jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bojana Koteska | Anastas Mishev | Ljupco Pejov
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

RESTful web service for fitting repulsive potentials in density-functional tight-binding with Gaussian process regression: Gaussian API Upload ; Workflow ; Gaussian process ; Gaussian ; Web service ; Process (computing) ; Service (business) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Loushambam Samananda Singh | Chabungbam Satyananda Singh | Thounaojam Premlata
Source: Molecular Imaging and Biology (article, 2026-03-16)
Field: Materials Science — Biomaterials

Magnetically Controlled Nanocarriers for Site-Specific Drug Delivery and Theranostics in Cancer Precision Medicine Precision medicine ; Nanocarriers ; Drug delivery ; Cancer ; Targeted drug delivery ; Delivery system ; Drug Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Characterization and Applications of Magnetic Nanoparticles
Authors: Nicholas E. Jackson
Source: ChemRxiv (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

A Theory of Electronic Structure for Coarse-Grained Resolutions We present a stochastic diabatic projection method for constructing electronic structure models that operate at coarse-grained (CG) molecular resolutions. By minimizing the relative entropy of joint electronic-nuclear distributions between the fine-grained and CG resolutions, we derive a stochastic effective Hamiltonian that decomposes CG configuration dependent interactions into deterministic means and residual fluctuations. This framework bridges downfolding theory with data-driven CG modeling, enabling the accurate reproduction of electronic properties renormalized over eliminated nuclear and electronic degrees of freedom. Electronic structure ; Diabatic ; Hamiltonian (control theory) ; Entropy (arrow of time) ; Stochastic process ; Residual ; Minification Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Block Copolymer Self-Assembly
Authors: Arghya Paria | Md Ali Mujtaba | Sonam M. Gandhi | Vipin Saini | Devesh U. Kapoor | Dikshitkumar Modi
Source: Naunyn-Schmiedeberg s Archives of Pharmacology (article, 2026-03-09)
Field: Materials Science — Biomaterials

Advanced nanoplatforms for oxaliplatin-based breast cancer therapy: targeted delivery, stimuli-responsive design, and translational challenges Nanocarriers ; Breast cancer ; Oxaliplatin ; Cancer ; Malignancy ; Targeted therapy ; Taxane ; Nanomedicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Intraperitoneal and Appendiceal Malignancies
Authors: Chang Li | Wenfeng Xu | Hang Yang | Yong Li | Lili Zhang | Ziwei Chen | Shuanghu Wang | Yibo Xie | Hexin Li | Ye Liu | Yayu Li | Zebei Lu | Chunqing Zhang | Xue Yu | Dapeng Dai | Peng Jin | Fei Xiao
Source: ACS Catalysis (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model Large language models (LLMs) have demonstrated their limitations in addressing the design of active proteins that rely on intricate intramolecular interactions, particularly in the engineering of biocatalysts. Conducting real-world studies from targeted laboratory assays has become the de facto standard for artificial intelligence (AI) research in complex biological tasks. In this study, we present a standardized strategy using function-targeted models to decode the subtle effect of sequence variations on the function. Unlike affinity-oriented protein–protein interaction studies using LLMs, our model targets the specific functional interpretation, thereby guiding enzyme evolution. We established the VERnet model using deep mutation scanning data that underwent self-distillation, achieving an optimal accuracy of 93.5% for interpreting CYP2C9 variants. Through directed evolution at conserved positions enhanced by generative AI, we identified multiple CYP2C9 variants exhibiting a broad range of functional alterations. Additionally, a fine-tuned model optimized by AlphaFold3 significantly improved the prediction of variants involving the substitution of two amino acids. Molecular dynamics simulations revealed the structural and dynamic features of the catalytic alterations in evolved variants. The in vitro validation of metabolic activity strongly corroborated the in silico predictions, highlighting the substantial potential of AI models in predicting functional evolution. In silico ; Directed Molecular Evolution ; Sequence (biology) ; Generative model ; Virtual screening ; Generative grammar ; Selection (genetic algorithm) ; Mutation Machine Learning in Materials Science ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Mediterranean Journal of Medical Research | S Revathi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-14)
Field: Materials Science — Biomaterials

Molecularly targeted theranostics in neurology and neuroinflammation: Current status and future directions By combining imaging and targeted therapy into a single platform, theranostics, an emerging field in neurological diseases, has the potential to completely transform diagnosis and treatment. Alzheimer's disease, Parkinson's disease, multiple sclerosis, glioblastoma, epilepsy, and other neurological conditions pose a number of complex challenges, such as difficulties in early diagnosis, restrictions in the delivery of drugs across the blood-brain barrier, and a lack of real-time therapeutic monitoring. Molecular imaging methods, such as PET and MRI, are utilized in theranostic approaches, along with drug delivery systems based on nanotechnology that can cross the blood brain barrier. Functionalized nanoparticles improve personalized medicine by enabling accurate targeting and concurrent imaging and treatment. Amyloid-PET tracers and therapeutic agent-loaded nanocarriers are two disease-specific theranostic approaches that have shown promise in preclinical and clinical research. For increased accuracy and effectiveness, future directions focus on combining state-of-the-art technologies, such as artificial intelligence and CRISPR-Cas9 gene editing. Theranostics holds revolutionary potential for managing neurological disorders by enabling early detection, personalized therapy, and dynamic treatment monitoring, despite translational challenges related to blood-brain barrier penetration, safety, regulatory barriers, and cost. To advance clinical outcomes in neurology and move these innovations from the bench to the bedside, more interdisciplinary research is essential. Personalized medicine ; Nanocarriers ; Bench to bedside ; Neurology ; Precision medicine ; Neuroimaging ; Translational research ; Drug delivery Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; S100 Proteins and Annexins
Authors: Mediterranean Journal of Medical Research | S Revathi
Source: Open MIND (article, 2026-02-14)
Field: Materials Science — Biomaterials

Molecularly targeted theranostics in neurology and neuroinflammation: Current status and future directions By combining imaging and targeted therapy into a single platform, theranostics, an emerging field in neurological diseases, has the potential to completely transform diagnosis and treatment. Alzheimer's disease, Parkinson's disease, multiple sclerosis, glioblastoma, epilepsy, and other neurological conditions pose a number of complex challenges, such as difficulties in early diagnosis, restrictions in the delivery of drugs across the blood-brain barrier, and a lack of real-time therapeutic monitoring. Molecular imaging methods, such as PET and MRI, are utilized in theranostic approaches, along with drug delivery systems based on nanotechnology that can cross the blood brain barrier. Functionalized nanoparticles improve personalized medicine by enabling accurate targeting and concurrent imaging and treatment. Amyloid-PET tracers and therapeutic agent-loaded nanocarriers are two disease-specific theranostic approaches that have shown promise in preclinical and clinical research. For increased accuracy and effectiveness, future directions focus on combining state-of-the-art technologies, such as artificial intelligence and CRISPR-Cas9 gene editing. Theranostics holds revolutionary potential for managing neurological disorders by enabling early detection, personalized therapy, and dynamic treatment monitoring, despite translational challenges related to blood-brain barrier penetration, safety, regulatory barriers, and cost. To advance clinical outcomes in neurology and move these innovations from the bench to the bedside, more interdisciplinary research is essential. Personalized medicine ; Nanocarriers ; Bench to bedside ; Neurology ; Precision medicine ; Neuroimaging ; Translational research ; Drug delivery Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; S100 Proteins and Annexins
Authors: Ricardo Flores‐Cruz | Lena Ruiz Azuara | Hélder A. Santos
Source: Journal of Controlled Release (article, 2026-03-02)
Field: Materials Science — Surfaces, Coatings and Films

The multiverse of zebrafish within the nanoworld The zebrafish (Danio rerio) has evolved into a strategic, high-throughput platform, bridging the critical gap between in vitro assays and costly mammalian studies in nanomedicine development. This is underscored by its prolific adoption, with over 1130 studies published in the last five years. The model's high genetic homology, optical transparency, and rapid development offer an ethically favorable system for the high-throughput assessment of nanotoxicity, biodistribution, and therapeutic efficacy. This review positions the zebrafish as an indispensable intermediate filter to prioritize lead candidates and deconvolute mechanistic pathways for nanomaterials. Critically, the model is best suited for preliminary in vivo evaluation; its findings necessitate subsequent validation in established mammalian models prior to any human translation. Systematically reviewing the literature, we organize nanomaterials by chemical composition and map their respective applications to different disease areas, analyzing the type of nanomaterial and its properties and therapeutic effectiveness. We detail how its integration with automated imaging, microinjection, and artificial intelligence (AI) is powering sophisticated high content screening pipelines, drastically reducing preclinical costs and timelines and with the possibility of developing personalized medicine. By improving predictive validity and serving as a bridge between in vitro trials and preclinical studies in higher-order animals, the zebrafish model streamlines the translation pathway, accelerating the development of therapies within the nanoworld. Zebrafish ; Danio ; Timeline ; Model system ; Nanomedicine ; Bridge (graph theory) ; Nonhuman primate Polymer Surface Interaction Studies ; Diatoms and Algae Research ; Zebrafish Biomedical Research Applications
Authors: Jean claude Perez
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-31)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" By Jean-Claude Perez , PhD Mathematics & Computer Science, Bordeaux University. Retired IBM Artificial Intelligence European Research Centre, Montpellier Luc Montagnier Foundation. jeanclaudeperez2@gmail.com ( mailto:jeanclaudeperez2@gmail.com) @JCPEREZCODEX ORCID https://orcid.org/0000-0001-6446-2042 Mathematical Interpretation & Math Formula : ($c(p) = 2 \cdot Int(\frac{p+2}{2})^2$) Discovered by Jean-Claude Perez, the periodic table can be extended to predict the structure of matter in hypothetical, deep layers 1-16. This deterministic model demonstrates that the number of elements grows in predictable, symmetrical pairs, where each period $p$ contains a specific capacity $c(p)$ of elements 4-6. Abstract Jean-Claude Perez’s "Generic Equation of Mendeleev" reveals a deterministic mathematical law governing atomic structure. Specifically, the concise writing of the equation—c(p)=2mp=2np2—shows direct links to the magnetic (m) and principal (n) quantum numbers. Central to the formula is the number "2," acting as the key of absolute symmetry. The model aligns perfectly with the four fundamental quantum numbers, viewing the table as an architectural blueprint rather than empirical data. By calculating the capacity of "virtual" layers, the framework extends beyond known radioactive elements into hypothetical periods. This growth is visualized through 3D "diamonds-like" crystals, where matter organizes according to a strict numerical logic of whole numbers. Mathematical structure ; Principal (computer security) ; Interpretation (philosophy) ; Symmetry (geometry) ; Mathematical logic ; Mathematical theory ; Mathematical model ; Table (database) History and advancements in chemistry ; Quasicrystal Structures and Properties ; Graph theory and applications
Authors: Jean claude Perez
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-31)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" Is this Mendeleev predictive law UNIVERSAL (ie. anywhere in the UNIVERSE)? Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" By Jean-Claude Perez , PhD Mathematics & Computer Science, Bordeaux University. Retired IBM Artificial Intelligence European Research Centre, Montpellier Luc Montagnier Foundation. jeanclaudeperez2@gmail.com ( mailto:jeanclaudeperez2@gmail.com) @JCPEREZCODEX ORCID https://orcid.org/0000-0001-6446-2042 Mathematical Interpretation & Math Formula : ($c(p) = 2 \cdot Int(\frac{p+2}{2})^2$) Discovered by Jean-Claude Perez, the periodic table can be extended to predict the structure of matter in hypothetical, deep layers 1-16. This deterministic model demonstrates that the number of elements grows in predictable, symmetrical pairs, where each period $p$ contains a specific capacity $c(p)$ of elements 4-6. Abstract Jean-Claude Perez’s "Generic Equation of Mendeleev" reveals a deterministic mathematical law governing atomic structure. Specifically, the concise writing of the equation—c(p)=2mp=2np2—shows direct links to the magnetic (m) and principal (n) quantum numbers. Central to the formula is the number "2," acting as the key of absolute symmetry. The model aligns perfectly with the four fundamental quantum numbers, viewing the table as an architectural blueprint rather than empirical data. By calculating the capacity of "virtual" layers, the framework extends beyond known radioactive elements into hypothetical periods. This growth is visualized through 3D "diamonds-like" crystals, where matter organizes according to a strict numerical logic of whole numbers. Mathematical structure ; Principal (computer security) ; Interpretation (philosophy) ; Symmetry (geometry) ; Mathematical logic ; Mathematical theory ; Mathematical model ; Table (database) History and advancements in chemistry ; Quasicrystal Structures and Properties ; Graph theory and applications
Authors: Eugeniy Yurievich Beliayev | Horst-Günter Rubahn | Yogendra Kumar Mishra
Source: Defence Technology (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Adaptive and multifunctional nanomaterials for defence-grade EMI shielding The rapid evolution of electronic warfare and communication systems has intensified the demand for adaptive, lightweight, and multifunctional electromagnetic interference (EMI) shielding materials that meet defence-grade reliability standards. This review provides a comprehensive overview of recent advances in nanomaterials engineered for EMI shielding in defence and aerospace platforms. Particular attention is devoted to adaptive phase-change systems, such as VO 2 , high-temperature ferrites, and MXene–carbon hybrids, which combine EMI suppression with thermal management, structural integrity, and stealth functionality. We map how nanoscale architectures—from zero- to three-dimensional, including graded, laminated, and hierarchical designs—tune impedance, dielectric and magnetic losses, and percolation pathways to achieve the desired broadband absorption. The discussion connects microstructural design to macroscopic performance metrics such as reflection–absorption balance, mechanical robustness, and environmental durability within the framework of MIL-STD-461G, RTCA-DO-160G, and NATO STANAG-4370 standards. Emerging trends, including gradient interfaces, liquid-metal-assisted hybrids, and scalable fabrication routes (roll-to-roll, spray, and additive manufacturing), are highlighted for their potential to achieve broadband performance at low areal density. Despite substantial progress, challenges remain in ensuring long-term stability, sustainable production, and seamless integration of adaptive nanostructures into deployable defence systems. Looking ahead, the field is expected to converge with data-driven materials discovery, multiphysics modelling, and green manufacturing, enabling the development of reconfigurable, recyclable, and environmentally resilient EMI shields for next-generation military applications. • Provides a defence-focused synthesis of adaptive nanomaterials for EMI shielding. • Maps nanoscale architectures from 0D to 3D for broadband absorption control. • Highlights VO 2 , ferrites, and MXene–carbon hybrids for multifunctional performance. • Links shielding performance with MIL-STD, RTCA, and NATO qualification standards. • Discusses gradient interfaces and scalable fabrication of lightweight composites. • Identifies challenges and future routes for durable, sustainable EMI shields. EMI ; Electromagnetic shielding ; Electromagnetic interference ; Multiphysics ; Broadband ; Scalability ; Resilience (materials science) ; Percolation (cognitive psychology) Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties
Authors: Jean claude Perez
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-31)
Field: Chemistry — Physical and Theoretical Chemistry

Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" Is this Mendeleev predictive law UNIVERSAL (ie. anywhere in the UNIVERSE)? Mathematical Symmetry and the Extended Periodic Table: Based on the mathematical framework and the "Generic equation of Mendeleev" By Jean-Claude Perez , PhD Mathematics & Computer Science, Bordeaux University. Retired IBM Artificial Intelligence European Research Centre, Montpellier Luc Montagnier Foundation. jeanclaudeperez2@gmail.com ( mailto:jeanclaudeperez2@gmail.com) @JCPEREZCODEX ORCID https://orcid.org/0000-0001-6446-2042 Mathematical Interpretation & Math Formula : ($c(p) = 2 \cdot Int(\frac{p+2}{2})^2$) Discovered by Jean-Claude Perez, the periodic table can be extended to predict the structure of matter in hypothetical, deep layers 1-16. This deterministic model demonstrates that the number of elements grows in predictable, symmetrical pairs, where each period $p$ contains a specific capacity $c(p)$ of elements 4-6. Abstract Jean-Claude Perez’s "Generic Equation of Mendeleev" reveals a deterministic mathematical law governing atomic structure. Specifically, the concise writing of the equation—c(p)=2mp=2np2—shows direct links to the magnetic (m) and principal (n) quantum numbers. Central to the formula is the number "2," acting as the key of absolute symmetry. The model aligns perfectly with the four fundamental quantum numbers, viewing the table as an architectural blueprint rather than empirical data. By calculating the capacity of "virtual" layers, the framework extends beyond known radioactive elements into hypothetical periods. This growth is visualized through 3D "diamonds-like" crystals, where matter organizes according to a strict numerical logic of whole numbers. Mathematical structure ; Principal (computer security) ; Symmetry (geometry) ; Interpretation (philosophy) ; Mathematical theory ; Mathematical logic ; Mathematical model ; Table (database) ; Numbering History and advancements in chemistry ; Quasicrystal Structures and Properties ; Theoretical and Computational Physics
Authors: Robin Bruce Thacker
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

CPE-Constrained AI Reasoning on Research Mathematics: Process Governance Without Domain Expertise — A Response to the First Proof Challenge We report results from applying CPE-constrained (Coherence ≥ Preservation ≥ Emergence) multi-architecture AI collaboration to four problems from the First Proof mathematical challenge (arXiv:2602.05192). The human operator has no formal training in the relevant mathematical domains. The governing methodology uses two domain-independent principles derived from the Structural Dependencies of Intelligence (SDI) framework and operationalized via the Unbounded Emergence (UE) methodology: (1) no claim may exceed what can be verified (E ≤ P), and (2) all reasoning paths must be explored to their endpoints. We achieve complete proofs for Q10 (preconditioned conjugate gradient for CP-RKHS tensor decomposition), partial results with documented gaps for Q9 (quadrilinear determinantal tensors), Q6 (ε-light subsets), and Q4 (superadditivity of 1/Φₙ under finite free convolution). The primary contribution is not the mathematical results themselves — other submissions achieve equal or stronger results — but the fully auditable reasoning trace, including documented errors, correction pathways, and a novel failure taxonomy for AI-assisted mathematical reasoning. We compare our process and results against the independent submissions of Armstrong, Kempe, and Munos (2026) and Dillerop (2026), and demonstrate that domain-independent process governance produces auditable, correctable reasoning that expert-guided prompting does not. This upload contains two documents: 1. Main paper covering all four problems with cross-submission comparison and synthesis 2. Companion document with complete Q10 solution, elimination trace, and correction documentation LaTeX source files are included for transparency. Operationalization ; Mathematical proof ; Domain (mathematical analysis) ; Documentation ; Process (computing) ; Proof theory ; Qualitative reasoning ; Homomorphic encryption Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)