jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akash Kumar | Sumaiya Qasim | Ashwani Sharma | Dalapathi Gugulothu | Surajpal Verma
Source: Current Pharmaceutical Design (article, 2026-03-12)
Field: Materials Science — Biomaterials

Machine Learning Algorithm for Nanomedicine: AI Curated Nanocarriers for Cancer Treatment Cancer remains a major global health challenge due to its genetic variability and intricate molecular mechanisms, which complicate the development of effective therapies. This review elucidates the integration of AI-driven methodologies in nanoparticle (NP) design, optimizing drug delivery systems (DDSs) for targeted cancer therapy. AI's predictive analytics facilitate the rational design of nanocarriers, enhancing drug bioavailability, optimizing pharmacokinetics, and improving tumor penetration. The incorporation of machine learning (ML) models accelerates NP fabrication, enabling real-time simulation of tumor dynamics and drug release kinetics. Furthermore, AI-powered platforms, such as EVOnano, simulate in silico tumor microenvironments to refine nanocarrier functionalities. This synergy fosters the development of next-generation smart therapeutics, wherein adaptive nanomedicines exhibit enhanced tumor specificity while mitigating systemic toxicity. Challenges, such as nanoparticle scalability, AI interpretability, and biological heterogeneity, persist, necessitating interdisciplinary advances. Nevertheless, AI-assisted nanomedicine signifies a paradigm shift towards highly efficacious, patient-tailored cancer interventions, revolutionizing treatment landscapes and propelling oncology into a new frontier of data-driven, precision-based therapeutics. Nanocarriers ; Nanomedicine ; In silico ; Cancer treatment ; Drug delivery ; Cancer ; Cancer therapy ; Precision medicine ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Artificial Intelligence in Healthcare and Education
Authors: Ihtesham Ibn Malek | Koushik Sarkar
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Accelerated, Cost-Effective, and Accurate Prediction of Carbon Nanotube Stress–Strain Characteristics under Thermal Vibration Carbon nanotube ; Thermal ; Chirality (physics) ; Vibration ; Dynamics (music) ; Mean squared error ; Ensemble learning ; Stress (linguistics) Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Linxuan Li | Wenjia Wei | Yi Lu | Wenwen Zhang | Yanlong Zhang | Wei Zhao
Source: ArXiv.org (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

BSoNet: Deep Learning Solution for Optimizing Image Quality of Portable Backscatter Imaging Systems Portable backscatter imaging systems (PBI) integrate an X-ray source and detector in a single unit, utilizing Compton scattering photons to rapidly acquire superficial or shallow structural information of an inspected object through single-sided imaging. The application of this technology overcomes the limitations of traditional transmission X-ray detection, offering greater flexibility and portability, making it the preferred tool for the rapid and accurate identification of potential threats in scenarios such as borders, ports, and industrial nondestructive security inspections. However, the image quality is significantly compromised due to the limited number of Compton backscattered photons. The insufficient photon counts result primarily from photon absorption in materials, the pencil-beam scanning design, and short signal sampling times. It therefore yields severe image noise and an extremely low signal-to-noise ratio, greatly reducing the accuracy and reliability of PBI systems. To address these challenges, this paper introduces BSoNet, a novel deep learning-based approach specifically designed to optimize the image quality of PBI systems. The approach significantly enhances image clarity, recognition, and contrast while meeting practical application requirements. It transforms PBI systems into more effective and reliable inspection tools, contributing significantly to strengthening security protection. Image quality ; Backscatter (email) ; Flexibility (engineering) ; Reliability (semiconductor) ; Noise (video) ; Deep learning ; Transmission (telecommunications) ; Compton scattering Radiation Shielding Materials Analysis ; Radiation Detection and Scintillator Technologies ; Random lasers and scattering media
Authors: Bei Jing | Miao Cui | Chenming Ma | Wei Xia | Xinghai Wang | Yahui Wen | Jiaxi Wang | Ziyan Zhou | Yifei Peng | Hongtao Lei | Jing Jin | Minmin Ai | Lidong Wu
Source: Analytical Chemistry (article, 2026-04-09)
Field: Chemistry — Spectroscopy

Antioxidative and Robust Fluorescent Hydrogel for Deep Learning-Assisted Non-Destructive Detection of Hg <sup>2+</sup> Flexible fluorescent hydrogels offer great potential for on-site heavy-metal sensing, but their practical use is often limited by a fundamental trade-off: materials that provide strong fluorescence responses typically suffer from poor stability in oxidative environments, while hydrogel matrices with good robustness or adhesion rarely support reliable fluorescence signaling. Here, we introduce a phycocyanin-polyacrylamide (PC-PAAM) hydrogel that resolves this conflict by integrating high mechanical flexibility and strong underwater adhesion with stable red fluorescence and intrinsic antioxidative protection. PC acts simultaneously as a fluorescent cross-linker and ROS-scavenging component, enabling the hydrogel to maintain signal integrity while undergoing rapid fluorescence quenching upon Hg<sup>2+</sup> coordination. The detection limit of the sensor achieves 6.21 nM. It demonstrates conformal contact on irregular biological surfaces, enabling nondestructive detection on fish. Coupled with a deep-learning model trained on hydrogel fluorescence images, the system further enables accurate, portable, and visual quantification of Hg<sup>2+</sup> contamination using smartphone imaging. This work provides a flexible, adhesive, and intelligent sensing platform for environmental and food-safety monitoring. Fluorescence ; Self-healing hydrogels ; Robustness (evolution) ; Detection limit ; Flexibility (engineering) ; Quenching (fluorescence) Molecular Sensors and Ion Detection ; Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials
Authors: Vahidullah Tac | Ellen Kuhl
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Materials Science — Materials Chemistry

Generative AI for material design: A mechanics perspective from burgers to matter Generative artificial intelligence offers a new paradigm to design matter in high-dimensional spaces. However, its underlying mechanisms remain difficult to interpret and limit adoption in computational mechanics. This gap is striking because its core tools-diffusion, stochastic differential equations, and inverse problems-are fundamental to the mechanics of materials. Here we show that diffusion-based generative AI and computational mechanics are rooted in the same principles. We illustrate this connection using a three-ingredient burger as a minimal benchmark for material design in a low-dimensional space, where both forward and reverse diffusion admit analytical solutions: Markov chains with Bayesian inversion in the discrete case and the Ornstein-Uhlenbeck process with score-based reversal in the continuous case. We extend this framework to a high-dimensional design space with 146 ingredients and 8.9x10^43 possible configurations, where analytical solutions become intractable. We therefore learn the discrete and continuous reverse processes using neural network models that infer inverse dynamics from data. We train the models on only 2,260 recipes and generate one million samples that capture the statistical structure of the data, including ingredient prevalence and quantitative composition. We further generate five new burgers and validate them in a restaurant-based sensory study with 100 participants, where three of the AI-designed burgers outperform the classical Big Mac in overall liking, flavor, and texture. These results establish diffusion-based generative modeling as a physically grounded approach to design in high-dimensional spaces. They position generative AI as a natural extension of computational mechanics, with applications from burgers to matter, and establish a path toward data-driven, physics-informed generative design. Generative grammar ; Generative model ; Artificial neural network ; Markov chain ; Statistical mechanics ; Limit (mathematics) ; Bayesian probability ; Process (computing) ; Generative Design Machine Learning in Materials Science ; Design Education and Practice ; Music Technology and Sound Studies
Authors: Kang Xu | ZhengMing Sun | Wenwang Wu
Source: Materials Science and Engineering A (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

A three-stage learning framework for sparse annotation-based segmentation and quantification of metallic microstructures Segmentation ; Pattern recognition (psychology) ; Feature (linguistics) ; Image segmentation ; Noise (video) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Domain Adaptation and Few-Shot Learning
Authors: Yanli Liu | Yindan Fu | Jianzhong Wu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Energy Education: Synergizing Technological Innovation with Human Capital Development for a Sustainable Future - For the Applied Energy 50th Anniversary Special Issue Nexus (standard) ; Energy (signal processing) ; Transformative learning ; Enabling ; Human capital ; Energy engineering ; Energy management ; Efficient energy use ; Energy market Machine Learning in Materials Science ; Integrated Energy Systems Optimization ; Sustainability in Higher Education
Authors: Qi Zhang | Sicong Wan | Lei Wang
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Ab initio simulation of the first-order proton-ordering transition in water ice Proton ordering in water ice is a paradigmatic order-disorder transition in a locally constrained system. The ice rules require exactly two hydrogens close to each oxygen, restricting the disorder to an exponentially large yet strongly correlated manifold of hydrogen-bond configurations. Within this constrained space, meV-scale energy differences drive the transition from disordered ice Ih to ordered ice XI, while distinct configurations are separated by eV-scale barriers. These barriers hinder equilibration in experiments, and efficient sampling of this space with the required energy accuracy has remained a long-standing challenge in simulation. We address this by combining a machine learning interatomic potential with loop updates that preserve the ice rules and continuous updates of atomic coordinates, enabling equilibrium sampling with ab initio accuracy and capturing configurational entropic effects. In systems of up to 360 water molecules, with over 10^6 samples retained per temperature point, the simulations reveal clear first-order transition signatures at 83 K: a negative Binder cumulant, a bimodal potential energy distribution, and a sharp step in the lattice aspect ratio. Nuclear quantum effects are estimated to lower the transition temperature by approximately 20 K, bringing the prediction closer to the experimental value of 72 K. Ab initio ; Ab initio quantum chemistry methods ; Lattice (music) ; Interatomic potential ; Potential energy ; Sampling (signal processing) ; Space (punctuation) ; Molecular dynamics ; Quantum Material Dynamics and Properties ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Linxuan Li | Wenjia Wei | Yi Lu | Wenwen Zhang | Yanlong Zhang | Wei Zhao
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

BSoNet: Deep Learning Solution for Optimizing Image Quality of Portable Backscatter Imaging Systems Portable backscatter imaging systems (PBI) integrate an X-ray source and detector in a single unit, utilizing Compton scattering photons to rapidly acquire superficial or shallow structural information of an inspected object through single-sided imaging. The application of this technology overcomes the limitations of traditional transmission X-ray detection, offering greater flexibility and portability, making it the preferred tool for the rapid and accurate identification of potential threats in scenarios such as borders, ports, and industrial nondestructive security inspections. However, the image quality is significantly compromised due to the limited number of Compton backscattered photons. The insufficient photon counts result primarily from photon absorption in materials, the pencil-beam scanning design, and short signal sampling times. It therefore yields severe image noise and an extremely low signal-to-noise ratio, greatly reducing the accuracy and reliability of PBI systems. To address these challenges, this paper introduces BSoNet, a novel deep learning-based approach specifically designed to optimize the image quality of PBI systems. The approach significantly enhances image clarity, recognition, and contrast while meeting practical application requirements. It transforms PBI systems into more effective and reliable inspection tools, contributing significantly to strengthening security protection. Image quality ; Backscatter (email) ; Flexibility (engineering) ; Reliability (semiconductor) ; Noise (video) ; Deep learning ; Transmission (telecommunications) ; Compton scattering Radiation Shielding Materials Analysis ; Radiation Detection and Scintillator Technologies ; Random lasers and scattering media
Authors: Yue Fang | Guangyao Huang | Sensen Hao | Bin Chen | Jiqing Hao | Ying Wang (11406) | Nan Liu | Zhipeng Li | Wen Cao | M. G. Zhao | Hanghan Jiang | Lanxin Bao | J. Wang | Hongzhi Wang
Source: Biosensors and Bioelectronics (article, 2026-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A programmable matrix-robust plasmonic MetaRing biosensor for rapid SERS-based chemotherapeutic response profiling Biosensor ; Profiling (computer programming) ; Plasmon ; Biochip ; Analyte Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Ville Uski | C. Ballard | Maria Fando | Ronan M. Keegan | Eugene Krissinel | A. Lebedev | David G. Waterman | Jamison Wills
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Innovations and enhancements in the upcoming CCP4 Release 10 The forthcoming CCP4 [1] version 10 delivers key infrastructural and functional upgrades to support macromolecular structure determination.The suite now runs on Python 3.11 and continues to evolve its two main front ends: ccp4i2, the graphical desktop interface, and CCP4 Cloud, the web-based platform that integrates with Moorhen -a browser-based interactive graphical model builder like Coot [2].Among the new tools, NucleoFind [3] stands out as a deep-learning-based solution for segmenting nucleic acid electron density maps.It identifies phosphate, sugar, and base atoms, significantly enhancing model-building speed and quality.NucleoFind integrates with tools like ModelCraft [4] and outperforms traditional methods, even in challenging, noisy datasets.PanDDA 2, a rewrite of PanDDA [5], is also included in this release.It introduces two major methodological advances: dynamic selection of dataset subsets to optimize statistical contrast for each test dataset, enabling better handling of subtle heterogeneity, and automated event model building with ranking based on fragment model quality.These improvements enhance the detection and prioritization of ligand-binding events.CCP4 continues to support a comprehensive pipeline for macromolecular structure solution, including tools for data processing, molecular replacement, experimental phasing, model building, and validation.Ongoing development focuses on leveraging predicted models in phasing (MRParse, SliceNDice).MrParse and MrBUMP have been updated to search the entire AlphaFold database for search models in molecular replacement when used through CCP4 Cloud.Monthly updates ensure the suite remains at the forefront of crystallographic software innovation. Python (programming language) ; Suite ; Prioritization ; Ranking (information retrieval) ; Key (lock) ; Software ; Pipeline (software) ; Event (particle physics) ; Graphical user interface Enzyme Structure and Function ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: James K. Ebajemito | Lauren Edward | Matthew Napleton | Joel Strickland | G. J. Conduit | M. Ghisoni | Stacey McKenna | Abhi Naha | Allen Griffiths
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Digital Accelerators as Catalysts for Scientific Research Innovation Transformative learning ; Aerospace ; Paradigm shift ; Scientific progress ; Digital transformation ; Best practice ; Business model ; Mechanism (biology) Machine Learning in Materials Science ; Digital Transformation in Industry ; Artificial Intelligence in Healthcare and Education
Authors: Kamyar Barakati | Boris N. Slautin | Utkarsh Pratiush | Hiroshi Funakubo | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2026-04-05)
Field: Materials Science — Materials Chemistry

PATHFINDER: Multi-objective discovery in structural and spectral spaces Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workflows optimize a single predefined objective and tend to converge prematurely on familiar responses, overlooking rare but scientifically important states. More broadly, the challenge is not only where to measure next, but how to coordinate exploration across structural, spectral, and measurement spaces under finite experimental budgets while balancing target-driven optimization with novelty discovery. Here we introduce PATHFINDER, a framework for autonomous microscopy that combines novelty driven exploration with optimization, helping the system discover more diverse and useful representations across structural, spectral, and measurement spaces. By combining latent space representations of local structure, surrogate modeling of functional response, and Pareto-based acquisition, the framework selects measurements that balance novelty discovery in feature and object space and are informative and experimentally actionable. Benchmarked on pre acquired STEM EELS data and realized experimentally in scanning probe microscopy of ferroelectric materials, this approach expands the accessible structure property landscape and avoids collapse onto a single apparent optimum. These results point to a new mode of autonomous microscopy that is not only optimization-driven, but also discovery-oriented, broad in its search, and responsive to human guidance. Novelty ; Workflow ; Property (philosophy) ; Object (grammar) ; Feature (linguistics) ; Measure (data warehouse) ; Novelty detection ; Point (geometry) ; Characterization (materials science) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Elamvazhudi Balasubramanian | S. Kulothungan | Sathish Kumar Sankar | Nivethan Pazhani
Source: Journal of Reinforced Plastics and Composites (article, 2026-01-30)
Field: Materials Science — Polymers and Plastics

Nanoclay influence on hygrothermal aging of corn husk–glass fiber hybrid composites This work investigates the hygrothermal aging behavior of sustainable hybrid composites composed of corn husk, E-glass reinforcement, and epoxy matrix modified with organo-montmorillonite nanoclay (0–2 wt.%). Natural fibers reinforced hybrid composites were exposed to acidic, neutral, and seawater conditions for 60 days, to assess their environmental endurance. Natural fibers were incorporated in various forms included sheet, powder, and fibers. The research reveals the effect of incorporating nanoclay into the epoxy on hygrothermal aging of hybrid composites. Nanoclay additions into epoxy considerably reduced the moisture absorption percentage and decelerating mechanical properties deterioration rate. The sheet-based reinforced composites demonstrated greater long-term retention in aggressive conditions and fiber based composites offered higher initial strength. The study tackles important issues in the durability of natural fiber composites while advancing the value adding of agricultural waste. This work provides tensile properties decay models which offers practical guidance for designing sustainable composites for chemical, marine, and construction applications. In addition, this work provides open avenues for future research on machine learning models to predict durability performance under various environmental conditions. Epoxy ; Durability ; Natural fiber ; Ultimate tensile strength ; Moisture ; Fiber ; Absorption of water ; Composite number Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Advanced Cellulose Research Studies
Authors: Kamyar Barakati | Boris N. Slautin | Utkarsh Pratiush | Hiroshi Funakubo | Sergei V. Kalinin
Source: arXiv (Cornell University) (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

PATHFINDER: Multi-objective discovery in structural and spectral spaces Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workflows optimize a single predefined objective and tend to converge prematurely on familiar responses, overlooking rare but scientifically important states. More broadly, the challenge is not only where to measure next, but how to coordinate exploration across structural, spectral, and measurement spaces under finite experimental budgets while balancing target-driven optimization with novelty discovery. Here we introduce PATHFINDER, a framework for autonomous microscopy that combines novelty driven exploration with optimization, helping the system discover more diverse and useful representations across structural, spectral, and measurement spaces. By combining latent space representations of local structure, surrogate modeling of functional response, and Pareto-based acquisition, the framework selects measurements that balance novelty discovery in feature and object space and are informative and experimentally actionable. Benchmarked on pre acquired STEM EELS data and realized experimentally in scanning probe microscopy of ferroelectric materials, this approach expands the accessible structure property landscape and avoids collapse onto a single apparent optimum. These results point to a new mode of autonomous microscopy that is not only optimization-driven, but also discovery-oriented, broad in its search, and responsive to human guidance. Novelty ; Workflow ; Property (philosophy) ; Object (grammar) ; Feature (linguistics) ; Measure (data warehouse) ; Novelty detection ; Point (geometry) ; Characterization (materials science) Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications