jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyu Wang | Fei Dou | Jinbo Bi | Minghu Song
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

SIGMA: Structure-Invariant Generative Molecular Alignment for Chemical Language Models via Autoregressive Contrastive Learning Linearized string representations serve as the foundation of scalable autoregressive molecular generation; however, they introduce a fundamental modality mismatch where a single molecular graph maps to multiple distinct sequences. This ambiguity leads to \textit{trajectory divergence}, where the latent representations of structurally equivalent partial graphs drift apart due to differences in linearization history. To resolve this without abandoning the efficient string formulation, we propose Structure-Invariant Generative Molecular Alignment (SIGMA). Rather than altering the linear representation, SIGMA enables the model to strictly recognize geometric symmetries via a token-level contrastive objective, which explicitly aligns the latent states of prefixes that share identical suffixes. Furthermore, we introduce Isomorphic Beam Search (IsoBeam) to eliminate isomorphic redundancy during inference by dynamically pruning equivalent paths. Empirical evaluations on standard benchmarks demonstrate that SIGMA bridges the gap between sequence scalability and graph fidelity, yielding superior sample efficiency and structural diversity in multi-parameter optimization compared to strong baselines. Generative model ; String (physics) ; Generative grammar ; Inference ; Scalability ; Graph ; Redundancy (engineering) ; Autoregressive model ; Boosting (machine learning) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xianghao Meng | Kaiwei Yang | Xuyang Li | Haining Liu | Shanlin Qiao
Source: Materials Science and Engineering R Reports (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

The trilemma of 2D covalent organic frameworks in integrated device Covalent bond ; Trilemma ; Context (archaeology) ; Work (physics) ; Stacking Covalent Organic Framework Applications ; Surface Chemistry and Catalysis ; 2D Materials and Applications
Authors: Jiabao Cao | Guangqi Fan | Baoxin Lu | Zhigang Xiao | Guang Xin
Source: Trends in Food Science & Technology (article, 2026-02-04)
Field: Materials Science — Biomaterials

Food-derived self-assembling peptide hydrogels: Mechanistic insights, food applications, and AI-driven future perspectives Self-healing hydrogels ; Process (computing) ; Peptide ; Sustainability ; Sequence (biology) ; Analytics Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Polydiacetylene-based materials and applications
Authors: P. Rubach | A. Wlodawer | W. Minor
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Advances in cryo-electron microscopy (cryoEM) and X-ray crystallography for structure-based drug discovery Macromolecular X-ray crystallography (XRC), nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryoEM) are the primary techniques for determining atomic-level, three-dimensional structures of macromolecules essential for drug discovery.With advancements in artificial intelligence (AI) and cryoEM, the Protein Data Bank (PDB) [1] is solidifying its role as a key resource for 3D macromolecular structures.This position underscores the growing need for enhanced quality metrics and robust validation standards for all experimental structures.This presentation examines recent advancements in cryoEM [2] and X-ray crystallography [3], analyzing structure quality metrics (i.a.see Fig. 1: Q-score and CC MASK vs. Resolution for cryoEM structures), resolution improvements, ligand and water molecule identification, refinement software, and the identification of duplicate submissions that are not necessarily duplicates [4].Nearly 800 protein crystal structures determined at a resolution of 2.5 or better are present in the current PDB release without any water molecules, while some other depositions exhibit unusually low or high solvent occupancies [5].This study also includes an in-depth analysis of crystal structures' geometrical and quality parameters, such as: R, R-free, clashscore, rotamer and Ramachandran outliers as well as the PQ1 measure performed on a dataset of medicinally important cAMP-dependent protein kinases.In particular, we will show how the different metrics unevenly change when these structures are re-refined by PDB-REDO [6].Finally, we will discuss the role of both X-ray crystallography and cryoEM techniques in drug discovery in light of two significant findings: the demonstration that cryoEM can resolve hydrogen atom positions and water networks [7], and the report of an atomic-resolution (1.09) protein structure (8RQB) that reveals double conformations [8]. Figure 1.Q-score and CCMASK vs. Resolution for cryoEM structures. Ramachandran plot ; Protein Data Bank (RCSB PDB) ; Drug discovery ; Resolution (logic) ; Structural biology ; Protein Data Bank ; Protein crystallization ; Protein structure Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Y. Wang | Wansen Tan | Jingjun Hong
Source: Materials & Design (article, 2026-01-17)
Field: Materials Science — Biomaterials

Advances in recombinant type III collagen A comprehensive overview of recombinant type III collagen (rhCOL III) spanning molecular design to practical applications (The structure of the recombinant collagen molecule is derived from PDB ID 6A0A [1] ). • This review provides a comprehensive examination of Type III collagen, with particular emphasis on its unique triple helix structure, which confers distinctive biological properties and functional characteristics. • The review further explores its essential roles in various physiological and pathological processes, such as wound healing, skin aging, and cardiovascular diseases, while also addressing current challenges and future research directions in the field. Collagen, the most abundant protein in mammals, constitutes approximately 30 % of the total protein content in the human body and plays a critical role in maintaining tissue structure and function. Among various types, type III collagen is a key component of the extracellular matrix and features a distinctive triple-helical structure that confers specific biological activities and functional properties. However, its production has long been constrained by low expression levels and an unstable triple helix structure. With advances in genetic engineering and synthetic biology, significant progress has been achieved in the research and application of recombinant type III collagen (rhCOL III). This review focuses on recent developments in expression systems, particularly in Escherichia coli and Pichia pastoris , where production yields have reached 3.02 g/L and 10.3 g/L respectively. Recombinant type III collagen has found applications in diverse fields, including skincare, injectable fillers, wound dressings, and tissue engineering. Future development directions include AI-driven molecular design, 3D bioprinting, and large-scale fermentation processes, aimed at addressing current challenges related to proline hydroxylation, purification efficiency, and immunogenicity. Recombinant DNA ; Pichia pastoris ; Triple helix ; Extracellular matrix ; Type IV collagen ; Type I collagen ; Escherichia coli ; Protein engineering Collagen: Extraction and Characterization ; Wound Healing and Treatments ; Tissue Engineering and Regenerative Medicine
Authors: Xuanqi Luo | Li Wang | Zichao Yuan | Yuan Wang | Quanmao Wei | Hu Han | Yahua Liu
Source: ACS Applied Materials & Interfaces (article, 2026-03-17)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired Nanostructures for Combined Hydrophobicity and Light Propagation: From Natural Prototypes to Applications Bioinspired nanostructures that integrate hydrophobicity with light propagation have attracted growing interest for creating multifunctional and environmentally adaptive interfaces. While both functionalities have been extensively studied independently, the integration of optical and hydrophobic properties into a single surface remains a developing area. This review provides a comprehensive overview of the fundamental mechanisms, synergistic effects, and potential applications arising from the coupling between light propagation and hydrophobicity. We first introduce representative biological prototypes, structure classification, and basic theories. Building on this foundation, we analyze the synergistic effect and inherent trade-offs between optical performance and wetting properties. Fabrication strategies are reviewed with attention to performance, structural robustness, and cost. Particular attention is given to applications, including solar energy utilization, sensors, information security, dynamic wettability control, and medical technologies. We also discuss the potential of combining slippery liquid-infused porous surfaces (SLIPSs) with light-propagating structures, as well as AI-assisted inverse design for optimizing multifunctional interfaces. This review provides an integrated framework linking bioinspired wetting and optical phenomena, offering guidance for the design of next-generation multifunctional and adaptive optical materials. Nanostructure ; Biomimetics ; Wetting ; Fabrication ; Design elements and principles ; Coupling (piping) ; Surface energy Surface Modification and Superhydrophobicity ; Solar-Powered Water Purification Methods ; Electrowetting and Microfluidic Technologies
Authors: Wenjie Yang | Shuocheng Fan | Jie Luo | Yichu Zhou | Xingyang Dai | Jinhu Huang | Yuan Wang | Xiaoming Wang
Source: Antioxidants (article, 2026-01-23)
Field: Chemistry — Organic Chemistry

Natural Products as Potentiators of β-Lactam Antibiotics: A Review of Mechanisms, Advances, and Future Directions This review focuses on the research progress on natural products as β-lactam antibiotic adjuvants, aiming to address the escalating challenge of antibiotic resistance, particularly the inactivation of antibiotics caused by β-lactamases. The article provides an in-depth analysis of the mechanisms by which plant-derived (e.g., flavonoids, tannins, phenolics, terpenoids, and alkaloids) and microbial-derived (e.g., clavulanic acid, fungal metabolites, bacteriophages) natural products enhance antimicrobial efficacy. Key potentiation strategies discussed include efflux pump inhibition, membrane permeability alteration, biofilm disruption, PBP2a inhibition, and direct β-lactamase inhibition. Additionally, the review outlines in vitro methods (e.g., dilution and checkerboard assays) and in vivo models (e.g., mouse infection models) used to assess synergistic effects. It also addresses major challenges in identifying active compounds, elucidating mechanisms of action, and pharmacokinetic characterization. Looking forward, the article highlights the potential of multi-omics approaches, artificial intelligence, and nanotechnology to overcome existing bottlenecks, providing novel strategies for the development of effective and safe antibiotic adjuvants. These advances are expected to provide both theoretical insights and practical guidance for combating antibiotic-resistant bacterial infections. Potentiator ; Antibiotics ; Antimicrobial ; Limiting ; Efflux ; Antibiotic resistance Synthesis of β-Lactam Compounds ; Microbial Natural Products and Biosynthesis ; Antibiotic Resistance in Bacteria
Authors: Henrietta Marcel | Daniel Idowu | Adeyinka Ademilua | Loveth Marcel | Mohammed Issa Abdul-Rahman | Favour Segun Fabuyi
Source: Chemistry and Materials (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Covalent Organic Framework (COFs) in Targeted Drug Delivery and Therapeutics: A Critical Review Covalent organic frameworks (COFs) are an emerging type of crystalline, porous polymers which are formed through covalent bonds using designed organic building blocks. Their high pore structuring, high surface area, and modular chemistry make them attractive platforms for targeted drug delivery and multimodal therapeutics. This is a critical review of the developments in the field of foundational COF chemistry to the most recent biomedical applications, especially with regard to drug encapsulation mechanisms (physical adsorption, covalent conjugation, and host-guest interactions), stimulus-responsive release strategies (pH, redox, enzyme, light), and targeting of the host (passive EPR accumulation and active ligand directed delivery). It focuses on discussion around nanoscale COF fabrication and functionalization approaches that permit controlled loading, colloidal stability, and receptor-mediated uptake, and multifunctional and theranostic COF systems that combine photodynamic/photothermal therapy, catalytic nanozyme activity, gene delivery, and multimodal imaging. In the literature, COFs have shown excellent drug loading capabilities and preclinical efficacy potentials. However, translation is a bottleneck due to several factors, including reproducibility, hydrolytic stability of linkages, full pharmacokinetics/toxicology, and scalable GMO-compatible synthesis. It reported methodological heterogeneities with loading/release assays and advocates standardized reporting (DLC, EE, release conditions, pre-/and post-structural characterization) and intensive in vivo PK/PD and immunotoxicity investigations. Lastly, strategic priorities were outlined in the field, which include rational linkage selection of biodegradability, orthogonal post-synthetic modification protocols, integration of computational design tools, and specific translational work to fast-track clinical translation of COF-based nanomedicines. Taken together, this review evaluates the potential and practical challenges of the COFs as next-generation targeted delivery and therapeutic vehicles. Drug delivery ; Nanomedicine ; Surface modification ; Covalent bond ; Targeted drug delivery ; Rational design ; Modular design ; Encapsulation (networking) Covalent Organic Framework Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Zesen Zhou | Jeevan Rois | Matias Kagias
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Photon density of states engineering with generative inverse design for scalable 3D photonic metamaterials The photon density of states (pDOS) governs fundamental light matter interactions and is a critical parameter for designing next generation light driven technologies such as photocatalysis and solar energy harvesting. Achieving a target pDOS in 3D nanoarchitected structures remains challenging due to the nonlinear and non unique relationship between geometry and spectral response. Here, we present an end to end inverse design framework for tailoring the pDOS of 3D photonic metamaterials fabricated via the scalable nanofabrication approach of metasurface-based holographic lithography. A data driven forward surrogate model is constructed to predict frequency resolved pDOS spectra from metasurface diffraction parameters and lithographic thresholds. Inverse design is performed using a conditional generative adversarial network (cGAN) that generates candidate metasurface diffraction parameters for target pDOS features. 3D structures featuring high local pDOS were obtained across a broad normalized frequency range and consistently outperformed those in the original dataset. Structural analysis revealed that these high pDOS architectures fall into two predominant structural categories with similar rotational symmetry characteristics. Our work establishes the first inverse design strategy for 3D photonic metamaterials fabricated via holographic lithography. Metamaterial ; Photonics ; Photonic crystal ; Holography ; Inverse ; Photonic metamaterial ; Photon ; Nanophotonics ; Density of states ; Lithography Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Quasicrystal Structures and Properties
Authors: Adel Noubigh | Lotfi Ben Tahar | Hammadi Khmissi
Source: Arabian Journal of Chemistry (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Thermodynamic study of benzimidazole solubility and preferential solvation in binary alcohol-water systems The solubility of benzimidazole (BZI) in aqueous-alcohol blends (water with methanol (MeOH), or ethanol (EtOH)) was measured by a gravimetric method at temperatures ranging from 288.15 to 323.15 K. This research is the first systematic report on BZI solubility in these binary mixed solvents, thus filling a considerable gap in the literature. The study found that BZI solubility was higher at elevated temperatures in all the binary solvent systems. The kamlet-abboud-taft linear solvation energy relationship (KAT-LSER) model was employed to evaluate the role of solute-solvent contacts in solubility behavior. Three thermodynamic models (Jouyban-Acree, Jouyban-Acree-van’t Hoff, and Apelblat-Jouyban-Acree) were employed to connect experimental solubility data with the models. The models are in strong agreement with the experimental results, as indicated by the low average relative deviation (RAD%) values (≤ 1.52, ≤ 1.34, and ≤ 2.78, respectively). Inverse Kirkwood-Buff integrals (IKBI) were used to obtain dissolution parameters, which indicated positive preferential solvation values for the alcohol components in alcohol aqueous solutions, with the strength of preferential solvation following the order EtOH &gt; MeOH and showing a significant dependence on both solvent composition and temperature. These results augment the knowledge of BZI dissolution thermodynamics and serve as very dependable reference data for its separation, formulation, and pharmaceutical processing. Solvation ; Solubility ; Dissolution ; Gravimetric analysis ; Methanol ; Solvent ; Benzimidazole ; Alcohol Crystallization and Solubility Studies ; Phase Equilibria and Thermodynamics ; Drug Solubulity and Delivery Systems
Authors: Helong Hu | HongDan Pan | ShuiQing Hu
Source: arXiv (Cornell University) (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

From Phenomenological Fitting to Endogenous Deduction: A Paradigm Leap via Meta-Principle Physics Architecture The essence of current neural network architectures is phenomenological fitting: they learn input-output statistical correlations via massive parameters and data, yet lack intrinsic understanding of the fundamental principles governing physical reality. This paper proposes a paradigm leap from pure phenomenological fitting to the fusion of phenomenological fitting and endogenous deduction. By embedding physical meta-principles into neural network architecture, we construct the Meta-Principle Physics Architecture (MPPA). Specifically, MPPA embeds three core meta-principles - Connectivity, Conservation, Periodicity - into its architecture, implemented via three core components: the Gravitator realizes Connectivity via standard causal attention; the Energy Encoder implements Conservation via log-domain energy tracking and delayed compensation; the Periodicity Encoder fulfills Periodicity via FFT-based spectral analysis and delayed modulation. These components collaborate via a learnable independent gating fusion mechanism, forming a complete physical cognition framework of 'local relational connectivity - global conservation constraint - evolutionary periodic law'. Experiments show MPPA achieves significant improvements: physical reasoning (from near zero to 0.436, 0.436 vs 0.000), 2.18x mathematical task improvement (0.330 vs 0.151), 52% logical task gain (0.456 vs 0.300), and 3.69% lower validation perplexity (259.45 vs 269.40), with only 11.8% more parameters (242.40M vs 216.91M). Notably, MPPA shows strong generalization on out-of-distribution physical scenarios, proving the robustness and interpretability of this principle-embedded design. This work establishes a new theoretical foundation and technical path for next-generation AI with physical common sense, causal reasoning, and mathematical rigor. Interpretability ; Artificial neural network ; Robustness (evolution) ; Physical system ; Phenomenology (philosophy) ; Generalization ; Sensor fusion ; Architecture Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Reservoir Computing
Authors: Khan Muhammad Ijaz | Rahman Fazal | Khurshid Razia | Mahfuza Anjum | Siraj Muhammad | Rehman Ubaid Ur | Ullah Ihsan | Ahmad Ayaz | Khan Adnan
Source: Annals of Advances in Chemistry (article, 2026-02-09)
Field: Materials Science — Biomaterials

Biofunctionalisation of Nanoparticles: Emerging Strategies and Future Prospects for Biomedical Applications Nanotechnology has emerged as a transformative approach in modern medicine, offering powerful strategies for early disease detection and the development of safer, more precise, and cost-effective personalized therapies. Engineered nanoparticles, designed through controlled manipulation of size, shape, and surface chemistry, serve as highly efficient drug carriers that can improve therapeutic outcomes while minimising systemic toxicity. Surface biofunctionalization plays a pivotal role by enhancing nanoparticle stability, biocompatibility, and selective targeting. Functional ligands such as aptamers, peptides, and antibodies enable molecular recognition, facilitating accurate drug delivery and advanced diagnostic applications. Aptamers provide flexible, high-affinity targeting capabilities, whereas antibodies ensure specificity toward defined biomarkers. Despite existing challenges in clinical translation, biofunctionalized nanoparticles demonstrate significant promise in targeted drug delivery, diagnostic imaging, and theranostic systems. Antibody-conjugated nanoparticles have notably advanced diagnostics and treatment strategies for cancer, neurological disorders, and precision medicine. Similarly, transferrin-modified nanocarriers enable selective cellular uptake. Recent developments integrate therapeutic and imaging functionalities within unified nanoplatforms, allowing simultaneous drug delivery and real-time in vivo monitoring. This review highlights advances in surface engineering and biofunctionalization techniques, providing a comprehensive framework for designing multifunctional nanomaterials that synergistically combine diagnostic and therapeutic capabilities for next-generation biomedical applications. Nanocarriers ; Drug delivery ; Targeted drug delivery ; Aptamer ; Nanomedicine ; Precision medicine ; Personalized medicine ; Drug Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanotechnology research and applications