jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Naveen Karuthodi Mohandas | Sebastián Echeverri Restrepo | Marcel H. F. Sluiter
Source: Journal of Phase Equilibria and Diffusion (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Fine-Tuning Universal Machine-Learned Interatomic Potentials for Applications in the Science of Steels Abstract Recycling steel at scale is hindered by tramp elements such as Cu and Sn, which degrade material properties. Atomistic simulations using foundational machine-learned interatomic potentials (MLIPs) trained on large databases, such as Materials Project, Alexandria, and OMAT, offer a promising approach to study the effects of these impurities. However, fine-tuning these models to specific systems can lead to catastrophic forgetting–the loss of general chemical knowledge acquired during pretraining. Here, we evaluate forgetting in three foundational MLIPs: CHGNet, SevenNet-O, and MACE, by fine-tuning on a data set of bcc-based structures, with Fe atoms only. When evaluated on a subset of the Materials Project data set with a learning rate of 0.0001, the fine-tuned MLIPs of CHGNet and SevenNet-O exhibited only a minor increase in RMSE of 0.047 and 0.022 eV/atom, respectively, indicating markedly minor forgetting. In contrast, fine-tuned MACE exhibited catastrophic forgetting, despite a range of additional strategies such as layer freezing and data set replay. We attribute the catastrophic forgetting to architectural sensitivity. These results highlight the importance of fine-tuning hyperparameters, model architecture, and data set design, with fine-tuned models of CHGnet and SevenNet-O showing some potential for efficient and transferable modeling of recycled steels. Forgetting ; Set (abstract data type) ; Minor (academic) ; Interatomic potential ; Data set ; Scale (ratio) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Mir Sajad Rabani | Meenakshi Shrivastav | Mudasir Fayaz | Anjali Pathak | Mahroof Khan | Sheikh Umar Bashir | Insha Hamid | Humaira Hussain | Jitendra Kumar Sharma | Mahendra K. Gupta
Source: Discover Nano (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Mechanistic advances in microbial nanobiotechnology and their applications in sustainable agriculture, environment and biomedicine Microbial nanobiotechnology represents a groundbreaking convergence of microbiology and nanotechnology offering sustainable and innovative solutions across diverse fields. This review explores the emerging trends in microbial nanobiotechnology with a focus on tools, applications, and challenges. Microorganisms including bacteria, fungi, and algae possess exceptional ability to biosynthesize nanoparticles through environmentally benign and cost-effective biochemical pathways. These biogenic nanoparticles, characterized by their controlled size, shape, and functional properties have demonstrated potential in biomedical applications, including targeted drug delivery, imaging, and antimicrobial treatments. Furthermore, microbial nanotechnology plays a pivotal role in environmental remediation contributing to wastewater treatment and pollution control as well as in industrial applications such as agriculture and energy production. The review highlights the integration of advanced tools like genetic engineering, synthetic biology, which have revolutionized nanoparticle synthesis and characterization. However, significant challenges remain including scalability, biosafety, regulatory concerns, and ethical implications. Addressing these barriers is crucial for advancing microbial nanobiotechnology from research to commercial applications. By providing a comprehensive overview, this review aims to serve as a resource for researchers and practitioners offering insights into the future directions and multidisciplinary collaborations necessary for the growth of field. Microbial nanobiotechnology stands poised to address global challenges in health, environment, and industry marking a new frontier in science and technology. Nanobiotechnology ; Biomedicine ; Applications of nanotechnology ; Biopharmaceutical ; Sustainability ; Multidisciplinary approach Nanoparticles: synthesis and applications ; Microbial Applications in Construction Materials ; Cancer Research and Treatments
Authors: Yongchang Li | Ao Zeng | Rawaid Ali | Shili Fu | Haoyun Dou | Lei Liu | Asif Rasool | Zhou Yang | Hongqing Ma | Hong‐En Wang
Source: Particle & Particle Systems Characterization (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

One‐Pot Hydrothermal Synthesis of Single‐Crystalline BiSbS <sub>3</sub> Microrods for Efficient Visible Photodetection ABSTRACT Bismuth antimony sulfide (BiSbS 3 ) is a promising, low‐cost, and earth‐abundant ternary semiconductor. Its tunable bandgap of 1.2–1.7 eV, combined with high carrier mobility and strong light absorption, makes it ideal for optoelectronic applications. In this study, we have successfully synthesized single‐crystalline BiSbS 3 microrods via a one‐pot hydrothermal process using BiCl 3 , SbCl 3 , and thioacetamide as precursors and demonstrated their promising performance in photodetectors. The structure and composition are confirmed by scanning electron microscopy, X‐ray diffraction, X‐ray photoelectron spectroscopy, and high‐resolution transmission electron microscopy characterizations. The unique 1D morphology of BiSbS 3 significantly enhances light absorption and carrier transport, leading to improved sensitivity and efficiency in light detection. The photodetector based on a single BiSbS 3 microrod manifests an impressive photodetection performance, achieving a switching ratio of 165.2 at a bias voltage of 6 V under 790 nm monochromatic light, a responsivity of 82 A/W, an external quantum efficiency of 18,289%, a detectivity of 2.8 × 10 10 Jones, and fast response times (6.3 ms rise and 4.8 ms fall). These findings highlight the potential of BiSbS 3 microrods for high‐performance, cost‐effective optoelectronic devices. Photodetection ; Quantum efficiency ; Responsivity ; Photodetector ; Band gap ; Photocurrent ; Hydrothermal synthesis ; Visible spectrum ; Photoelectric effect ; Ternary operation 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Gas Sensing Nanomaterials and Sensors
Authors: Cumali Celik | Muhammad Yasar | Khalid J. Alzahrani | Anorgul I. Ashirova | Yashwantsinh Jadeja | R. Roopashree | Subhashree Ray | Krishan Kumar Sah | Sultonmakhmud Polvanov | Khalaf F. Alsharif
Source: Journal of Sol-Gel Science and Technology (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Enhanced photocatalytic degradation of organic pollutants using cu-doped spinel ferrites synthesized via sol-gel auto-combustion: a study of structural, optical, and catalytic properties CR dye, a toxic industrial pollutant discharged at 280,000 tons annually, poses severe environmental risks. In this study, we developed Cu-doped Sr₁₋₂Al₀.₄Fe₁.₆O₄ (x = 0.0, 0.4) spinel ferrites via sol-gel auto-combustion method as efficient photocatalysts for dye degradation. Cu-doping induced significant structural modifications: the crystallite size decreased from 28.011 to 20.438 nm, the lattice parameter contracted from 8.4679 to 8.3801 Å, and the BET surface area increased 3.7-fold (from 6.63 to 24.45 m²/g). The optical bandgap narrows from 2.6 to 2.3 eV, enhancing visible-light absorption. Under optimised conditions (pH 7, 30 °C, 10 mg catalyst, 10 ppm dye, 100 W visible light), Cu₀.₄Sr₀.₄Mn₀.₂Al₀.₄Fe₁.₆O₄ achieved 99.70% CR degradation in 100 min, compared to 52.45% for the undoped catalyst. Performance metrics showed a quantum yield of 2.77 × 10⁻⁶ molecules/photon and space-time yield of 1.39 × 10⁻⁷ molecules/photon. Hydroxyl radicals were identified as the primary reactive species in scavenger studies. The catalyst demonstrated versatility by degrading multiple dyes: Rhodamine B (84.23%), Alizarin Yellow (71.83%), and Methylene Blue (53.10%). The addition of H₂O₂ (6 mM) accelerated the complete degradation to 65 min. The catalyst maintained an efficiency of 91.34% after five cycles, confirming its stability. This Cu-doped spinel ferrite system presents a sustainable solution for the treatment of industrial wastewater. Spinel ; Catalysis ; Degradation (telecommunications) ; Rhodamine B ; Photocatalysis ; Crystallite ; Pollutant ; Methylene blue ; Calcination Magnetic Properties and Synthesis of Ferrites ; Advanced oxidation water treatment ; Advanced Photocatalysis Techniques
Authors: Mengyun Hu | Enlai Wan | Yu Qiao | Shupeng Xu | Shiwen Li | Rui Zhu | Jiamin Guo | Xi-Hao Chen | Heping Zeng
Source: Energy & Environmental Science (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Transient interference-based spectroscopy for molecular-bond sensitive probe of lithium ion batttery The lack of effective methods to track in-situ lithium-ion distribution and structural evolution remains a bottleneck in battery industry, while yet no techniques currently allow in-operando dynamic detection of vicious... Transient (computer programming) ; Spectroscopy ; Lithium (medication) ; Ion ; Analytical Chemistry (journal) ; Track (disk drive) Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Ana María Díez Pascual | José A. Luceño
Source: New Journal of Chemistry (article, 2026-01-01)
Field: Materials Science — Biomaterials

Thermal and barrier properties of poly(3-hydroxybutyrate) hybrid nanocomposites: use of experimental data for reliable prediction <i>via</i> machine learning We report a triple filler strategy, the simultaneous addition of carbon nanotubes, sepiolite and tungsten disulfide, to improve the thermal and barrier properties of poly(3-hydroxybutyrate). Experimental data ; Thermal ; Tungsten ; Filler (materials) ; Carbon fibers ; Thermal barrier coating biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Machine Learning in Materials Science
Authors: Arnav Anney | Sananjay Biswas
Source: World Journal of Nano Science and Engineering (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanomaterials in Energy Storage and Sensing: A Comprehensive Review Nanomaterials have transformed energy storage and sensing, providing innovative answers to fundamental scientific and technical difficulties due to their distinctive physical and chemical features. This review offers a comprehensive and current synthesis of advancements in the design, synthesis, and integration of nanomaterials for energy storage systems and other sensing platforms. The discourse encompasses essential categories and attributes of nanomaterials. It underscores their contribution to improving electrochemical performance in batteries and supercapacitors. It also examines their facilitating role in chemical, biosensing, and physical sensor technologies. An analysis of significant accomplishments in hybrid materials, multifunctional device architectures, and data-driven smart sensor systems is presented. Ongoing difficulties in scalability, enduring stability, and practical application are examined. Recent breakthroughs, including two-dimensional materials, nano-hybrids, and AI-integrated sensor networks, are emphasized, providing essential insights for future study and practical application. The analysis closes by pinpointing significant knowledge deficiencies and delineating prospective avenues for interdisciplinary advancement in nanomaterial-based energy and sensor technologies [1]-[3]. Nanomaterials ; Energy storage ; Electrochemical energy storage ; Energy (signal processing) ; Efficient energy use ; Computer data storage Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Carbon and Quantum Dots Applications
Authors: Idris Temitope Bello | Saroj Karki | N Xu | Anshu Kumari | Shuanglin Zheng | Yuqi Geng | Guntae Kim | Shanwen Tao | Y. Liu | Hanping Ding
Source: Advanced Functional Materials (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Heterojunction Architecture Resolves the Stability‐Performance Paradox in Reversible Protonic Ceramic Cells ABSTRACT The practical deployment of protonic ceramic cells (PCCs) is currently impeded by an intrinsic trade‐off between chemical stability and electrochemical performance. Here, we resolve this paradox through a heterojunction architecture that synergistically integrates a high‐conductivity, Zn‐modified chemically stable electrolyte with a self‐assembled, multiphasic oxygen electrode, PrNi 0.5 Co 0.3 Zr 0.1 Y 0.1 O 3‐δ (PNCZY). This design establishes a functionally graded interface with engineered band alignment, creating a Schottky‐type barrier that selectively suppresses electronic leakage while facilitating rapid proton transport. The resulting device exhibits exceptional bidirectional performance, achieving a peak power density of 956 mW cm −2 at 600°C in fuel cell mode and an electrolysis current density of −2300 mA cm −2 at 1.3 V. Crucially, the cell maintains &gt; 96% Faradaic efficiency under high steam loads, confirming the efficacy of the carrier‐selective interface. This work provides a generalizable blueprint for decoupling material properties through interfacial engineering, paving the way for durable, high‐efficiency energy conversion technologies. Faraday efficiency ; Ceramic ; Heterojunction ; Decoupling (probability) ; Electrochemistry ; Electrolyte ; Power density ; Current density Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Thermal Expansion and Ionic Conductivity
Authors: Dionysios Sema | Ngoc Cuong Nguyen | Spencer Wyant | Nicolas G. Hadjiconstantinou
Source: npj Computational Materials (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Environment-adaptive machine-learned force fields for materials under extreme conditions: hafnium and hafnium dioxide polymorphs Advances in machine-learned interatomic potentials have enabled the prediction of complex material properties with accuracy approaching that of ab initio methods. However, it is unclear how the finite capacity of such models affects their ability to achieve consistent accuracy across diverse thermodynamic conditions without introducing trade-offs. In this paper, we present two computationally efficient interatomic potentials capable of accurately simulating the behavior of hafnium and hafnium dioxide across a very wide variety of thermodynamic conditions. Our approach combines Latin Hypercube and Monte Carlo Sampling for generating diverse data sets, with an extended formulation of the recently-developed environment-adaptive proper orthogonal descriptors. Molecular dynamics simulations show that the resulting potentials accurately reproduce density functional theory results and experimental data for pressure- and temperature-induced phase transitions as well as other properties associated with the materials’ polymorphs and liquid phases. We further showcase the versatility of the environment-adaptive formulation by using our potential to compute the shock Hugoniot of hafnium up to temperatures and pressures of 1 MK and 1 TPa, respectively; good agreement with available experimental data is observed. Hafnium ; Molecular dynamics ; Ab initio ; Monte Carlo method ; Ab initio quantum chemistry methods ; Density functional theory ; Interatomic potential ; Material properties Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Chemical Physics Studies
Authors: Alhumam A. Al-Shammari | Bashir Y. Al-Zaidi | Ali Al-Shathr
Source: Reactions (article, 2026-03-11)
Field: Chemistry — Inorganic Chemistry

Dual Roles of Coke in Fresh and Modified HY Zeolite Catalyzed Aromatic Alkylation: Mechanisms, Structural Transformations, and Catalyst Regeneration Linear alkylbenzene (LAB) is the main raw material used to make biodegradable detergents, and its production process is based on aromatic alkylation. HY zeolites that have undergone controlled dealumination and desilication have led industrial standards amongst solid acid catalysts because of their controllable acidity and hierarchical pore structure. Coke formation in such systems can assume a dual role, which is dependent on its condition. Though the over-deposition is known to cause deactivation by blocking the micropores, Bronsted acid-site masking, and diffusion collapse, the low-level deposition could also be done to increase the monoalkylate selectivity by the pore mouth catalysis, steric modulation, and selective suppression of secondary alkylation pathways. The critical review is done on the structural-kinetic interaction that determines the coke evolution in HY-based catalysts. In order to moderate the acid-site density and enhance hydrothermal stability, dealumination (Si/Al optimization of about 2.5 to 30–100) occurs, but to reduce deep-pore coke formation, desilication (interconnected mesopores) is created. The bimodal porosity and regulated acidity are found to be synergistic, as hierarchical HY zeolites produced through successive cycles of steam and alkaline treatments not only show LAB selectivity in excess of 90% but also exhibit much longer catalyst lifetimes. Quantitative research on the beneficial coke regime revealed that it was composed of about 36 wt% hydrogen-rich species, which were localized at the pore mouths, hence enhancing monoalkylation selectivity by 15–40%. Beyond a critical transition window (e.g., 8–12 wt.%), coke formation to condensed polyaromatic and graphitic products leads to fast deactivated coke formation, which is due to percolation limits and transport-controlled kinetics. More advanced techniques of characterization of the coke, e.g., temperature-programmed oxidation (TPO), 27Al MAAS NMR, and UV-Raman spectroscopy, indicate how the coke is changed to highly structured graphitic deposits of high oxidation activation energy. Activity recovery of 85–98% is obtained in regeneration processes, including controlled oxidative calcination, microwave-based and plasma-based processes, and thermal management protocols, and it would be determined by the chemistry of the coke, its spatial distribution, and the regeneration protocols. This paper has developed a mechanistic coke control system by cross-tuning the acidity and development of an effective pore network, which led to a sustainable aromatic alkylation reaction with minimal activity loss, high selectivity, and long life. Coke ; Catalysis ; Zeolite ; Selectivity ; Porosity ; Brønsted–Lowry acid–base theory ; Molecular sieve ; Mesoporous material Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Thermochemical Biomass Conversion Processes
Authors: Li‐Cheng Xu | Junyi An | Weiqi Liu | Yunfei Shi | Chong‐Lei Ji | Fenglei Cao | Qi Yuan
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A unified framework for automated transition state generation to accelerate mechanistic exploration in organic synthesis Transition states (TS) are pivotal for elucidating reaction mechanisms, but their calculation constitutes a major bottleneck. While generative AI provides a route to automate transition state generation, existing methods remain confined to simplified chemical systems, lacking generalizability to complex reactions like transition metal catalysis. Here, we present a unified framework for general-purpose TS discovery, comprising the UniTS-Lib library of 4,391 high-fidelity structures spanning 42 elements and diverse chemical transformations, coupled with the UniTS-Gen diffusion model that generates 3D TS configurations from 2D reactant graphs using a higher-degree equivariant network. Validation demonstrates UniTS-Gen's superior structural accuracy and robust generalization to unseen chemical systems. We show that UniTS-Gen provides highly reliable initial guesses and actively accelerates discovery by locating kinetically preferred TS conformation. This work provides a scalable and transferable solution for automating mechanistic studies in organic synthesis. Generalizability theory ; Generalization ; Scalability ; State (computer science) ; Organic synthesis ; Generative grammar ; Transition (genetics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dong-Hui Chen | Christof Wöll
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

f‐Block Element‐Based MOF Thin Films: A Platform for Luminescence, Sensing, and Energy Applications f-Block element-based metal-organic framework (f-MOF) thin films, incorporating lanthanides and actinides, have emerged as a promising class of materials due to their unique properties and potential in sensing, luminescence, and energy-related applications. While MOF powders have been widely explored, many advanced applications—particularly in optics, electronics, and heteroepitaxial integration-require the controlled architecture and substrate interface that only thin films can provide. This review comprehensively summarizes recent advances in the synthesis, characterization, and functional deployment of f-MOF thin films, highlighting diverse fabrication techniques such as composite MOF particle assembly, layer-by-layer deposition, in situ solvothermal deposition, and electrodeposition. Tabulated data covering polymeric matrices, self-supporting structures, substrates, and synthesis conditions are included to support comparative analysis. The wide range of demonstrated applications, including luminescence sensing, anti-counterfeiting, radiation detection, and catalysis, are critically assessed alongside challenges in film stability, scalability, and performance optimization. Finally, future research directions are proposed, emphasizing the need for innovative synthetic strategies, advanced characterization tools, and tailored functional designs to fully exploit the potential of f-MOF thin films in next-generation technologies. Thin film ; Characterization (materials science) ; Fabrication ; Exploit ; Substrate (aquarium) ; Metal-organic framework ; Composite number Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Qianyu Zheng | Victor Fung
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining Machine learned interatomic potentials (MLIPs) have emerged as powerful tools for molecular dynamics (MD) simulations with their competitive accuracy and computational efficiency. However, MLIPs are often observed to exhibit un-physical behavior when encountering configurations which deviate significantly from their training data distribution, leading to simulation instabilities and unreliable dynamics, thus limiting the reliability of MLIPs for materials simulations. We present a physics-informed pretraining strategy that leverages simple physical potentials which can improve the robustness and stability of graph-based MLIPs for MD simulations. We demonstrate this approach by deploying a pretraining-finetuning pipeline where MLIPs are initially pretrained on data labelled with embedded atom model potentials and subsequently finetuned on the quantum mechanical ground truth data. By evaluating across three diverse material systems (phosphorus, silica, and a subset of Materials Project) and three representative MLIP architectures (CGCNN, M3GNet, and TorchMD-NET), we find that this physics-informed pretraining consistently improves both prediction accuracy as well as stability in MD compared to the baselines. Robustness (evolution) ; Limiting ; Reliability (semiconductor) ; Pipeline (software) ; Stability (learning theory) ; Interatomic potential ; Ground truth ; Molecular dynamics Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly