jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wout Bittremieux
Source: Open MIND (article, 2026-02-24)
Field: Chemistry — Spectroscopy

Learning the Rules of Molecular Behavior from Open Mass Spectrometry Repositories High-throughput mass spectrometry has generated repositories containing billions of peptide and small-molecule spectra. Beyond their immediate analytical value, these repositories provide an unprecedented opportunity to uncover fundamental biophysical principles when combined with modern bioinformatics and AI. In this talk, I will illustrate how large-scale reanalysis and synthesis of public data can transform raw spectra into molecular insight. First, I will show how deep contrastive learning embeds hundreds of millions of spectra into a shared latent space, where spectra from the same peptide naturally cluster. Strikingly, this representation captures intrinsic biophysical parameters—including precursor mass, charge state, and sequence composition—without explicit supervision. The learned embedding reveals latent order in fragmentation processes and enables systematic exploration of the "dark proteome." Second, I will introduce a language-model approach to de novo peptide sequencing that treats mass spectra as a symbolic language of fragment ions. By directly translating spectra into amino acid sequences, the model learns the physics of peptide fragmentation from data alone. In addition to outperforming prior methods, it demonstrates that complex, sequence-dependent fragmentation pathways can be captured through large-scale, data-driven modeling. Finally, I will highlight the construction of a repository-scale "suspect library" that links hundreds of millions of spectra into molecular networks. By propagating structural information across related spectra, this approach uncovers families of metabolites connected by systematic chemical modifications. The resulting network maps expose how structural diversity emerges across metabolism, natural products, and environmental chemistry. Together, these examples illustrate how coupling large-scale public data with powerful AI approaches enables us to extract generalizable physical and chemical principles that govern molecular behavior in biological systems. Fragmentation (computing) ; Representation (politics) ; Raw data ; Embedding ; Mass spectrum ; Peptide ; Sequence (biology) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Hua Huang | Wekai Shao | Jeff Hammond
Source: arXiv (Cornell University) (preprint, 2026-01-12)
Field: Materials Science — Materials Chemistry

Accelerating Density Fitting with Adaptive-precision and 8-bit Integer on AI Accelerators The emergence of artificial intelligence (AI) accelerators like NVIDIA Tensor Cores offers new opportunities to speed up tensor-heavy scientific computations. However, applying them to quantum chemistry is challenging due to strict accuracy demands and irregular data patterns. We propose an adaptive precision algorithm to accelerate the density fitting (DF) method with Gaussian basis sets on AI accelerators using 8-bit integer (INT8) arithmetics. Implemented in the GPU-accelerated PySCF package, the algorithm is tested on more than twenty molecular systems with different NVIDIA GPUs. Compared to the standard FP64 code, our algorithm is up to 204\% faster on a RTX 4090 gaming GPU and up to 364\% faster on a RTX 6000 Ada workstation GPU without compromising the converged energy. This work demonstrates a practical approach to use AI hardware for reliable quantum chemistry simulations. Workstation ; Software ; Integer (computer science) ; Gaussian ; Quantum ; Heuristic ; Quantum computer ; Quantum chemical Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Big Data and Digital Economy
Authors: Shivani Dhall | Srijan Pushkarna | Jyoti Prakash | Kapil Sood
Source: Nano Express (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Green carbon dots: a review on sustainable synthesis, challenges and emerging applications Abstract Carbon dots are zero-dimensional carbon nanomaterials (&lt;10 nm) that have attracted significant attention due to their excellent biocompatibility, chemical stability, water solubility, and tunable photoluminescence. This review presents a critical and quantitative assessment of green-synthesized carbon dots, with an exclusive focus on natural and biomass-derived precursors, synthesis mechanisms, and structure property application correlations. Reported quantum yields of green carbon dots span a remarkably wide range from ∼3% to ∼75%, depending strongly on precursor chemistry, heteroatom content, surface passivation, and processing conditions. For example, carbohydrate-rich plant precursors typically yield are 3%–8%, whereas N/S-rich molecular and protein precursors such as citric acid–glutathione and egg white produce ultra-bright CDs with quantum yields as high as 65%–75%. Furthermore, particle sizes generally fall within 1–8 nm, although extreme aggregation up to ∼178 nm is reported for poorly controlled hydrothermal systems. A quantitative comparison of synthesis routes reveals that hydrothermal methods offer maximum precursor flexibility and tunability, microwave routes provide superior kinetic control and high average QY, and pyrolysis enables the most scalable biomass to carbon dots conversion with moderate quantum yields (7%–17%). The review further correlates these structure processing property trends with applications in biosensing, metal-ion sensing bioimaging, nanomedicine, energy storage, and supercapacitors, where performance is shown to be highly sensitive to surface chemistry and dopant distribution. Overall, this review provides a data-driven and mechanism-based perspective on the current status and future translational potential of green carbon dots.Introduction Carbon fibers ; Nanomaterials ; Quantum dot ; Dopant ; Biomass (ecology) ; Hydrothermal circulation ; Pyrolysis Carbon and Quantum Dots Applications ; Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors
Authors: Shuo Qiao | Bo Zhang | Yu Wu | Jiaqi Wang | Zhengyu Wu | Feng Shi
Source: Optical and Quantum Electronics (article, 2026-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research progress on wafer-scale metasurface plane optics Lithium niobate ; Planar ; Silicon ; Nanolithography ; Modulation (music) ; Integrated optics ; Optical materials Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Coatings and Gratings
Authors: Hua Huang | Wekai Shao | Jeff Hammond
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Accelerating Density Fitting with Adaptive-precision and 8-bit Integer on AI Accelerators The emergence of artificial intelligence (AI) accelerators like NVIDIA Tensor Cores offers new opportunities to speed up tensor-heavy scientific computations. However, applying them to quantum chemistry is challenging due to strict accuracy demands and irregular data patterns. We propose an adaptive precision algorithm to accelerate the density fitting (DF) method with Gaussian basis sets on AI accelerators using 8-bit integer (INT8) arithmetics. Implemented in the GPU-accelerated PySCF package, the algorithm is tested on more than twenty molecular systems with different NVIDIA GPUs. Compared to the standard FP64 code, our algorithm is up to 204\% faster on a RTX 4090 gaming GPU and up to 364\% faster on a RTX 6000 Ada workstation GPU without compromising the converged energy. This work demonstrates a practical approach to use AI hardware for reliable quantum chemistry simulations. Workstation ; Software ; Integer (computer science) ; Gaussian ; Quantum ; Heuristic ; Quantum computer ; Quantum chemical Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Big Data and Digital Economy
Authors: Bharat Pratap Singh | Shristika Adhikari
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Materials Science — Polymers and Plastics

Smart Hybrid Self-Healing Elastomeric Waterproof Coatings: Materials, Mechanisms, and Industrial Applications – A Systematic Review Self-healing, waterproof coatings represent a new generation of smart protective materials designed to autonomously repair damage and maintain barrier properties during service. Conventional waterproofing systems such as polymer membranes, cementitious coatings, and crystalline admixtures provide initial resistance to water ingress but gradually deteriorate due to microcrack formation, ultraviolet radiation, thermal stresses, and mechanical loading. Once microcracks develop, moisture and aggressive ions penetrate through the coating layer and reach the underlying substrate, leading to corrosion of reinforcing steel, deterioration of concrete, and eventual structural failure. Recent advances in polymer science, nanotechnology, and bio-inspired materials have enabled the development of coatings capable of autonomously restoring their protective functionality. These coatings incorporate healing mechanisms such as microcapsule-triggered polymerization, reversible dynamic covalent bonds, supramolecular interactions, and shape-memory effects. Among these approaches, hybrid elastomeric coatings containing silane-based healing agents encapsulated within polymeric microcapsules have emerged as promising solutions for waterproofing applications. Upon crack formation, capsules rupture and release reactive silane molecules, which undergo moisture-induced hydrolysis and condensation reactions to form new siloxane networks. This process seals cracks and restores the waterproof barrier. This review discusses the fundamental concepts, healing mechanisms, materials, fabrication methods, characterization techniques, and industrial applications of self-healing waterproof coatings. Current challenges related to scalability, environmental sustainability, and long-term durability are also examined along with emerging research trends, including nanostructured coatings, multifunctional materials, and AI-assisted materials design Waterproofing ; Coating ; Durability ; Elastomer ; Cementitious ; Self-healing ; Corrosion ; Characterization (materials science) Polymer composites and self-healing ; Microbial Applications in Construction Materials ; Corrosion Behavior and Inhibition
Authors: Zuoshuai Xi | Xinmeng Xu | Hongyi Gao | Ge Wang
Source: Materials Today (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

LLM-assisted intelligent discovery of metal-organic frameworks for solid-state electrolytes Transformative learning ; Inefficiency ; Zhàng ; Cluster analysis Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Bo Han | Mai Sheng Ng | Anutosh Chakraborty
Source: International Journal of Hydrogen Energy (article, 2026-02-22)
Field: Chemistry — Inorganic Chemistry

Advancements in metal-organic frameworks (MOFs) for high-capacity hydrogen storage: Mechanisms, optimization strategies, and future perspectives Deliverable ; Hydrogen storage ; Key (lock) ; Benchmark (surveying) ; Automotive industry ; Multiphysics ; Paradigm shift ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies
Authors: Berfin Dolunay Karacay | Selin Siviş | Ayşe Çağıl Kandemir | Gokce Nur Yilmaz
Source:  (article, 2026-02-05)
Field: Materials Science — Polymers and Plastics

Elastic Modulus as a Regime-Aware Computational Descriptor for Knowledge Extraction in Polymer/Clay Nanocomposites In data-driven materials research, elastic modulus is often treated as a scalar target variable to be optimized. However, such an assumption obscures the regime-dependent nature of elastic responses in polymer nanocomposites. In this study, elastic modulus data of nanocomposite systems are analyzed using a regime-aware computational framework. Materials are first clustered based on the neat polymer elastic modulus, defining stiffness regimes that enable meaningful local comparison. Relative modulus changes are then represented through graph-based abstraction, allowing structural patterns to be extracted without black-box learning. The results show that elastic modulus exhibits amplified but unstable behavior in soft polymers, highly tunable yet chaotic responses in semi-soft systems, and increasingly constrained and predictable behavior in rigid polymers. Surface modification does not universally maximize modulus enhancement but consistently increases the organization and interpretability of elastic responses. The proposed approach demonstrates how elastic modulus can be transformed from heterogeneous numerical data into structured, explainable knowledge suitable for data-driven materials design. Elastic modulus ; Extraction (chemistry) ; Nanocomposite ; Modulus ; Young's modulus Polymer Nanocomposites and Properties ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Yanan Huang | Nengqi Xu | Zhuo Li | Jianming Pan | Kang Liang | Jianxin Wang | Biao Kong
Source: Advanced Materials (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Interfacial Super‐Assembled Hetero‐Structured Chiral Nanochannels for Enantioseparation Applications Chirality is a fundamental property with profound implications in fields like pharmaceuticals, yet the separation of enantiomers remains a significant technical hurdle. This review chronicles the development of interfacial super-assembled hetero-structured chiral nanochannel membranes, a transformative approach for enantioseparation. We begin by systematically summarizing the construction of these hierarchical membranes from zero- to three-dimensional building blocks, employing a versatile toolbox of interfacial assembly strategies including phase inversion, electrospinning, and vacuum filtration. The design principles of pore-in-pore, pore-on-pore, and multichannel configurations are detailed, demonstrating how they break the conventional permeability-selectivity trade-off. The core transport mechanisms including diffusion-selective, adsorption-selective, and the recently proposed couple-accelerated-selective transport are elucidated. The review also surveys representative material systems, from metal-organic frameworks (MOF) to molecularly imprinted polymers (MIP). We conclude with a forward-looking perspective on persistent challenges, such as scalability and stability, and the immense opportunities presented by artifacial intelligence (AI)-assisted design and bioinspired dynamic membranes for the future of chiral technologies. Chirality (physics) ; Membrane ; Toolbox ; Scalability ; Perspective (graphical) ; Polymer ; Transport phenomena ; Supramolecular chirality Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Hui Bian | Dandan Ma | Y Chen | JeongYeon Hong | Yi Nan | Hai Xu | Myung Hwa Kim | Xiaoqiang Chen | Xiaojun Peng | Juyoung Yoon
Source: Chemical Society Reviews (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Molecular fluorophore dimerization: a new paradigm for precision phototheranostics Molecular fluorophore dimerization has recently emerged as a powerful and versatile design strategy in phototheranostics, offering a distinct regulatory regime that is fundamentally different from conventional single-molecule, polymeric, or aggregate-based systems. In this review, we present the first systematic and unified analysis of molecular dimerization as an independent paradigm for precision phototheranostics. Unlike previous reviews that primarily focus on isolated small-molecule fluorophores, polymeric architectures, or aggregates, this work highlights dimeric systems as an intermediate yet well-defined state that bridges molecular-level precision and collective-level functionality. We first comprehensively elucidate the fundamental photophysical mechanisms governing dimerization, and demonstrate how these processes uniquely regulate excited-state dynamics. Then, we reveal how dimerization enhances biophysical performance, such as controllable self-assembly and improved tumor accumulation. Representative dimeric systems across multiple dye families, including BODIPY, cyanine, porphyrins, donor-acceptor molecules, and metal complexes, are systematically categorized and analyzed, with an emphasis on structure-activity relationships and dimer-specific functional advantages in imaging-guided therapy. Finally, we discuss the current challenges and outline future directions, especially for artificial-intelligence-assisted molecular design. By positioning molecular dimerization as a distinct intermediate state between single molecules and higher-order assemblies, this review provides conceptual clarity and design principles for the development of next-generation phototheranostic agents. Fluorophore ; Focus (optics) ; CLARITY ; Molecule ; Small molecule ; Solid-state Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Nanoplatforms for cancer theranostics
Authors: Ashish Kumar Parashar | Anu Hardenia | Pratibha Bhide | Sunil Kumar Dwivedi | Purvi Chaturvedi | Asraf Khan
Source: Recent Advances in Drug Delivery and Formulation (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Nanozymes: The Emerging Frontier in Catalytic Nanomaterials and Their Therapeutic Applications Artificial nanomaterials known as nanozymes, which possess inherent enzyme-mimetic characteristics, have transformed environmental research, biomedicine, and catalysis. Compared with natural enzymes, nanozymes are advantageous due to their high stability, low cost, and adaptability. They exhibit oxidation, reduction, peroxidation, and superoxide dismutation activities, which are essential for environmental remediation, disease therapy, and biological diagnostics. Recent developments have further broadened their catalytic capabilities. Nanozyme-based biosensors offer high sensitivity and rapid detection of biomarkers for diseases such as cancer, diabetes, and infectious disorders, significantly improving early disease diagnosis. In therapeutics, nanozymes play a critical role in regulating oxidative stress, enabling targeted drug delivery, and supporting antibacterial treatments. Their ability to mimic peroxidase and catalase activities has greatly enhanced photodynamic therapy, immunotherapy, and neuroprotection strategies. Despite these advances, challenges remain in improving catalytic efficiency, biocompatibility, and target specificity. Safe clinical translation requires addressing issues related to immunogenicity, long-term toxicity, and controlled biodegradability. Advances in surface modification, functionalization, and AI-driven nanozyme design have improved selectivity and therapeutic performance. Integrating nanozymes with bioinformatics, artificial intelligence, and nanomedicine has opened new avenues for precision healthcare, including real-time disease monitoring and personalized treatment strategies. This review highlights the transformative impact of nanozymes in industrial and medical applications by examining recent advances, challenges, and future prospects in the field. With continued development, nanozymes have the potential to revolutionize enzyme-based technologies, making them indispensable to modern science and medicine. Nanomedicine ; Nanosensor ; Clinical Practice ; Biocompatible material ; Nanomaterials ; Transformative learning Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Dishniya Vishnuramkumar | Danveer Singh
Source: ACS Applied Optical Materials (article, 2026-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Index Dielectric Metasurfaces: Unlocking Frontiers in Mid-Infrared Photonics Metasurfaces, engineered planar structures manipulating light at subwavelength scales, have revolutionized nanophotonics across the visible to terahertz spectrum. All-dielectric material-based metasurfaces offer low optical losses, CMOS-compatible fabrication, and efficient light control via electric and magnetic resonances. The refractive index of such materials critically impacts the device performance, as traditional semiconductors like silicon and germanium provide moderate confinement, while emerging high-index materials such as WS2, PbTe, and GeSbTe enable stronger field localization and smaller device footprints. These high-index materials are vital for compact, reconfigurable metasurfaces operating in the mid- to far-infrared, essential for sensing, imaging, and thermal control. This article focuses on mid-IR photonics with dielectric metasurfaces, highlighting materials for high to ultrahigh refractive index with low losses, their advantages over plasmonics, and applications in wavefront control, nonlinear optics, and tunable devices. Future perspectives include scalable fabrication, AI-driven design, and broad industrial impact. Nanophotonics ; Photonics ; Terahertz radiation ; Dielectric ; Refractive index ; Planar ; Silicon ; Silicon photonics ; Wavefront Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Konstantinos Anthopoulos | Stefanos Michailidis | Zafeiro Thomaidou | Lydia Vogiatzaki | Nikolaos Kokkinos
Source: ChemEngineering (article, 2026-03-04)
Field: Chemistry — Spectroscopy

Friedel–Crafts: A Key Step in the Synthesis of Pharmaceutical Compounds This comprehensive review provides a consolidated and practically oriented overview of the Friedel–Crafts reaction in pharmaceutical synthesis, bringing together data from 93 peer-reviewed studies published between 1962 and 2025. Through a structured and comparative analysis of the literature retrieved from the Scopus and PubMed databases, this work integrates scattered information into a single, accessible resource, designed to guide researchers in drug discovery and development. The findings identify alkylation and acylation as the dominant Friedel–Crafts transformations, often enabling the synthesis of pharmacologically relevant scaffolds depending on substrate structure and the efficiency and selectivity of the catalytic system. These include compounds with anticancer, anti-inflammatory, and antimicrobial potential. Trends in catalyst and solvent selection highlight both the persistent reliance on classical Lewis acids in chlorinated media and a gradual interest in more sustainable alternatives, although their adoption remains system-dependent. By consolidating 63 years of research into a unified reference, this review underscores the versatility and enduring relevance of Friedel–Crafts methodologies in medicinal chemistry but also offers a data-driven foundation for their optimized and more sustainable application in future pharmaceutical development. Relevance (law) ; Key (lock) ; Drug discovery ; Scope (computer science) ; Pharmaceutical industry Organic and Inorganic Chemical Reactions ; Synthesis of heterocyclic compounds ; Chemical Synthesis and Reactions