jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiying He | Jiayu Ma | Zhenfei Wu | Di Wu | Zhao Li | Xiangyu Song | Jiajia Liu | Jiatao Zhang
Source: Nano Research (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Quantum Dot-based POCT systems: From multiplexed detection to cross-domain applications Quantum dots (QDs) are positioned to revolutionize point-of-care testing (POCT), with their exceptional brightness, photostability, and multiplexing capabilities being leveraged to enable real-time, in-situ detection with practical utility. In this review, recent advances in QD-based sensing are systematically summarized, with emphasis placed on how precise control over synthesis and surface functionalization across key material families (e.g., chalcogenide QDs) has facilitated the development of a versatile analytical toolkit. Diverse transduction mechanisms, including fluorescence, colorimetric, electrochemiluminescence, photoelectrochemical, and chemiresistive sensing, are explored, and their deployment across biomedical diagnostics, environmental surveillance, and food safety is highlighted. Moving beyond a mere summary of progress, this review focuses on addressing the critical challenges that currently hinder the practical translation of QD-based sensing technologies. Key issues include the biotoxicity of heavy-metal-containing QDs, their limitation in complex sample matrices, performance gaps in emerging “green” QDs, and hurdles in device integration and data analysis. A coordinated strategy is therefore proposed, centered on greenification, intellectualization and integration. It is envisioned that through advances in biocompatible materials, integration with portable platforms such as microfluidics and smartphones, and the incorporation of machine learning for intelligent signal processing, QDs will be propelled from laboratory tools into foundational, democratized technologies for next-generation POCT. The strategic framework presented here provides a clear roadmap to guide future research and translation efforts in QD-POCT. Key (lock) ; Software deployment ; Microfluidics ; Intellectualization ; Multiplexing ; Biocompatible material Quantum Dots Synthesis And Properties ; Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection
Authors: Nanxi Chen | Airong Chen | Rujin Ma
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Physics-informed neural operator for predictive parametric phase-field modelling Predicting the microstructural and morphological evolution of materials through phase-field modelling is computationally intensive, particularly for high-throughput parametric studies. While neural operators such as the Fourier neural operator (FNO) show promise in accelerating the solution of parametric partial differential equations (PDEs), the lack of explicit physical constraints, may limit generalisation and long-term accuracy for complex phase-field dynamics. Here, we develop a physics-informed neural operator framework to learn parametric phase-field PDEs, namely PF-PINO. By embedding the residuals of phase-field governing equations into the data-fidelity loss function, our framework effectively enforces physical constraints during training. We validate PF-PINO against benchmark phase-field problems, including electrochemical corrosion, dendritic crystal solidification, and spinodal decomposition. Our results demonstrate that PF-PINO significantly outperforms conventional FNO in accuracy, generalisation capability, and long-term stability. This work provides a robust and efficient computational tool for phase-field modelling and highlights the potential of physics-informed neural operators to advance scientific machine learning for complex interfacial evolution problems. Operator (biology) ; Parametric statistics ; Artificial neural network ; Embedding ; Benchmark (surveying) ; Limit (mathematics) ; Partial differential equation Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Yuman Guo | X.H. Sun | Xiaoli Xi | Zuoren Nie
Source: Advanced Science (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Mechanistic Analysis of Large Atomic Models of Molten Salt Machine-learned interatomic potential (MLIP) has become a powerful tool to combine the accuracy of quantum mechanics with the efficiency of molecular dynamics in the era of artificial intelligence. However, a key open question persists: what physical mechanism is behind the atomic model that generates the MLIP and what physical information determines the final outputs? To address this problem, we use molten Na<sub>2</sub>WO<sub>4</sub> as a representative system and fine-tune a pretrained deep potential model (DPA2) with ab initio molecular dynamics data of Na<sub>2</sub>WO<sub>4</sub>. We find a strong correlation between the model's final output and the projected density of states (PDOS) in energy regions exhibiting high electron density and distinct local atomic environments. This result indicates that a well-constructed neural network inherently captures the quantum-mechanical information and its predictions represent meaningful physicochemical interactions rather than purely statistical patterns. Importantly, the mechanistic insights gained in this work-which links model's outputs to electronic structure descriptors- are general in nature. It provides an electronic-structure-informed metric for feature learning and a general strategy for building interpretable, transferable MLIPs across diverse material systems. Interatomic potential ; Molecular dynamics ; Metric (unit) ; Ab initio ; Electronic structure ; Key (lock) ; Physical system ; Field (mathematics) ; Mechanism (biology) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Chemical Physics Studies
Authors: Muhammad Luqman Mohd Napi | Izzaty Mohd Zambri | Michael Loong Peng Tan | Suhana Mohamed Sultan
Source: Journal of The Electrochemical Society (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

ZnO Nanostructure-Based Biosensors in Cancer Detection: Synthesis, Mechanisms, and Future Perspectives Cancer remains a leading global health challenge, with early detection critical in improving patient outcomes. Zinc oxide (ZnO) nanostructure-based biosensors have emerged as a promising solution due to their high sensitivity, biocompatibility, and flexible transduction mechanisms. This review highlights the recent advancements in ZnO biosensors for cancer detection, focusing on synthesis methods, biosensing mechanisms, and performance metrics. A solution-based and vapour-phase technique for fabricating ZnO nanostructures is compared, emphasising their effect on morphology, crystallinity, and sensing performance. Electrochemical, optical, and piezoelectric mechanisms are explored, showing ZnO’s ability to detect biomarkers such as CA-125, CYFRA 21–1, and sarcosine with ultra-low detection limits. Innovations in multimodal sensing and integration with portable platforms are discussed, along with challenges in stability, scale-up production, and clinical translation. The review concludes with recommendations for future directions, supporting the improvement of material engineering, point-of-care usage, and machine learning integration to advance ZnO-based biosensors for earlier cancer diagnosis. These developments hold great potential to transform cancer diagnostics, providing rapid, non-invasive, and low-cost solutions to global healthcare. Biosensor ; Sarcosine ; Cancer ; Cancer detection ; Cancer therapy ZnO doping and properties ; Advanced Sensor and Energy Harvesting Materials ; Biosensors and Analytical Detection
Authors: D.H. Hernandez | Zhang Xiang | Tayab  Uddin Wara | Fnu Joshua | Jishnu Murukeshan | Sadia Rafiq | Lei Zhai | Yao Yao | Pulickel M. Ajayan | Tong Wu | Sidong Lei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Laser Tomographic Imaging of Defects in CVD Diamonds Utilizing Deep Neural Network Algorithm Femtosecond ; Photoluminescence ; Tomographic reconstruction ; Laser ; Visualization ; Chemical vapor deposition ; Diamond ; Raman spectroscopy ; Artificial neural network Diamond and Carbon-based Materials Research ; Laser Material Processing Techniques ; High-pressure geophysics and materials
Authors: Simon J. L. Billinge
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

What I learned about structure from artificial intelligence We sometimes wonder what artificial intelligence can teach us that we don't already know.Are there important life-lessons that we can glean from our interactions with AI beyond a more efficient (and more error prone) web search?In my case, the answer is a firm yes, but in a slightly surprising way.I have been a "crystallographer" (the quotes are because I like structure beyond crystals) for my whole long career and for the entire time, whilst often advocating for looking beyond the crystallographic description of structure, I have never questioned my understanding of what a structure is.However, when thinking about how to use AI and machine learning (ML) to study structure, new questions were brought into sharp focus that questioned my very understanding of what a structure is.In this talk I will share this intellectual journey.The journey is not over and I hope that interaction with natural intelligence (the audience members) can help to move the journey forward.I believe that there are somewhat serious and interesting implications for the large and growing industry of people applying ML to materials discovery. Wonder ; Focus (optics) ; Natural (archaeology) ; Applications of artificial intelligence ; Interpretation (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Yizhi Song | Renxi Liu | Chunyi Zhang | Yifan Li | Biswajit Santra | Mohan Chen | Michael L. Klein | Xifan Wu
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Understanding the Density Maximum of Water with Machine Learned Potentials After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4 °C. For nearly a century, this phenomenon has been qualitatively attributed to a mixture of ordered and disordered structures. Herein, we employ a deep neural network to train a machine learned (ML) interatomic potential for water using electronic structure data from advanced density functional theory. Notably, molecular dynamics simulations with the ML potential reproduce both the experimental water density anomaly and the thermal expansion coefficient. Detailed structural analysis of the computed hydrogen-bond network reveals that the density anomaly arises from an emergent liquid structure that retains nearly ideal tetrahedral coordination at short range but collapses at intermediate range. Our findings point to a more delicate mechanism causing the density maximum than the conventional picture, emphasizing the collective roles of structural orderings at different length scales. Maximum density ; Anomaly (physics) ; Tetrahedron ; Liquid water ; Thermal ; Range (aeronautics) ; Molecular dynamics ; Point (geometry) ; Ideal (ethics) Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Edvin Fako | Sandip De
Source: ACS Catalysis (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Simple Heuristics for Advanced Sampling of Reactive Species on Surfaces Understanding interactions between reactive fragments and surfaces is a central objective in functional materials design and discovery. No available computational methods can reliably generate such structures for an arbitrary choice of fragment and surface while explicitly describing the binding mode. As a result, human curation is accepted as the norm, introducing bias and hampering efforts toward computational materials screening and database engineering, limiting the reach and impact of machine learning in the field. By implementing two complementary concepts, surrogate-SMILES (*SMILES) and an atomic-position-only algorithm for surface site identification, we achieve simple parametrization of all fragment-surface configurations. We harness this fully hands-off approach to revisit CO2 reduction on copper, discovering diversity in the adsorption energy spectra, even for this benchmark system. We generate a holistic picture of fragment-surface energy features and compare them across systems and facets. Efficient parametrization and string-based expression provide a strong bridge of our approach to generative, optimization, and language-driven machine learning frameworks. With no domain-specific assertions, this approach extends to all domains in which surface reactivity is key for rational design. Heuristics ; Parametrization (atmospheric modeling) ; Benchmark (surveying) ; Simple (philosophy) ; Sampling (signal processing) ; Reduction (mathematics) ; Surface (topology) ; Key (lock) ; Generalization Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Tengyu Cheng | Zizhe Feng | Yingli Ha | Mingbo Pu | Lijun Wang | Yijia Huang | X. J. Luo
Source:  (article, 2026-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A fast computation method for the performance of meta-atoms based on deep learning Metasurfaces, with flexible electromagnetic wave control and smaller size than traditional devices, offer new potentials for light manipulation. However, the meta atoms of metasurfaces often require repeated electromagnetic simulations and parameter adjustments for design, which is time-consuming. Recently, deep learning has enabled fast computation and inverse design methods. Fast computation accelerates design speed and aids inverse design by validating data and speeding up the training process. However, current fast computation methods face issues such as a narrow range of applicability and discrepancies compared to electromagnetic simulations. In this work, we propose a fast computation method for optimizing meta-atoms based on deep learning. To validate the method, a deep learning network based on feature fusion was trained using 600,000 datasets, achieving a mean squared error (MSE) of less than 4×10<sup>−4</sup>. This network can rapidly obtain the optical responses of meta-atoms with almost arbitrary shapes within the 400 to 700nm range for TiO₂ and Si₃N₄ materials, all within milliseconds. This method not only accelerates the design of metasurfaces but also expedites the training of other metasurface networks, offering valuable insights for the rapid design of metasurfaces. Computation ; Deep learning ; Range (aeronautics) ; Inverse ; Feature (linguistics) ; Artificial neural network ; Mean squared error Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Plasmonic and Surface Plasmon Research
Authors: Avik Mahata
Source: ArXiv.org (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework integrating high-throughput Density Functional Theory (DFT), Random Forest screening, and Symbolic Regression to navigate this design space. This approach resolves the paradox where stoichiometric compounds like RuHf fail to achieve theoretical solvus temperatures. By deriving a closed-form physical law, we quantify the strain penalty: a 1% lattice misfit reduces the solvus temperature by approximately 200 degrees C. This finding confirms that maximizing thermodynamic driving force alone is insufficient. We demonstrate that multi-component alloying is structurally necessary, identifying ternary additions such as Al and Ti as essential lattice-tuning agents that zero out the elastic penalty. This framework establishes a rigorous, constraint-based protocol for alloy design, enabling the precise engineering of zero-misfit, high-stability microstructures. Solvus ; Superalloy ; Stability (learning theory) ; Intermetallic ; Density functional theory ; Ternary operation ; Alloy ; Precipitation ; Refractory (planetary science) Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Intermetallics and Advanced Alloy Properties
Authors: Daniel Isaias Vivas Zamora | Jose Alfredo Sanchez Diaz | Yndira Patricia Brito Guerrero
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-08)
Field: Chemistry — Physical and Theoretical Chemistry

An Integrated Electrochemical-Dielectric Framework for Multimodal Cellular Triage:Physics-Informed Edge AI on ESP32-S3 Early detection of cellular anomalies remains challenging in resource-limited settings where conventional diagnostic infrastructure is unavailable. This work presents a unified electrochemical-dielectric framework for multimodal (whole blood and buffer) label-free cellular screening that integrates Hydrogen Evolution Reaction (HER) voltammetry with dynamic ohmic drop compensation, Maxwell-Wagner dielectric spectroscopy with global spectral fitting, and machine learning with physics-informed regularization on an edge computing platform. We postulate that metabolic acidosis manifests as shifts in HER onset potential governed by Nernstian thermodynamics with explicit temperature dependence and kinetic/ohmic correction terms, while genomic anomalies, bacteria, and parasites alter complex permittivity through Hanai mixture dynamics with α and β dispersion differentiation. A 1D Convolutional Neural Network (CNN) with dual architecture and physics-informed regularization processes electrochemical signatures on an ESP32-S3 microcontroller, ensuring thermodynamic consistency during inference through explicit loss function formulation. Theoretical analysis indicates that populationlevel measurements (> 10Exp5 cells/mL) can achieve signal-to-noise ratios sufficient for anomaly flagging, though individual cell detection remains infeasible. We discuss implementation requirements, including dynamic iRΩ compensation via highfrequency pulse, external analog front-ends for impedance spectroscopy above 1 MHz, and uncertainty propagation analysis with error budgets for pH and dielectric parameters. This framework targets triage applications rather than definitive diagnosis, offering a sub-$50 portable screening tool for detection of metabolic, genetic, and infectious anomalies in multiple biological matrices. Open-source code and mathematical derivations are provided to facilitate reproducibility and community validation. Convolutional neural network ; Enhanced Data Rates for GSM Evolution ; Population ; Relaxation (psychology) ; Admittance ; Spectroscopy ; Artificial neural network ; Signature (topology) ; Genetic algorithm thermodynamics and calorimetric analyses ; Fractal and DNA sequence analysis ; Advanced Chemical Sensor Technologies
Authors: Yiran Zhou | Rui Tang | Rui Huang | Hui Dong | Weijie Lan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

High-Performance prediction of protein in brown rice via multi-spectral fusion and deep learning: A comparative study of visible, near infrared, mid infrared and hyperspectral imaging Hyperspectral imaging ; Feature selection ; Convolutional neural network ; Pattern recognition (psychology) ; Feature (linguistics) ; Fusion ; Support vector machine ; Artificial neural network ; Sensor fusion Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Avik Mahata
Source: arXiv (Cornell University) (preprint, 2026-01-04)
Field: Materials Science — Materials Chemistry

Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework integrating high-throughput Density Functional Theory (DFT), Random Forest screening, and Symbolic Regression to navigate this design space. This approach resolves the paradox where stoichiometric compounds like RuHf fail to achieve theoretical solvus temperatures. By deriving a closed-form physical law, we quantify the strain penalty: a 1% lattice misfit reduces the solvus temperature by approximately 200 degrees C. This finding confirms that maximizing thermodynamic driving force alone is insufficient. We demonstrate that multi-component alloying is structurally necessary, identifying ternary additions such as Al and Ti as essential lattice-tuning agents that zero out the elastic penalty. This framework establishes a rigorous, constraint-based protocol for alloy design, enabling the precise engineering of zero-misfit, high-stability microstructures. Solvus ; Superalloy ; Stability (learning theory) ; Intermetallic ; Density functional theory ; Ternary operation ; Alloy ; Precipitation ; Refractory (planetary science) Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Intermetallics and Advanced Alloy Properties
Authors: Xingshuo Feng | Wei Chen | Zongyu Huang | Jun Li | Xiang Qi
Source: Nanotechnology (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Machine learning for layer number identification of black phosphorus based on Raman spectra Black phosphorus (BP) is a novel two-dimensional (2D) material with tunable electronic and optical properties. Thickness is a pivotal parameter in defining the electronic, optical, and thermal properties of 2D crystals. Determining the thickness of a material is crucial to studying its properties. However, conventional characterization methods for the directly determination of thick layers of BP are complex and inefficient. In this paper, we propose a machine learning (ML)-based method that can efficiently and accurately determine the layer number of BP. The features of the three characteristic peaks (Ag1,B2g, andAg2) were extracted from the Raman spectra, including peak position, intensity, full width at half maximum, and integrated intensity. Subsequently, we found that the intensity ratio of the substrate (Si) peak to the Raman mode is crucial to predicting the number of layers by feature importance analysis. This study makes a key contribution by presenting, for the first time, a comparative analysis of multiple ML algorithms for identifying the layer number of BP. Furthermore, it identifies a specific set of discriminative features tailored for BP's Raman spectra. Finally, by synergistically augmenting the dataset and refining the model architecture, we effectively mitigated the performance limitations imposed by the small dataset. The performance of the model is evaluated based onR2, mean square error, and mean absolute error, where theR2of all algorithms is not less than 0.9. ML models can accurately predict the number of layers of BP material. ML algorithms can automatically learn from the data and optimize the algorithm to improve the efficiency and accuracy of the model. This not only reduces the analysis burden on researchers but also promotes the in-depth application of artificial intelligence in 2D material characterization. Raman spectroscopy ; Feature (linguistics) ; Black phosphorus ; Discriminative model ; Substrate (aquarium) ; Layer (electronics) ; Raman scattering ; Identification (biology) ; Support vector machine 2D Materials and Applications ; Thermal properties of materials ; Ga2O3 and related materials