jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeheon Woo | Sunghwan Choi
Source: Journal of Chemical Theory and Computation (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs Recent advances in graphics processing units (GPUs) have substantially broadened the applicability of computational chemistry. Nevertheless, the double-precision (FP64) operations conventionally required in quantum chemistry remain inefficient on artificial intelligence (AI)-focused GPUs, which are predominantly optimized for lower precision arithmetic. Moreover, the limited memory capacity of such GPUs necessitates algorithmic adaptations to traditional approaches in density functional theory (DFT). In this work, we propose a systematic mixed precision strategy for the iterative matrix diagonalization procedure, which constitutes the principal bottleneck in real-space DFT calculations. By selectively employing single-precision (FP32) in most computational steps and even adopting brain floating point (BF16) in the preconditioning stage, we demonstrate that numerical accuracy can be preserved with negligible degradation. Building on these observations, we developed a mixed precision eigensolver and validated its performance across material systems spanning a wide range of sizes. Our results show that the proposed method achieves up to 10× speedup in the diagonalization step relative to FP64, while extending the feasible system size by approximately 50%. Comprehensive validation across multiple GPU architectures further confirms that AI-focused GPUs can deliver performance comparable to that of high-end high-performance computing (HPC)-focused GPUs, thereby substantially improving accessibility to large-scale electronic structure simulations. Bottleneck ; Speedup ; Graphics ; Graphics processing unit ; Range (aeronautics) ; Matrix multiplication ; Density functional theory ; Quantum Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Protein Structure and Dynamics
Authors: Dongyang Xiao | Rongyu Deng | Zhaoen Liu | Yuxian Wu | Dawei Chu | Yaochun Qiang | Wanwan Mei | Peiyi Song | Leimeng Sun | Fangjing Hu
Source: Laser & Photonics Review (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Carbon Nanotubes for Terahertz Waves: Synthesis, Patterning, and Applications ABSTRACT Terahertz (THz) technology is emerging as a key enabler for high‐speed communications, imaging, and sensing. However, the advancement of THz devices remains hindered by the intrinsic limitations of conventional materials in delivering broadband absorption, efficient emission, and real‐time control of THz waves. Carbon nanotubes (CNTs), with their quasione‐dimensional structure, high carrier mobility, tunable plasmonic response, and pronounced electromagnetic anisotropy, offer a uniquely versatile material platform to address these challenges. This review systematically examines the fundamental properties, controllable synthesis, and precision patterning of CNTs for THz applications. It further surveys the operating mechanisms and recent progress of CNT‐based THz devices, including emitters, antennas, absorbers, detectors, modulators, polarizers, transistors, and amplifiers. Particular emphasis is placed on the roles of photoelectric, thermoacoustic, and plasmonic effects in enabling efficient detection, emission, and dynamic manipulation of THz. Finally, the review discusses emerging opportunities for CNT‐enabled THz technologies, including highly integrated systems, quantum‐effect modulation, and artificial‐intelligence‐assisted design for future applications. This review is organized as follows: Section 1 introduces the advantages of CNTs for THz applications; Section 2 summarizes CNT synthesis and transfer methods; Section 3 highlights representative CNT‐based THz devices for wave generation, modulation, absorption, and processing; and Section 4 presents the conclusions and outlook. Terahertz radiation ; Carbon nanotube ; Plasmon ; Broadband ; Nanoelectronics ; Metamaterial Carbon Nanotubes in Composites ; Terahertz technology and applications ; Superconducting and THz Device Technology
Authors: P.J. Canfield | Maxwell J. Crossley
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The Polytope Formalism: application to molecular constitution and the prospect of a complete description of Chemical Space The principles of the Polytope Formalism - first developed for stereoisomerism - are here extended to molecular constitution (including constitutional isomerism), highlighting a deep connection between these two aspects of structure and opening the way toward a unified description of all isomerism. A key feature of this development is that it is based solely on atom connectivity, with explicit inclusion of subvalent and hypervalent species. The resulting complete sets of possible species include traditional isomers and their interconversion intermediates (transition states, higher-order saddle points, <i>etc.</i>), providing a powerful tool for elucidating isomerisation mechanisms. This is demonstrated through increasingly complex examples of H-tautomerism. The corresponding networks of species and interconversion pathways map directly onto their associated potential-energy surfaces and thus function as a "discretised encoding" of them. Because this framework accommodates a multidimensional implementation of transition-state theory, it describes and organises the behaviour of the chemical system. Beyond stereoisomerism and molecular constitution, the same mathematical and conceptual principles may be applied to the quantum chemical aspects (nuclear, electronic, and rovibrational states), yielding a fully discretised and physically grounded representation of molecular systems. In doing so, the Polytope Formalism provides a universal framework for the automated exploration of chemical behaviour and Chemical Space - integrating rigorous theory, digital representation, and data-driven discovery within a single coherent framework. Chemical space ; Polytope ; Formalism (music) ; Quantum chemical ; Representation (politics) ; Infinitesimal History and advancements in chemistry ; Chemistry and Stereochemistry Studies ; Graph theory and applications
Authors: Giovana Signori-Iamin | André Mazega | Quim Tarrés | Ronald Marquez | José Luis Sanchez-Salvador | Carlos Negro | Alexandre F. Santos | Marc Delgado-Aguilar
Source: Chemical Engineering Journal (article, 2026-04-06)
Field: Materials Science — Biomaterials

Integrated techno-economic and structure-property assessment of continuous TEMPO-mediated oxidation for cellulose nanofiber production The transition towards sustainable materials manufacturing demands scalable and economically viable processes for producing cellulose nanofibers (CNFs). TEMPO-mediated oxidation is among the most efficient routes for generating carboxylated fibers, yet its industrial application remains constrained by chemical costs and the challenge of shifting from batch to continuous operation. In this work, TEMPO-mediated oxidation of bleached eucalyptus kraft pulp for CNF production was performed in a continuous stirred-tank reactor (CSTR) to assess how residence time affects CNF properties and process economics, through integrated experimentation, reaction-kinetics modeling and techno-economic assessment (TEA). Short residence times generated under-oxidized fibers, whereas excessive exposure led to partial degradation, indicating an optimal residence time of 90 min. TEA under two scenarios, fixed production rate and fixed reactor volume, showed that operating at 90 min offers the most cost-effective balance between CNF quality and processing costs. Finally, an AI-assisted TEA framework (predictive R 2 > 0.99) allowed a 10,000-run Monte Carlo Simulation, yielding a 90% confidence interval for the Minimum Selling Price of $4.46/kg - $9.78/kg, considering catalyst recovery. Overall, this study establishes a process-level framework by providing an understanding of the relation between the residence times in a CSTR with final CNF properties, indispensable for implementing industrial production and application of these nanomaterials. By integrating the experimental structure-property maps with the TEA, an ideal operating window was identified, that balances CNF performance with reaction cost, and quantitatively identified production scale (≈90% importance) and catalyst cost (≈5% importance) as the dominant drivers of economic viability. • Continuous TEMPO-oxidation of fibers proof of concept for scale up • Best conditions for optimal residence time were identified. • ML-augmented TEA identifies feature importance. • Monte Carlo simulation showed economic potential for speciality applications. Cellulose ; Nanofiber ; Production (economics) ; Catalysis ; Cellulose fiber Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Electrospun Nanofibers in Biomedical Applications
Authors: Lianjie Wan | Fei MA | Jianmin Zhou | changwen du
Source: Journal of Integrative Agriculture (article, 2026-01-08)
Field: Chemistry — Inorganic Chemistry

Green agriculture enabled by versatile metal-organic frameworks: A review • Strategies for green synthesis and production of target metal-organic frameworks (MOFs). • Removal of agricultural pollutants using functional MOFs. • Water capture and nutrient management through designed MOFs. • Challenges and perspectives of novel MOFs in smart agriculture. Modern agriculture faces unprecedented challenges: a growing global population, limited arable land, freshwater scarcity, and inefficient agrochemical use have triggered severe environmental degradation. Pollutants including pesticides, heavy metals, microplastics, antibiotics, nutrient runoff, and greenhouse gases threaten ecosystem stability, food security, and human health. Metal-organic frameworks (MOFs), with their tunable structures, high porosity, and versatile functionality, emerge as promising materials to address these issues. This review comprehensively summarizes recent advances in MOFs-based solutions for agriculture. It covers green synthesis strategies to enhance structural stability and promote circular economy principles. Applications span three primary domains: pollutant remediation, sustainable technologies (e.g., atmospheric water harvesting, seawater desalination, and green ammonia synthesis), and smart agricultural systems. The latter enables controlled agrochemical release and real-time sensing and monitoring. Finally, challenges - such as high costs, biosafety concerns, and scalability limitations - are discussed, alongside forward-looking perspectives including AI-assisted design, improved recyclability, scalable production, and multifunctional integration toward green and smart agriculture. Agrochemical ; Agriculture ; Arable land ; Pollutant ; Sustainability ; Sustainable agriculture ; Agricultural productivity ; Greenhouse gas Metal-Organic Frameworks: Synthesis and Applications ; Phosphorus and nutrient management ; Polymer-Based Agricultural Enhancements
Authors: Hongyu Chen | Mengyu Dai | Hongjiang Chen | Ruilin Liu | Xiaole Tian | Ruixiao Lian | Yuqian Zhang | Xia Cai | Wenwu Li | Hao Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-04)
Field: Materials Science — Materials Chemistry

Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble Accurate description of crystal structures is a prerequisite for predicting the physicochemical properties of materials. However, conventional X-ray diffraction (XRD) characterization often encounters intrinsic bottlenecks when applied to complex multiphase systems, necessitating the integration of complementary optical measurement. In this study, we developed a multi-descriptor framework by integrating key parameters including space groups, Pearson symbols, and Wyckoff sequences, to categorize the dataset of over 19,000 crystals into several dozen structural prototypes. Then, an accuracy-adaptive ensemble network based on residual architectures was implemented to capture structural ``fingerprints" within phonon vibration modes and Raman spectra. The ensemble algorithm demonstrates exceptional robustness when processing various crystals of varying lengths and quality. This data-driven classification strategy not only overcomes the reliance of traditional characterization on ideal data but also provides a high-throughput tool for the automated analysis of material structures in large-scale experimental workflows. Robustness (evolution) ; Crystal structure prediction ; Categorization ; Residual ; Diffraction ; Characterization (materials science) ; Ideal (ethics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Hongyu Chen | Mengyu Dai | Hongjiang Chen | Ruilin Liu | Xiaole Tian | Ruixiao Lian | Yuqian Zhang | Xia Cai | Wenwu Li | Hao Zhang
Source: ArXiv.org (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Phonon-informed Crystal Structure Classification via Precision-Adaptive ResNet-based Confidence Ensemble Accurate description of crystal structures is a prerequisite for predicting the physicochemical properties of materials. However, conventional X-ray diffraction (XRD) characterization often encounters intrinsic bottlenecks when applied to complex multiphase systems, necessitating the integration of complementary optical measurement. In this study, we developed a multi-descriptor framework by integrating key parameters including space groups, Pearson symbols, and Wyckoff sequences, to categorize the dataset of over 19,000 crystals into several dozen structural prototypes. Then, an accuracy-adaptive ensemble network based on residual architectures was implemented to capture structural ``fingerprints" within phonon vibration modes and Raman spectra. The ensemble algorithm demonstrates exceptional robustness when processing various crystals of varying lengths and quality. This data-driven classification strategy not only overcomes the reliance of traditional characterization on ideal data but also provides a high-throughput tool for the automated analysis of material structures in large-scale experimental workflows. Robustness (evolution) ; Crystal structure prediction ; Categorization ; Residual ; Diffraction ; Characterization (materials science) ; Ideal (ethics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Andrew J Medford | Todd N. Whittaker | Bjarne Kreitz | David W Flaherty | John Kitchin
Source: Current Opinion in Chemical Engineering (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Prospects for using artificial intelligence to understand intrinsic kinetics of heterogeneous catalytic reactions Heterogeneous catalysis research struggles to connect intrinsic kinetics with experimentally observed behavior due to complex multiscale models, limited observability, and a many-to-one mapping between mechanisms and data. Advances in operando experiments, atomic-scale models, microkinetic models, and reactor simulations provide rich information, but dramatically expand model complexity and uncertainty. Artificial intelligence can reduce the human time needed for modeling by enabling ‘self-driving’ multiscale models that automate model construction, refinement, and validation across scales. Increased throughput will result in large ensembles of multiscale models that better explore parameter space, yield insight into sensitivity and uncertainty, and improve quantitative agreement between theory and experiment. • Few AI models address the full multiscale nature of catalysis. • Multiscale models remain limited by assumptions and parameter uncertainty. • Operando and transient methods yield rich but heterogeneous, nonstandard data. • Generative and agentic AI can expand mechanistic search and reduce bias. • Integration of AI with modeling and data can produce higher-quality multiscale models. Multiscale modeling ; Throughput ; Sensitivity (control systems) ; Generative model ; Yield (engineering) ; Experimental data ; Data-driven Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Minyi Fan | Xiaogang Dong | Xuwei Liu
Source: Advanced Functional Materials (article, 2026-03-25)
Field: Chemistry — Inorganic Chemistry

So Much More Than Blue Color in Fruits ABSTRACT The blue appearance of fruits not only facilitates seed dispersal by attracting animals but also leaves a lasting impression on consumers. This coloration stems for example, from pigments like anthocyanins, but as well from structural coloration arising through the interaction between nanoscale structures and light. The intrinsic optical mechanism of structural color provides a stable and sustainable alternative to traditional pigmentary coloration, bridging biological inspiration with material innovation. And it is gradually demonstrating unique potential in various fields, for example, food, smart sensing, cosmetics, and functional materials. A systematic overview of types and sources of blue fruits is provided in this review. The coloration mechanisms of pigmentary and structural blue are elucidated separately. In addition, the current applications of their chromogenic components and structural features, along with recent advances in AI‐assisted color regulation, are summarized. As a next‐generation coloration strategy, structural color combines stability, tunability, and sustainability, offering a promising route toward the design of eco‐friendly and intelligent color systems. Furthermore, integrating AI‐assisted design and bioinspired nanostructure engineering could accelerate the development of advanced optical materials. Its unique optical versatility is expected to drive future innovations across sensing, functional materials, and bio‐related applications. Structural coloration ; Nanostructure ; Bridging (networking) ; Nanoscopic scale ; Design elements and principles ; Biomimetics Pigment Synthesis and Properties ; Photonic Crystals and Applications ; Advanced Photocatalysis Techniques
Authors: Uttam Singisetti
Source: Open Access Government (article, 2026-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gallium oxide: The race to power the next-generation grid and EV infrastructure Gallium oxide: The race to power the next-generation grid and EV infrastructure In an exclusive OAG Q&amp;A interview, Professor Singisetti from the Department of Electrical Engineering at the University at Buffalo discusses the commercialisation path for Gallium Oxide in high-power electronics. The exponential growth of Artificial Intelligence (AI) requires massive computing power, leading to a concurrent spike in energy consumption from Hyperscale data centres. This demand makes the adoption of high-efficiency, Ultra-Wide Bandgap (UWBG) semiconductors crucial for power grid stability and performance. Power (physics) ; Grid ; Path (computing) ; Race (biology) ; Power grid ; Energy (signal processing) ; Power consumption ; Smart grid Ga2O3 and related materials ; Photovoltaic Systems and Sustainability ; Silicon Carbide Semiconductor Technologies
Authors: Ayman K. El-Sawaf | Amal A. Nassar | Mahmoud F. Mubarak | Mohamed Hemdan
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2026-03-11)
Field: Chemistry — Inorganic Chemistry

Nanoarchitectonics of Metal–organic Framework Technologies for Dual Antibiotic Adsorption and Photocatalytic Degradation in Water Treatment Photocatalysis ; Adsorption ; Water treatment ; Solar energy ; Scalability ; Degradation (telecommunications) ; Sewage treatment Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Thanh D. Do
Source: Analysis & Sensing (article, 2026-03-01)
Field: Chemistry — Spectroscopy

Probing the Missing Links: Mass Spectrometry of Small‐Molecule Clusters on the Pathway to Fibrils and Crystals Small molecules such as amino acids (AAs) and metabolites are often treated as chemically simple species that either remain soluble or crystallize directly into ordered solids. Growing evidence challenges this view, revealing that many metabolites populate rich assembly landscapes containing oligomers and amyloid‐like fibrils that bridge the gap between monomers and crystals. Far from being transient curiosities, these intermediate states can encode structural information that shapes toxicity, polymorph selection, catalysis, and disease progression. In this review, we explore how analytical mass spectrometry (MS), particularly ion mobility spectrometry–MS (IMS–MS) and MS‐coupled ion spectroscopy, has opened a window onto these otherwise hidden assemblies. We show how IMS–MS resolves heterogeneous populations, captures early nucleation events, and exposes the decisive roles of charge balance, counterions, and additives in steering assembly pathways. Through representative examples spanning AAs and metabolites linked to amyloid formation and pathological crystallization, we argue that fibrillar intermediates can serve as deterministic waypoints rather than kinetic dead ends. We also confront the current limits of structural MS, from charge‐induced bias to spectral congestion and modeling challenges, and highlight emerging solutions enabled by gentler ion handling, cryogenic spectroscopy, multidimensional IMS, and data‐driven computation. Together, these advances transform MS from a passive observer into an active guide for discovering and predicting order in small‐molecule assemblies. Ion-mobility spectrometry ; Mass spectrometry ; Nucleation ; Monomer ; Molecule ; Ion ; Fibril ; Dissociation (chemistry) Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Molecular Sensors and Ion Detection
Authors: Gourab Datta | Safura Sharifi | Yaser Banad
Source: Research Square (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

Combinatorial Precursor Set Prediction for Inorganic Materials Synthesis via Graph-Conditioned Sequence Generation Generator (circuit theory) ; Oracle ; Sequence (biology) ; Set (abstract data type) ; Limit (mathematics) ; Generative grammar ; Domain (mathematical analysis) ; Ranking (information retrieval) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials