jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fen Zhang | Yì Wáng | Haiqing Xie | Mianzeng Zhong
Source: Journal of Alloys and Compounds (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

High-performance broadband photodetection in WSe2/CsPbBrI quantum dot hybrid heterostructures for optoelectronic switching and image recognition Photodetection ; Responsivity ; Heterojunction ; Photodiode ; Quantum dot ; Broadband 2D Materials and Applications ; Perovskite Materials and Applications ; Nanowire Synthesis and Applications
Authors: Qiujie Huang | Yu Dou | Yang Li | Zhihao He | Jianguo Zhu | Duleepa J. Thrimawithana | Sinan Li
Source: IEEE Transactions on Power Electronics (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural-Enhanced Physics: A New Paradigm for Accurate and Dimension-Aware Magnetic Core Loss Modeling The accurate and dimension-aware modeling of magnetic core loss is fundamentally limited by a trade-off: model-driven approaches are inherently dimension-aware but are often inaccurate; purely data-driven methods are often accurate but lack dimension-transferability. This paper resolves this dilemma by introducing a novel neural-enhanced physics (NEP) paradigm, which enables highly data-efficient adaptation to different physical core dimensions while maintaining high fidelity. We demonstrate this paradigm with the neural-enhanced dynamic circuit model (NE-DCM), where a physics-based circuit handles dimension-aware electromagnetic dynamics while a data-efficient neural network accurately captures complex material hysteresis. This architecture, based on a principled decoupling of physics and material behavior, yields a key practical advantage: a highly data-efficient workflow. The neural network is trained only once using quasi-static data for a given material, while the physics-based model requires only minimal calibration for new core dimensions. Validated on TDK N87 toroidal cores of different sizes under diverse excitations, the NE-DCM demonstrates superior accuracy, waveform generality, and—most critically—data-efficient transferability across core dimensions, outperforming both purely model-driven and purely data-driven models. The extensibility of the paradigm is further confirmed through successful validation on a second, distinctly different ferrite material (TDK T65). This work establishes the NEP paradigm as a robust and extensible foundation for the next generation of magnetic component design tools. Decoupling (probability) ; Ferrite core ; Waveform ; Core (optical fiber) ; Robustness (evolution) ; Artificial neural network ; Magnetic core ; Key (lock) Magnetic Properties and Applications ; Magnetic properties of thin films ; Model Reduction and Neural Networks
Authors: Jingwen Zhou | Yawen Yu | Xuwei Liu | Chungen Liu
Source: arXiv (Cornell University) (preprint, 2026-04-08)
Field: Materials Science — Materials Chemistry

Explicit Electric Potential-Embedded Machine Learning Framework: A Unified Description from Atomic to Electronic Scales To further develop accurate and large-scale simulations of electrochemical interfaces, we propose a unified explicit electric potential framework to simultaneously predict atomic forces and electron density distributions. The framework consists of three components: data generation, model training, and application. The data generation component, implemented in Hy-DFT, efficiently regulates the potential during constant-potential ab initio molecular dynamics (CP-AIMD), reducing the number of single-point calculations required for convergence. The model training component includes two modules: Potential-Embedded MACE (PE-MACE) and Potential-Embedded Electron Density Prediction (PE-EDP). PE-MACE implements an explicit electric potential machine learning force field (EEP-MLFF) based on the MACE architecture. We develop PE-EDP to overcome the limitation of EEP-MLFF in describing atom forces. PE-EDP, also based on equivariant graph neural networks, predicts electron density distributions under arbitrary potentials. Using the Pt(111)/water interface as a model system, both PE-MACE and PE-EDP show high accuracy on training and test sets. Radial distribution functions from CP-MLMD agree well with CP-AIMD, and long-timescale simulations reveal potential-induced reorganization of interfacial water. Planar-integrated charge profiles and Bader analysis from PE-EDP are consistent with DFT results. These results demonstrate that the framework can simultaneously describe atomic dynamics and electron density distributions under arbitrary potentials, providing a useful tool for studying electrochemical interfaces. Electric field ; Charge density ; Electron density ; Atom (system on chip) ; Electron ; Molecular dynamics ; Density functional theory ; Electric potential Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Jackson S. Bentley | Christopher M. Rouleau | Ilia N. Ivanov | T. Zac Ward | Jiaqiang Yan | Anghea Dolisca | Rob G. Moore | Gyula Eres | Richard F. Haglund | Sumner B. Harris | Matthew Brahlek
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Active Learning for Tractable and Reproducible Pulsed Laser Deposition This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth conditions and material properties, consistently producing high-quality films of complex materials like LaVO$_3$ remains difficult due to the highly non-equilibrium nature of PLD and the defects and competing phases that accumulate during growth. Here, we use an active learning framework based on Gaussian process Bayesian optimization that incorporates measured bulk and surface lattice properties along with impurity phase information to efficiently map the multidimensional growth space of LaVO$_3$ by PLD. By tuning the relative weighting of these properties, the model identifies an optimized region where phase-pure films of LaVO$_3$ exhibit two-dimensional surfaces, near-ideal lattice parameters, and minimal sub-band gap optical absorption. The trained model reveals clear competition among different defect formation mechanisms that are connected to unseen parameters like supersaturation and surface mobility, thus giving insight into the highly non-equilibrium process of PLD growth. Together, this demonstrates that property-guided machine learning can accelerate materials optimization while providing a new way to address fundamental growth mechanisms in PLD that enable understanding and utilization of quantum phenomena found in complex oxides. Bayesian optimization ; Pulsed laser deposition ; Active learning (machine learning) ; Gaussian process ; Lattice (music) ; Deposition (geology) ; Process (computing) ; Weighting Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Artificial Intelligence in Healthcare and Education
Authors: Xiang Zhou | Sixue Ouyang | Siyun Guo | Yukang Wen | Sike Lv | Ningxuan Liu | Jiajia Lu | Xin Zheng | Haohan Ji | Xijia Deng | Peng Zhao | Long Hao | Jia Tao
Source: Nano Letters (article, 2026-03-06)
Field: Chemistry — Inorganic Chemistry

Lanthanide-Doped Organic Framework Sensor Array Coupled with Machine Learning for Minimally Invasive Glioma Diagnosis via Cerebrospinal Fluid Biopsy Glioblastoma multiforme (GBM), the most malignant subtype of glioma, poses significant diagnostic challenges due to limitations in current methods, such as invasive histopathological examination and costly, lab-restricted biomarker detection technologies. Herein, we report a lanthanide (Tb<sup>3+</sup>)-doped organic framework-based sensor array for minimally invasive, sensitive, and accurate glioma diagnosis via cerebrospinal fluid (CSF) biopsy. The sensor array integrates three distinct Tb<sup>3+</sup>-doped frameworks, which exhibit unique topological structures, surface charges, and fluorescence responses, enabling differential recognition of glioma-related biomarkers. The sensor array demonstrated robust discriminatory capacity for eight CSF-relevant molecules via a machine learning algorithm. When applied to clinical CSF samples, it achieved satisfactory separation of glioma patient and normal control samples with 95.5% diagnostic accuracy. This sensor array, combined with advanced machine learning, offers great potential for clinical translation in early glioma diagnosis and molecular stratification. Glioma ; Cerebrospinal fluid ; Sensor array ; Biomarker ; Magnetic resonance imaging ; Glioblastoma ; Differential diagnosis ; Biopsy Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Jackson S. Bentley | Christopher M. Rouleau | Ilia N. Ivanov | T. Zac Ward | Jiaqiang Yan | Anghea Dolisca | Rob G. Moore | Gyula Eres | Richard F. Haglund | Sumner B. Harris | Matthew Brahlek
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Active Learning for Tractable and Reproducible Pulsed Laser Deposition This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth conditions and material properties, consistently producing high-quality films of complex materials like LaVO$_3$ remains difficult due to the highly non-equilibrium nature of PLD and the defects and competing phases that accumulate during growth. Here, we use an active learning framework based on Gaussian process Bayesian optimization that incorporates measured bulk and surface lattice properties along with impurity phase information to efficiently map the multidimensional growth space of LaVO$_3$ by PLD. By tuning the relative weighting of these properties, the model identifies an optimized region where phase-pure films of LaVO$_3$ exhibit two-dimensional surfaces, near-ideal lattice parameters, and minimal sub-band gap optical absorption. The trained model reveals clear competition among different defect formation mechanisms that are connected to unseen parameters like supersaturation and surface mobility, thus giving insight into the highly non-equilibrium process of PLD growth. Together, this demonstrates that property-guided machine learning can accelerate materials optimization while providing a new way to address fundamental growth mechanisms in PLD that enable understanding and utilization of quantum phenomena found in complex oxides. Bayesian optimization ; Pulsed laser deposition ; Active learning (machine learning) ; Gaussian process ; Lattice (music) ; Deposition (geology) ; Process (computing) ; Weighting Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Artificial Intelligence in Healthcare and Education
Authors: Terry C. W. Lam | Niamh O'Neill | Christoph Schran | Lars L. Schaaf
Source: ArXiv.org (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials The accuracy of machine learning interatomic potentials suffers from reference data that contains numerical noise. Often originating from unconverged or inconsistent electronic-structure calculations, this noise is challenging to identify. Existing mitigation strategies such as manual filtering or iterative refinement of outliers, require either substantial expert effort or multiple expensive retraining cycles, making them difficult to scale to large datasets. Here, we introduce an on-the-fly outlier detection scheme that automatically down-weights noisy samples, without requiring additional reference calculations. By tracking the loss distribution via an exponential moving average, this unsupervised method identifies outliers throughout a single training run. We show that this approach prevents overfitting and matches the performance of iterative refinement baselines with significantly reduced overhead. The method's effectiveness is demonstrated by recovering accurate physical observables for liquid water from unconverged reference data, including diffusion coefficients. Furthermore, we validate its scalability by training a foundation model for organic chemistry on the SPICE dataset, where it reduces energy errors by a factor of three. This framework provides a simple, automated solution for training robust models on imperfect datasets across dataset sizes. Overfitting ; Outlier ; Scalability ; Extrapolation ; Noise (video) ; Robustness (evolution) ; Training (meteorology) ; Kernel (algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: K. Kurpiewska | J. Sławek
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Is it worth crystallizing phosphatases in the age of AI-based structural modeling? Recent advances in deep learning have dramatically expanded the scope of protein structure prediction by enabling accurate modeling not only of individual proteins but also of their interactions with ligands, nucleic acids, and small molecules.This development raises a fundamental question in structural biology: is experimental crystallization still necessary, particularly for functionally conserved enzyme families such as phosphatases?In this work, we model a representative set of phosphatases using AlphaFold 3 and analyze the structural features in detail for one selected enzyme.We compare the AI-generated model with its experimentally determined crystal structure to examine differences in side-chain positioning, and active site conformation. Phosphatase ; Scope (computer science) ; Set (abstract data type) ; Protein structure ; Structural similarity ; Active site Enzyme Structure and Function ; Protein Structure and Dynamics ; Protein Tyrosine Phosphatases
Authors: Raymond W. Lee | Areen S. Patil | Yunpeng Zhang
Source: Scientific Reports (article, 2026-01-10)
Field: Chemistry — Inorganic Chemistry

Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage Economic viability of seasonal hydrogen storage depends on minimizing recovery costs as reservoir pressure and hydrogen purity decline during withdrawal, necessitating pressure-swing adsorption purification where adsorbent selectivity directly impacts separation efficiency. Existing metal-organic framework (MOF) screening evaluates performance under idealized conditions such as equimolar mixtures and single pressures, providing no guidance for matching adsorbent geometry to evolving pressure and composition profiles during withdrawal. We establish quantitative, stage-specific design rules using machine learning and explainable AI on 712 experimentally synthesized MOF structures across four withdrawal stages spanning 60 to 25 bar and 98% to 65% hydrogen purity. Optimal geometric parameters controlling selectivity shift systematically across pressure and composition conditions: accessible volume dominates at high pressure, void fraction controls intermediate stages, and pore aperture governs low-pressure performance. Multiple viable pathways at each stage provide synthesis flexibility. These stage-resolved criteria enable MOF selection based on realistic operating conditions rather than idealized scenarios. Adsorption ; Selection (genetic algorithm) ; Hydrogen ; Hydrogen storage ; Matching (statistics) ; Selectivity ; Pressure swing adsorption ; Fraction (chemistry) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Hydrogen Storage and Materials
Authors: Hyo Sung Hwang | Jeong Kim
Source: Journal of the Korean Society for Precision Engineering (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A Machine Learning-based Approach for Classifying Waveform Distortion Due to Misalignment in SHPB Experiments The Split Hopkinson Pressure Bar (SHPB) experiment is commonly employed to assess the dynamic mechanical properties of materials under high strain-rate conditions (10²-10⁴ s-¹) through the propagation of elastic stress waves via pressure bars. The precision and dependability of SHPB measurements are heavily influenced by the alignment of the specimen with the bars. Misalignment can lead to flexural vibrations, causing waveform distortion and undermining the assumption of one-dimensional stress waves. While previous research has explored the impact of misalignment on waveform characteristics, pinpointing the specific sources of distortion from measured signals remains a challenge. This study introduces a machine learning-based classification method that extracts features from distorted SHPB waveforms to identify the type of misalignment. Incident wave signals under various misalignment scenarios were simulated using the commercial finite element software LS-DYNA, and the extracted features were utilized to create a training dataset. Several machine learning models, including XGBoost, were trained and evaluated, with XGBoost yielding the highest accuracy and F1-score. The trained model was then applied to experimentally measured distorted waveforms to validate its effectiveness. This proposed approach facilitates the automated diagnosis of distortion sources in SHPB data, reducing the need for manual interpretation and improving analysis efficiency. Waveform ; Distortion (music) ; Split-Hopkinson pressure bar ; Dependability ; Spurious relationship ; Finite element method High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; High-pressure geophysics and materials
Authors: Zijie Zhang | Yiming Liu | Siyu Chen | Lulu Liu | Junjie Liu | Tao Hu | Ting Xiao | Lihua Jiang | Xu Li | Xinyi Li | Xinyu Tan
Source: Surface and Coatings Technology (article, 2026-01-26)
Field: Materials Science — Surfaces, Coatings and Films

Design of superhydrophobic anti-icing coatings guided by full-process machine learning Superhydrophobic coatings hold immense potential in anti-icing applications. The preparation of superhydrophobic anti-icing coatings involves multi-parameter design such as coating components and substrate structures, which leads to high trial-and-error rates and unclear directionality in performance optimization. Here, we first report a full-process machine learning framework that integrates large language model, post-hoc explainable machine learning model, and Bayesian Optimization framework to guide the preparation of superhydrophobic coating with superior anti-icing performance. The FPMLF autonomously collected 217 sets of coating components data from 2315 published articles, constructed a regression model for coating components design based on the collected data, and achieved the high throughput screening and optimization of the complex process parameters of the substrate structure. Guided by this framework, we successfully fabricated a coating exhibiting a freezing delay time over 30-fold longer than bare substrate. This work provides systematic support and a methodological foundation for the rational design of advanced functional materials with coupled and complex performance requirements. • A full-process machine learning framework (FPMLF) was developed to design the superhydrophobic anti-icing coatings. • The FPMLF achieved data collection, prediction of coating components and optimization of substrate structure. • Through the FPMLF, a coating with a freezing delay time over 30-fold longer than bare substrate was obtained. Coating ; Substrate (aquarium) ; Bayesian optimization ; Process (computing) ; Superhydrophobic coating ; Throughput ; Component (thermodynamics) Surface Modification and Superhydrophobicity ; Solar-Powered Water Purification Methods ; Icing and De-icing Technologies
Authors: Tianmeng Huang | Xiao Shan | Hanchao Zhang | Min Zhang | Huangyue Cai | Y. Lu | Jingyang Wang | Xiaofeng Zhao
Source: ACS Applied Materials & Interfaces (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Microstructure-Mediated Inverse Design of Thermal Barrier Coatings via Interpretable, Data-Efficient Machine Learning Microstructure control is critical for thermal barrier coating (TBC) performance, yet the complex, nonlinear process-structure relationships impede predictive design. Conventional data-driven approaches are limited by scarce experimental data and poor physical interpretability in end-to-end models. Here, we present a closed-loop machine learning framework that decouples the process-property relationship by introducing microstructure as a physically interpretable intermediary, thereby enabling accurate optimization with only 45 training samples and 3 validation experiments. Key microstructure features such as equiaxed pores, unmelted regions, and cracks are quantitatively identified from scanning electron microscopy images using a deep neural network-based image recognition module, enabling automated feature extraction for subsequent modeling and optimization. The resulting forward prediction models achieve <i>R</i><sup>2</sup> values of 0.896, 0.878, and 0.727 for equiaxed pores, unmelted regions, and cracks, respectively. These models further uncover dominant mechanisms, including current-distance interactions that govern thermal energy distribution and gas flow-feed rate coupling that influence particle trajectory. The inverse design engine converts target microstructures into manufacturing parameters with 92% structural reproduction accuracy and 88% parameter prediction accuracy. Experimental validation confirms that coatings designed for low thermal conductivity achieve 35-46% reductions at 1000 °C relative to the data set average, realized with only two fabrication iterations. By explicitly decomposing the process-property relationship into interpretable process-structure and structure-property links, this framework shifts TBC development from empirical iteration into data-efficient, intelligent precision design and offers a generalizable strategy for interpretable materials design under data scarcity. Thermal barrier coating ; Inverse ; Thermal ; Inverse problem Machine Learning in Materials Science ; Thermal properties of materials ; Composite Material Mechanics