures As the proportion of composite materials used in aircraft continues to increase, the electromagnetic Shielding Effectiveness (SE) of these materials becomes a critical factor in the electromagnetic safety design of aircraft structures. The assessment of electromagnetic SE for Slotted Composite Structures(SCSs) is particularly challenging due to their complex geometries and there remains a lack of suitable models for accurately predicting the SE performance of these intricate configurations. To address this issue, this paper introduces SCS-Net, a Deep Neural Network (DNN) method designed to accurately predict the SE of SCS. This method considers the impacts of various structural parameters, material properties and incident wave parameters on the SE of SCSs. In order to better model the SCS, an improved Nicolson-Ross-Weir (NRW) method is introduced in this paper to provide an equivalent flat structure for the SCS and to calculate the electromagnetic parameters of the equivalent structure. Additionally, the prediction of SE via DNNs is limited by insufficient test data, which hinders support for large-sample training. To address the issue of limited measured data, this paper develops a Measurement-Computation Fusion (MCF) dataset construction method. The predictions based on the simulation results show that the proposed method maintains an error of less than 0.07 dB within the 8-10 GHz frequency range. Furthermore, a new loss function based on the weighted L1-norm is established to improve the prediction accuracy for these parameters. Compared with traditional loss functions, the new loss function reduces the maximum prediction error for equivalent electromagnetic parameters by 47%. This method significantly improves the prediction accuracy of SCS-Net for measured data, with a maximum improvement of 23.88%. These findings demonstrate that the proposed method enables precise SE prediction and design for composite structures while reducing the number of test samples needed. Electromagnetic shielding ; Composite number ; Net (polyhedron) ; Materials science ; Structural engineering ; Composite material ; Engineering ; Mathematics ; Geometry Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Measurements ; Advanced Antenna and Metasurface Technologies
Authors: Zhenming Cui | Hongwei Liu | X.L An | Xin An | Zhennan Qiao | Yongzhen Wang
Source: Journal of Electroanalytical Chemistry (article, 2025-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergistic regulation of carbonyl oxygen active sites and micropores in lignite-derived porous carbon for high-performance supercapacitors Supercapacitor ; Capacitance ; Microporous material ; Oxygen ; Carbon fibers ; Porosity ; Density functional theory ; Specific surface area Supercapacitor Materials and Fabrication ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Jiale Zhang | Bingjie Ye | Xiaoxiao Kong | Liejia Qian | Huazhen Sun | X. H. Zhang | И. Н. Пархоменко | Ф. Ф. Комаров | Jin Wang | Junjun Xue | Xinyi Shan | Jun‐Ge Liang | Guofeng Yang
Source: Materials Today Nano (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

2D/3D ZrS3/p-GaN heterostructure photodetector for dual-band and polarization-based optical information encryption Photodetector ; Heterojunction ; Optoelectronics ; Polarization (electrochemistry) ; Encryption ; Materials science ; Physics ; Optics ; Computer science ; Chemistry ; Computer network 2D Materials and Applications ; Metamaterials and Metasurfaces Applications ; MXene and MAX Phase Materials
Authors: Jen‐Tai Lin | Yi-Cheng Chung | Tong-Lin Wu | Wan-Ching Lin | Balasubramanian Sriram | Sea‐Fue Wang | Praveen SV | Thiagarajan Soundappan | Sakthivel Kogularasu | Guo-Ping Chang-Chien
Source: Microchemical Journal (article, 2025-07-15)
Field: Chemistry — Spectroscopy

Empowering early detection of prostate cancer: a point-of-care biosensing perspective Perspective (graphical) ; Prostate cancer ; Biosensor ; Cancer detection ; Point of care ; Point-of-care testing ; Medicine ; Cancer ; Internal medicine ; Nanotechnology ; Computer science ; Nursing ; Materials science ; Pathology ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Meng Zhang | Jiangping Song | Huixia Jia | Xiaohui Zhang | Wenlong Yang | Yang Wang | Haiping Wang
Source: Agriculture (article, 2025-05-12)
Field: Chemistry — Analytical Chemistry

Prediction of Vigor of Naturally Aged Seeds from Xishuangbanna Cucumber (Cucumis sativus L. var. xishuangbannanesis) Using Hyperspectral Imaging Xishuangbanna cucumber (Cucumis sativus L. var. xishuangbannanesis), as a rare and endangered cucumber germplasm resource, possesses certain irreplaceable characteristics that make it difficult to reacquire once lost. To ensure long-term preservation of this germplasm, immediate propagation and regeneration are required after successful collection. Current germplasm management relying on conventional viability testing methods often leads to seed loss. Therefore, there is an urgent need to develop a rapid and non-destructive testing technology for assessing the seed viability of Xishuangbanna cucumber. This study integrated hyperspectral imaging technology with various data preprocessing methods, feature wavelength selection algorithms, and classification models to achieve rapid and non-destructive detection of Xishuangbanna cucumber seed viability. Hyperspectral imaging was employed to acquire spectral data from the seeds. Preprocessing methods including MSC (Multivariate Scattering Correction), SNV (Standard Normal Variety), FD (First Derivative), SD (Second Derivative), and L2NN (L2 Norm Normalization) were applied to enhance spectral data quality. Feature selection algorithms such as UVE (Uninformative Variables Elimination), SPA (Successive Projections Algorithm), and CARS (Competitive Adaptive Reweighted Sampling) were utilized to identify optimal spectral bands. Combined with KNN (K-Nearest Neighbor) and LogitBoost algorithms, predictive models for seed viability were established. The results demonstrated that the L2NN-KNN model outperformed other models, achieving an accuracy of 83.33%, precision of 86.99%, and an F1-score of 0.83. This study confirms that hyperspectral imaging combined with machine learning can effectively predict the viability of Xishuangbanna cucumber seeds, providing a novel technical approach for the conservation of rare and endangered cucumber germplasm resources. The findings hold significant implications for promoting long-term preservation and sustainable utilization of this valuable genetic material. Cucumis ; Hyperspectral imaging ; Biology ; Botany ; Horticulture ; Geography ; Remote sensing Spectroscopy and Chemometric Analyses ; Advances in Cucurbitaceae Research ; GABA and Rice Research
Authors: Hyeong Chan Suh | Jaekak Yoo | Kangmo Yeo | Dong Hyeon Kim | Yo Seob Won | Taehoon Kim | Youngwoo Cho | Ki Kang Kim | Seung Mi Lee | Heejun Yang | Dongwook Kim | Mun Seok Jeong
Source: Applied Physics Reviews (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Probing nanoscale structural perturbation in a WS2 monolayer via explainable artificial intelligence This study investigates the applicability of the machine learning model in correlative spectroscopy to enhance spatial resolution for probing nanoscale structural perturbations. The developed model demonstrates significant enhancement in spatial resolution, achieving up to 50 nm through the integration of Kelvin probe force microscopy and atomic force microscopy data. The predicted nanoscale Raman image reveals abnormal behaviors associated with strain-induced lattice perturbations, such as the presence of compressive and tensile strains within identical nanoscale wrinkles. Afterward, we interpreted the trained model using explainable artificial intelligence techniques, uncovering synergistic contributions to the Raman features across each input dataset within the nanoscale region. Our analysis demonstrates that the model effectively reflects key strain-induced lattice behaviors, highlighting its nanoscale sensitivity to structural perturbations. Finally, we validated these findings using quantum mechanical calculations, which confirmed the strain-induced changes in Raman-active modes. This study offers comprehensive insights into nanoscale structural perturbations, paving the way for innovative approaches to high-resolution spectroscopic analysis in low-dimensional materials. Nanoscopic scale ; Monolayer ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology 2D Materials and Applications ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Rui Guo | Shiyu Song | Qing Cao | Wenbo Zhao | Xiangyu Li | Huifang Zhang | Fu‐Kui Li | Ya‐Chuan Liang | Chongxin Shan | Kai‐Kai Liu
Source: Advanced Materials (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven Achieving Efficient Phosphorescent Carbon Nanodots in Aqueous Solution by Suppressing Triplet Electron Leakage Matrix-assisted synthesis has emerged as a prevalent strategy for the fabrication of solid-state phosphorescent carbon nanodots (CNDs), yet achieving efficient liquid-phase CND systems remains challenging due to complex underlying mechanisms. The knowledge gap poses a substantial barrier to translating phosphorescent CND systems between solid-state and liquid-phase configurations. Herein, the critical role of triplet electron leakage is elucidated in reducing the phosphorescent performance of CNDs and demonstrates how machine learning (ML) can be applied to suppress this leakage, achieving efficient phosphorescent CNDs in aqueous solution. By integrating experimental datasets from hundreds of systematically designed syntheses, an interpretable ML model capable of predicting and tuning phosphorescence lifetimes in CND systems is developed. The quantitative relationships between features and lifetimes using SHapley Additive exPlanations (SHAP) analysis are established, revealing an inverse correlation between matrix thickness and the probability of triplet electron leakage. Guided by the developed ML model, efficient phosphorescent CNDs are achieved in aqueous solution with an emission duration lifetime of exceeding10 s and a phosphorescent quantum yield of over 10%. This study establishes a conceptual and methodological framework for engineering high-performance phosphorescent nanomaterials in liquid-phase systems. Phosphorescence ; Materials science ; Nanodot ; Leakage (economics) ; Aqueous solution ; Quantum yield ; Nanomaterials ; Nanotechnology ; Fluorescence ; Chemistry ; Physics ; Physical chemistry ; Optics Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Quantum Dots Synthesis And Properties
Authors: Rui Zhan | Wenyi Zhang | Jing Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-01-02)
Field: Materials Science — Materials Chemistry

Scrutinization on Docking Against Individually Generated Target Pockets for Each Ligand Abstract The longstanding challenge of limited exploration in protein receptor conformational space continues to constrain the precision of molecular docking. Ensemble docking, which employs methods such as molecular dynamics simulations to generate multiple receptor conformations for docking, has improved accuracy but remains limited by incomplete sampling and an inability to fully account for ligand-induced fit. To address these limitations, we introduce the concept of individual docking, a novel approach that involves docking against receptor conformations generated individually for each ligand in the docking library. This approach has only very recently become feasible due to advances in protein structure prediction, in particular end-to-end protein-ligand complex prediction technologies. In this study, we performed individual docking on 27 targets from the DUD-E dataset, using a two-step protocol that integrates NeuralPLexer or AlphaFold3 for receptor conformation sampling, followed by physics-based docking. Our results reveal that individual docking with AlphaFold3 predictions yields more than a twofold improvement in enrichment factors compared to standard docking. Furthermore, individual docking recovers distinct sets of active ligands, thereby expanding the diversity of virtual screening hits. Detailed analyses of pocket and ligand conformations suggest several potential incompatibilities between deep learning-based and physics-based virtual screening tools. Docking (animal) ; Computer science ; Chemistry ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shixin Wang | Daolin Yang | Xijun Wu | Zherui Du | Xin Zhang | Xuan Zhao | Jiangtao Wang | Yungang Zhang
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2025-09-08)
Field: Chemistry — Analytical Chemistry

Detection of antioxidants in edible oils using surface-enhanced Raman spectroscopy combined with machine learning Butylated hydroxytoluene ; Butylated hydroxyanisole ; Antioxidant ; Detection limit ; Colloidal gold ; Substrate (aquarium) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Jun Chai | Ming Chen | Yiming Zeng | Zhifu Liu | Yi‐Yang Sun
Source: Physical review. B./Physical review. B (article, 2025-01-21)
Field: Materials Science — Ceramics and Composites

Surface energy of glass and interfacial energy of glass with metals Interfaces between glass and metals are of paramount importance from many technological aspects. However, the information on surface energies of glass and their interfacial energies with metals is surprisingly absent in the literature. Here, we propose a protocol based on first-principles calculations and ab initio molecular dynamics simulations assisted by machine-learning force fields to address this problem. We evaluate the surface energy of silica glass and its interfacial energy with three metals (Cu, Ag, and Au). The surface models of glass are generated by using a potential wall approach and based on these surface models a sufficient number of interface models are constructed. Then, the surface energy of silica glass and the interfacial energies are evaluated by a statistical approach. Converged surface energy of 49 meV/\AA{}${}^{2}$ is obtained for silica glass. Its interfacial energies with the (001) surfaces of Cu, Ag, and Au are calculated to be 104, 76, and 86 meV/\AA{}${}^{2}$, respectively. The lowest interfacial energy between Ag and glass is consistent with experiments that show good wettability of glass on Ag surface. Surface energy ; Materials science ; Surface (topology) ; Energy (signal processing) ; Composite material ; Physics ; Geometry ; Mathematics Glass properties and applications ; Material Science and Thermodynamics ; Laser Material Processing Techniques
Authors: Cesare Malosso | Edward Danquah Donkor | Stefano Baroni | Ali Hassanali
Source: The Journal of Chemical Physics (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Dynamical heterogeneity in supercooled water and its spectroscopic fingerprints A growing body of theoretical and experimental evidence strongly supports the existence of a second liquid-liquid critical point (LLCP) in deeply supercooled water leading to the co-existence of two phases: a high- and low-density liquid (HDL and LDL). While the thermodynamics associated with this putative LLCP has been well characterized through numerical simulations, the dynamical properties of these two phases close to the critical point remain much less understood. In this work, we investigate their dynamical and spectroscopic features using machine-learning interatomic potentials. Dynamical analyses using the van Hove correlation function reveal that LDL exhibits very sluggish and heterogeneous molecular mobility, in contrast to the faster and more homogeneous dynamics of HDL. Infrared absorption (IR) spectra further show clear vibrational distinctions between LDL and HDL, in particular in the far IR region between 400 and 1000 cm-1. Together, these findings provide new dynamical fingerprints that clarify the microscopic behavior of supercooled water and offer valuable guidance for experimental efforts aimed at detecting the long-sought liquid-liquid transition. Supercooling ; Dynamical heterogeneity ; Homogeneous ; Critical point (mathematics) ; Molecular dynamics ; Absorption (acoustics) ; Spectral line ; Liquid water Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Spectroscopy and Quantum Chemical Studies