jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luca Pennati | Andong Hu | Ivy Peng | Lukas Müllender | Stefano Markidis
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy at MD throughput now poses a significant challenge: embedding neural-network inference into multi-GPU simulations retaining high-performance. In this work, we integrate the MLIP framework DeePMD-kit into GROMACS, enabling domain-decomposed, GPU-accelerated inference across multi-node systems. We extend the GROMACS NNPot interface with a DeePMD backend, and we introduce a domain decomposition layer decoupled from the main simulation. The inference is executed concurrently on all processes, with two MPI collectives used each step to broadcast coordinates and to aggregate and redistribute forces. We train an in-house DPA-1 model (1.6 M parameters) on a dataset of solvated protein fragments. We validate the implementation on a small protein system, then we benchmark the GROMACS-DeePMD integration with a 15,668 atom protein on NVIDIA A100 and AMD MI250x GPUs up to 32 devices. Strong-scaling efficiency reaches 66% at 16 devices and 40% at 32; weak-scaling efficiency is 80% to 16 devices and reaches 48% (MI250x) and 40% (A100) at 32 devices. Profiling with the ROCm System profiler shows that >90% of the wall time is spent in DeePMD inference, while MPI collectives contribute <10%, primarily since they act as a global synchronization point. The principal bottlenecks are the irreducible ghost-atom cost set by the cutoff radius, confirmed by a simple throughput model, and load imbalance across ranks. These results demonstrate that production MD with near ab initio fidelity is feasible at scale in GROMACS. Molecular dynamics ; Inference ; Domain decomposition methods ; Benchmark (surveying) ; Embedding ; Throughput ; Set (abstract data type) ; Profiling (computer programming) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Dong  Yun Shin | Joharimanitra Randrianandraina | Gayoung Cheon | Chang-Seop Hong | Jung-Hoon Lee
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Unifying CO2 Diffusion Mechanisms in Diamine-Functionalized Metal-Organic Frameworks via Quantum-Accurate Machine Learning Dynamics Diffusion ; Limiting ; Thermal diffusivity ; Molecular dynamics ; Trapping ; Surface diffusion ; Density functional theory ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Bo Liu | Qi Han | Wenxuan Wang | Lijing He | Wenjie Wang | Yanfen Cheng | Shaojun Yun | Feier Cheng | Cuiping Feng | Jinling Cao
Source: Microchimica Acta (article, 2026-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted SERS detection of pyrethroid pesticides in edible fungi using a magnetic nanosensor Nanosensor ; Deltamethrin ; Pesticide ; Pesticide residue ; Pyrethroid ; Support vector machine ; Discriminative model Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: J. P. Kim | Byungsoo Kim | Hee A Kim | Gi Baek Nam | Hyuk Jin Kim | Yoon Jung Lee | S. H. Kim | Suin Yi | Yongjo Park | Ho Won Jang
Source: InfoMat (article, 2026-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Domain engineered gallium oxide phototransistors enabling intelligent ultraviolet vision with dynamic gating Abstract Domain‐induced carrier scattering and persistent photoconductivity (PPC) remain major bottlenecks in β‐Ga 2 O 3 ‐based deep‐ultraviolet (DUV) photodetectors, limiting their use in high‐speed imaging, optical communication, and neuromorphic sensing systems. Here, we propose a materials–circuits co‐design strategy that integrates crystallographic domain engineering with pulsed‐gate modulation to overcome these challenges. Si‐doped β‐Ga 2 O 3 thin films were epitaxially grown on 6° off‐axis sapphire substrates, where atomic step edges induced single‐domain alignment, resulting in improved lateral carrier transport and reduced leakage current compared with on‐axis counterparts. Under comparable pulsed conditions, single‐domain devices exhibited faster recovery than previously reported β‐Ga 2 O 3 phototransistors, highlighting the synergistic interplay between domain‐engineered transport and dynamic gating. As a result, the devices achieved a high detectivity ( D *) of 9.17 × 10 15 Jones and a fast photoresponse of 0.7 ms under reset conditions, while maintaining stable and energy‐efficient operation under sub‐volt bias. Beyond individual devices, the optimized phototransistors were integrated into a 24 × 24 active‐pixel array for system‐level DUV imaging. Coupled with convolutional neural networks (CNNs), the array achieved accurate pattern recognition and image reconstruction, while synaptic depression and active reset processes enabled rapid afterimage suppression and image recovery. Overall, this work establishes a domain‐engineered, pulse‐modulated β‐Ga 2 O 3 phototransistor platform that unifies materials‐, device‐, and system‐level innovations, providing a scalable and energy‐efficient route toward intelligent ultraviolet vision. image Neuromorphic engineering ; Photodiode ; Transistor ; Ultraviolet ; Image sensor ; Photoconductivity ; Convolutional neural network ; Photodetector ; Reset (finance) Ga2O3 and related materials ; Transition Metal Oxide Nanomaterials ; Photocathodes and Microchannel Plates
Authors: Lei Li | Rumin Fu | Weihao Li | Zhentao Lu | Junchen Li | Zongde Wang | Zi Min | Lu-Ying Duan | Limin Lu | Fengli Qu
Source: Analytical Chemistry (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

AI-Integrated Conformal Cellulose Nanofiber-Reinforced Hydrogel Patch for Dual-Ratiometric Fluorescence Monitoring of Pyrimethanil Residues Real-time and on-site monitoring of pesticide residues remains challenging due to the lack of plant-interfaced, actively enriching, and nondestructive analytical devices. Herein, we present an intelligent, plant-conformal cellulose nanofiber (CNF)-reinforced hydrogel sensor with dual-emission fluorescence for in situ quantification of the fungicide Pyrimethanil (PMT). Unlike passive substrates, the poly(vinyl alcohol) (PVA)/CNF hybrid matrix serves as a mechanically robust, active enrichment scaffold. It exhibits a highly interconnected porous architecture and excellent flexibility (fracture strain >370%), enabling stable, conformal adhesion to complex leaf surfaces. Crucially, the CNF network acts as an active enrichment scaffold, preconcentrating PMT via specific intermolecular interactions to enhance signal intensity and response kinetics. For signal transduction, in situ synthesized Eu-MOF and coordinated carbon dots (CDs) provide a stable dual-color emission, enabling a self-referencing ratiometric mechanism intrinsically resistant to light scattering and instrumental drift. Furthermore, to achieve high-fidelity quantification, a back-propagation artificial neural network (BP-ANN) is integrated and trained on multichannel optical (RGB) and compositional data sets. This AI-driven quantification accurately maps RGB features to PMT concentrations, reducing operator bias and mitigating illumination-related interference. The resulting hydrogel patches tightly conform to diverse plant surfaces, exhibit excellent mechanical robustness, and demonstrate a low detection limit of 33.46 μg·kg<sup>-1</sup> and satisfactory recovery rates ranging from 94.0% to 104.7% in real agricultural samples. Overall, this synergistic integration of CNF-assisted chemical enrichment, dual-emission ratiometric response, and data-driven analytics establishes a robust, field-deployable platform for real-time, high-fidelity monitoring of pesticide residues, offering a promising paradigm for sustainable and intelligent agricultural diagnostics. Pyrimethanil ; Cellulose ; Detection limit ; Fluorescence ; In situ ; SIGNAL (programming language) ; Etching (microfabrication) Carbon and Quantum Dots Applications ; Polymer-Based Agricultural Enhancements ; Analytical Chemistry and Sensors
Authors: Gökhan Genç | Süheyla Koç | Mustafa Kaya
Source: Construction and Building Materials (article, 2026-03-09)
Field: Chemistry — Organic Chemistry

The deep learning method for heritage conservation: Non-destructive wood species classification in historic structures Robustness (evolution) ; Convolutional neural network ; Deep learning ; Field (mathematics) ; Identification (biology) ; Workflow ; Sustainability ; Random forest Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Wood Treatment and Properties
Authors: Guan Liu | Chenchen Chu | Yubei Zhang | Yijie Chen | Xiaolong Li | Chaolei Hua | Xudong Cheng Lihua Li | Chu Li | Mingyang Jiang | Shengyong Geng
Source: Advanced Functional Materials (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Dual‐Space Confined Magnetic Single‐Atom Nanozyme for Intelligent Point‐of‐Care Diagnostics ABSTRACT Colorimetric immunochromatographic biosensors (ICBs) are widely deployed in point‐of‐care testing (POCT). However, their application is fundamentally restricted by the limited detection sensitivity, which impedes reliable early disease diagnosis. Herein, we develop a high‐performance magnetic single‐atom nanozyme (mFeSiO 2 @PtSAN) through a dual‐space confinement strategy. This strategy, utilizing low‐temperature photochemical reduction instead of the traditional pyrolysis method, concurrently guarantees atomic dispersion, superparamagnetism, and a Pt‐N 4 Cl 2 coordination with a high Pt loading of 6.0 wt.%. The mFeSiO 2 @PtSAN shows outstanding oxidase‐like activity, as evidenced by a low Michaelis constant ( K m = 0.068 m M ) and a high maximal reaction velocity ( V max = 4.67 × 10 −7 M s −1 ). When incorporated into an ICB platform for cardiac troponin I detection, the mFeSiO 2 @PtSAN allows for visual detection at 0.01 ng/mL and attains a limit of detection of 3.3 pg/mL after catalytic amplification, which represents a 26.9‐fold improvement compared to conventional Pt nanoparticle‐based probes. Moreover, the combination with a convolutional neural network‐based image analysis system achieves a 94.7% accuracy for clinical samples. This research not only offers a scalable model for the design of magnetic single‐atom nanozymes but also presents a synergistic approach toward intelligent, high‐performance POCT by integrating advanced nanozymes with artificial intelligence‐driven analytics. Detection limit ; Scalability ; Magnetic nanoparticles ; Convolutional neural network ; Limit (mathematics) ; Nanoparticle ; Biosensor Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Rohit Goswami
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Reproducible Orchestration of Best Practices for Reaction Path Optimization with the Nudged Elastic Band The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention. Workflow ; Path (computing) ; Initialization ; Energy (signal processing) ; Orchestration ; Pipeline (software) ; Point (geometry) ; Saddle point ; Software ; Energy minimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Sen Zhai | WenChuang Liu | Jia Zhao | Kenjiro Sugio
Source: Journal of Alloys and Compounds (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Machine learning prediction of hardness and relative density in Ti/TiB2/TiC composites fabricated by spark plasma sintering Relative density ; Spark plasma sintering ; Sintering ; Composite number ; Multilayer perceptron ; Scanning electron microscope ; Support vector machine ; SPARK (programming language) Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Advanced ceramic materials synthesis
Authors: Yiting Liu | Cheng Chen | Wei Zhang | Yue Wang | Jiaxuan Xue | Wenkai Zhao | J. Stiens
Source: IEEE Journal of Selected Topics in Quantum Electronics (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Assists the Design of a W-Band Phase-Change Tunable Wire-Grid Periodic Resonant Broadband Absorber Millimeter-wave systems require absorbers with high absorption efficiency, broad bandwidth, and multi-functional tunability. However, existing devices based on composite structures and materials face design challenges: traditional methods hardly achieve all three indicators simultaneously, often sacrificing one performance for another, making "high absorption, broad bandwidth, multi-function" integration highly difficult. To overcome this fundamental trade-off, this study designs a W-band (75-110 GHz) composite absorber, integrating VO2 and graphene, and combining periodic resonant structures with a backplane-free phase-change wire-grid architecture. When integrated into the periodic resonant structure, the VO2 wire-grid provides thermal ‘on-off’ switching of absorption, whereas graphene offers electrical tunability— both achieved by modulating the effective surface conductivity and thus the impedance matching condition. Guided by an XGBoost-based closed-loop inverse design framework, the XGBoost model optimizes parameters, with convergence achieved after four closed-loop simulation iterations. Results show the VO2 wire-grid absorber performs excellently at 90° incidence: it achieves near-perfect absorption (> 99%) over a 21 GHz sub-band (82-103 GHz), showing an 80% improvement in average absorption compared to copper(Cu) backplanes, under identical unit-cell geometry and simulation conditions, with each configuration independently optimized for peak performance. Graphene provides a maximum modulation amplitude of depth of 68%. Innovations of this study include constructing a periodic resonant wire-grid structure that abandons traditional backplanes and uses wire-grids to expand loss areas, enabling dual-mode multi-physics control through co-integration of VO2 and graphene, and establishing a machine learning closed-loop framework for parameter optimization. It explores a new paradigm for multi-functional tunable absorbers, providing high-performance solutions for MMW systems such as radar, communications, and biomedical imaging. Broadband ; Convergence (economics) ; Absorption (acoustics) ; Matching (statistics) ; Impedance matching ; Electrical impedance ; Graphene ; Modulation (music) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Georg Weingrill | Georg Schinnerl
Source: Österreichische Wasser- und Abfallwirtschaft (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Optische Sortierung von Post-Consumer-Aluminiumschrott in Legierungsgruppen mittels KI als Alternative zu XRF- und XRT-Technologien CLARITY ; Secondary prevention Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Gia-Wei Chern | Yunhao Fan | Sheng Zhang | Puhan Zhang
Source: Journal of Magnetism and Magnetic Materials (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems Non-equilibrium thermodynamics ; Spintronics ; Magnetization ; Spin (aerodynamics) ; Magnetization dynamics ; Spin engineering ; Magnetic field ; Heisenberg model Machine Learning in Materials Science ; Quantum many-body systems ; Physics of Superconductivity and Magnetism
Authors: Yulong Han | Huanhuan Lu | Haili LIU | Hengye Chen | Wei Lan | Xinming Lv | Wanjun Long | Haiyan Fu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-01-24)
Field: Chemistry — Inorganic Chemistry

Machine learning-assisted ratiometric fluorescent sensing platform based on antenna effect in Tb-doped metal–organic framework for visual quantification of enoxacin Fluorescence ; Detection limit ; Enoxacin ; Ultraviolet ; Linear range ; Antenna (radio) ; Excitation ; Range (aeronautics) Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications
Authors: Chaeyeon Park | Min-Gu Sim | Seohyeon Kim | Geon-soo Ha | Balachandran Manavalan | Gahyun Baek
Source: Bioresource Technology Reports (article, 2026-03-13)
Field: Materials Science — Biomaterials

Stress condition optimization for maximizing polyhydroxybutyrate accumulation in Cupriavidus necator via experimental and ensemble machine learning approaches Cupriavidus necator ; Polyhydroxybutyrate ; Ensemble learning ; Stress (linguistics) ; Ensemble forecasting ; Environmental stress biodegradable polymer synthesis and properties ; Polymer Foaming and Composites ; Polymer Science and PVC
Authors: Humberto Morales | Estefanía Aguirre-Zapata | Xudong SHI | Fernando di Sciascio | Adriana Amicarelli
Source: Journal of Food Engineering (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

A Bayesian Gaussian Process Regression soft sensor for industrial sugar crystallization monitoring Soft sensor ; Gaussian process ; Support vector machine ; Covariance ; Kriging ; Artificial neural network ; Regression ; Covariance matrix Crystallization and Solubility Studies ; Fault Detection and Control Systems ; Spectroscopy and Chemometric Analyses
Authors: Sanja Martinez | Bojan Hudec | Izabela Martinez | Ivana Šoić
Source: Progress in Organic Coatings (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Unsupervised clustering of EIS spectra for distinguishing degradation states of polymer coatings Degradation (telecommunications) ; Polymer ; Cluster analysis ; Spectral line ; Polymer degradation Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Epoxy Resin Curing Processes
Authors: Chen Huang | Junting Zhao | Yu Zhang | Linghui Gong
Source: Cryogenics (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Optimizing hydrogen adsorption capacity in porous carbon through CNN and SHAP analysis Adsorption ; Hydrogen ; Porosity ; Hydrogen storage ; Carbon fibers ; Langmuir ; Bar (unit) Hydrogen Storage and Materials ; Mesoporous Materials and Catalysis ; Adsorption and biosorption for pollutant removal
Authors: H. Shen | T. Wang | X. Yao | O. Wu | C. Xie | C. Qian | H. Y. Chen
Source: Open MIND (preprint, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network Narrowband perfect absorbers are interesting for spectrum sensing, molecular detection, and infrared imaging. However, their design remains constrained by intuitive, iterative methods that lack flexibility, while also facing challenges in multi-objective optimization. Here, we introduce a deep learning-enabled inverse-design framework that overcomes these limitations through a conditional Wasserstein Generative Adversarial Network (WGAN). The main contribution of this work is a dual-channel image encoding scheme that jointly represents the geometry and thickness of a Si$_3$N$_4$ meta-layer, facilitating the network to learn the distribution of viable structures for a target optical response. This approach naturally solves the inherent ``one-to-many'' design issue, giving a diverse portfolio of functional candidates from a single input spectrum. The designed absorbers achieve exceptional spectral fidelity, with resonance peak errors below 5 nm, a mean squared error (MSE) on the order of $10^{-3}$, and the capacity to produce over 10 distinct, high-performance designs per target. Furthermore, we demonstrate the model's robustness under oblique illumination, showing that it can be efficiently fine-tuned to maintain spectral accuracy across incidence angles from $10^\circ$ to $40^\circ$ by transfer learning, thus extending its practical utility to non-normal operating conditions. Full-wave simulations confirm that the generated geometries support a hybrid plasmonic-dielectric resonance, leading to near-perfect absorption and strong near-field enhancement. Our study provides a robust, physics-aware design paradigm that moves beyond conventional parametric optimization. The introduced framework establishes a versatile platform for the on-demand inverse design of advanced photonic devices for sensing, spectroscopy, and optical signal processing. Robustness (evolution) ; Narrowband ; Parametric statistics ; Inverse ; Photonics ; Optical filter ; Inverse problem ; Encoding (memory) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Acoustic Wave Phenomena Research
Authors: B M Spandana | M R Bhavana | V R Sinija | C K Sunil
Source: Next research. (article, 2026-03-07)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy in combating seafood fraud: Authentication and adulterant detection Adulterant ; Traceability ; Chemometrics ; Authentication (law) ; Preprocessor ; Calibration ; Hyperspectral imaging ; Supply chain ; Digital polymerase chain reaction Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Food Supply Chain Traceability
Authors: Xiaolin Song | Zhao Da | Xin Zhang | Fangfang Xu | Zhenzhong Wang | Wenhua Jiao
Source: Chemometrics and Intelligent Laboratory Systems (article, 2026-02-14)
Field: Chemistry — Analytical Chemistry

Moisture monitoring in fluidized bed drying of Cassia Twig granules: A TCN–GRU approach with near-infrared spectroscopy-assisted label expansion Water content ; Fluidized bed ; Twig ; Moisture ; Process (computing) ; Correlation coefficient ; Mean squared error ; Feature (linguistics) ; Process analytical technology Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Food Supply Chain Traceability
Authors: Fahimeh Ahmadi | Mohsen Hajihassani | Rosli Hussin | S.K. Ghoshal
Source: Journal of Alloys and Compounds (article, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Structural and optical attributes of erbium ions-doped magnesium-zinc-sulfophosphate glass: Experimental and GEP-driven predictive model evaluation Amorphous solid ; Raman spectroscopy ; Erbium ; Band gap ; Ion ; Natural bond orbital ; Diffraction ; Analytical Chemistry (journal) ; Photonics Glass properties and applications ; Phase-change materials and chalcogenides ; Luminescence Properties of Advanced Materials