jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrycja Kwiek | Valentin Mirčeski | Szymon Wójcik | Katarzyna Jedlińska | Małgorzata Jakubowska
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Explainability of Linear and Nonlinear Regression Models in Voltammetric Analytical Signals Using SHAP Interpretability ; Nonlinear system ; Nonlinear regression ; Voltammetry ; Linear regression ; Partial least squares regression ; Differential pulse voltammetry ; Linearization ; Relevance (law) Spectroscopy and Chemometric Analyses ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Luhe Zhuang | Yanshen Sun | Jun Zhao | Hong Wang
Source: Applied Soft Computing (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Dual-stage learning framework fusing semantics and physical knowledge for molecular property prediction Property (philosophy) ; Semantics (computer science) ; Knowledge representation and reasoning ; Training set ; Knowledge-based systems Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kensuke Takaki | Shigeru Furuichi | M. Kosugi | Hiroki Arisawa | Y.-C. Lin | T. Taniguchi | K. Watanabe | Yasuhide Nonaka | K. Suenaga | Eiji Saitoh | J. Haruyama
Source: APL Electronic Devices (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Stochastic behaviors of electrons through atom-vacancy defects in few atom-layer MoS2-FETs and neuromorphic device application Atom-vacancy defects existing in various materials yield numerous physical phenomena, hindering high performance in electronic and photonic devices in some cases. These become more significant in two-dimensional (2D) (atomically) thin van der Waals (vdW) layers. Meanwhile, their applications to novel devices have gathered significant attention. Neuromorphic (NM) computation, which directly mimics the probabilistic operation of the human brain through ion transport, is such an effective example due to its potential in developing power- and size-efficient artificial intelligence applications. While magnetic tunnel junctions currently represent the mainstream approach in NM computation, they require local magnetic fields, posing integration challenges with CMOS circuits. Here, we demonstrate that sigmoid circuits—representative NM circuits—can be operated only by leveraging low-frequency noises [noise currents (IN) and power spectrum density (PSD)]—originating from the stochastic capture and emission of electrons through atom-vacancy defects—in few-atom-layer semiconductor molybdenum disulfide field-effect transistors. Modulating the back-gate voltage allows control of the magnitude of electrical hysteresis loops in the current and IN (PSDs), thereby enabling and regulating the stochastic electron behaviors and emergence of the observed sigmoid operation. This work paves the way for the development of NM circuits through defect engineering in vdW 2D semiconductors, eliminating the need for magnetic fields. Neuromorphic engineering ; Electron ; Quantum tunnelling ; CMOS ; Sigmoid function ; Probabilistic logic ; Voltage ; Electronic circuit ; Nanoelectronics 2D Materials and Applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: P. Rajkumar | S. Kokila | A. Subash | M. Suresh
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI- ENHANCED CATALYTIC KINETICS: ROLE OF METAL NANOPARTICLES IN THE REDOX DYNAMICS OF COBALT(III) BIOMOLECULES IN MICELLAR MEDIA The catalytic oxidation kinetics of pentaamminecobalt(III) complexes containing α-hydroxy acid ligands (glycolate, lactate, and mandelate) were investigated using permanganate ion in micellar media. Metal nanoparticles (~20 nm) were synthesized by chemical reduction and characterized by transmission electron microscopy (TEM) and dynamic light scattering (DLS). Oxidation reactions were monitored spectrophotometrically at 525 nm. The reactions followed pseudo-first-order kinetics with respect to permanganate concentration. The catalytic efficiency increased significantly in the presence of nanoparticles and surfactant aggregates such as cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS). Rate constants were analyzed using Michaelis–Menten kinetics, suggesting saturation-type catalytic behavior. Activation parameters derived from Eyring plots showed negative entropy values indicating formation of an ordered transition state. Furthermore, artificial intelligence models were proposed to predict reaction rates using parameters such as nanoparticle size, surfactant concentration, and ligand environment. The results demonstrate that nanoparticle-mediated micellar catalysis offers an efficient approach to accelerate redox reactions in cobalt coordination systems. Catalysis ; Redox ; Permanganate ; Bromide ; Nanoparticle ; Kinetics ; Reaction rate constant ; Pulmonary surfactant ; Chemical kinetics Porphyrin and Phthalocyanine Chemistry ; Metal complexes synthesis and properties ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Xuelian Yin | Haifeng Wang | Huanhuan Su | Xichuang Guo | Teng Li | Qing Shen | Yuying Fu | Yun Zhai
Source: Comprehensive Reviews in Food Science and Food Safety (article, 2026-01-01)
Field: Materials Science — Biomaterials

Nature‐Inspired Biomineralization for Food Innovation: Biomimetic Material Design and Targeted Nutrient Delivery Biomineralization, a process wherein biomolecules precisely regulate inorganic mineral formation, has inspired nature-derived biomineralization strategies widely applied in materials science and biomedicine. In recent years, this approach has gradually made inroads into food science, offering innovative solutions to critical challenges in the food industry, including packaging, processing, safety detection, and nutrient fortification. This article systematically reviews advances in biomineralization strategies, from molecular mechanisms to food applications over the past 25 years, highlighting its contributions in: (1) intelligent packaging, (2) controlled release of functional ingredients, and (3) functional food development. Research demonstrates that the biomineralization approach, leveraging its biocompatibility, structural tunability, and green synthesis characteristics, enhances the mechanical properties and antimicrobial functions of food packaging while optimizing food texture and flavor profiles. Furthermore, organic template-regulated mineralization and physicochemical strategies have opened new avenues for targeted delivery and controlled release of bioactive components/additives. Despite persistent challenges in scaling production and safety evaluation, the deep integration of biomineralization with synthetic biology, nanotechnology, and artificial intelligence has the potential to support the transformation of the food industry toward greater efficiency, automation, and sustainability. This strategy holds significant potential for reshaping food production methodologies and providing interdisciplinary support for precision nutrition and food safety initiatives. Biomineralization ; Mineralization (soil science) ; Biomimetic materials ; Design elements and principles ; Food industry ; Food products ; Food processing ; Process (computing) ; Applications of nanotechnology Calcium Carbonate Crystallization and Inhibition ; Nanocomposite Films for Food Packaging ; Microbial Applications in Construction Materials
Authors: Chenyingqi Teng | Houhua Zhu | Fu-Xue Chen
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

AI-Assisted design and DFT screening prioritize Y-SCN thiocyanating reagents Heterolysis ; Reagent ; Homolysis ; Electrophile ; Prioritization ; Dissociation (chemistry) ; Workflow Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Computational Drug Discovery Methods
Authors: Kristjan Eimre | Matthew L. Evans | Bud Macaulay | Xing Wang | Jusong Yu | Nicola Marzari | Gian-Marco Rignanese | Giovanni Pizzi
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

optimade-maker: Automated generation of interoperable materials APIs from static data Atomistic structural data are central to materials science, condensed matter physics, and chemistry, and are increasingly digitised across diverse repositories and databases. Interoperable access to these heterogeneous data sources enables reusable clients and tools, and is essential for cross-database analyses and data-driven materials discovery. Toward this aim, the OPTIMADE (Open Databases Integration for Materials Design) specification defines a standard REST API for atomistic structures and related properties. However, deploying and maintaining compliant services remains technically demanding and poses a significant barrier for many data providers. Here, we present optimade-maker, a lightweight toolkit for the automated generation of OPTIMADE-compliant APIs directly from raw atomistic structure and property data. The toolkit supports a wide range of raw datasets, enables conversion to a standardised OPTIMADE data representation, and allows for rapid deployment of APIs in both local and production environments. We further demonstrate it through an automated service on the Materials Cloud Archive, which automatically creates and publishes OPTIMADE APIs for contributed datasets, enabling immediate discoverability and interoperability. In addition, we implement data transformation pipelines for the Cambridge Structural Database (CSD) and the Inorganic Crystal Structure Database (ICSD), enabling unified access to these curated resources through the OPTIMADE framework. By lowering the technical barriers to interoperable data publication, optimade-maker represents an important step toward a scalable, FAIR materials data ecosystem integrating both community-contributed and curated databases. Interoperability ; Discoverability ; Cloud computing ; Raw data ; File format ; SOAP ; Software deployment ; Data access ; Standardization ; Data exchange Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Lei Ma | Jitao Shang | Hao Chen | Yuyang Yin | X. Zeng | Yingwei Li | Sheng Liu | Jian Lu
Source: Nano Materials Science (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic layer removal in silver via heating Artificial intelligence triggered device miniaturization and it requires materials to be processed easily and the associate fabrication techniques of much high precision, approaching atomic scale precision. However, thermal performances of materials at small scale are essential to the fabrication process. Although researchers have tried to apply sublimation of materials as potential atomic scale fabrication technique for device miniaturization, there exists limited controllability for possible applications. In this work, we studied the thermal performances of silver nanowires (Ag NWs) under in situ transmission electron microscopy (TEM) and found two distinctive but inherently related phenomena. The Ag NWs evaporated discontinuously at nano scale and left with sharp edges of Ag (111) plane. With high resolution TEM imaging, the atomic Ag (111) planes sublimate layer by layer. Furthermore, the sublimation rate of atomic Ag (111) plane is analyzed for faithful controllability. Thus so, such atomic layer removal (ALR) process in silver achieved simply by heating has great potential to be used as efficient atomic manufacturing technique to promote device miniaturization to facilitate the blooming artificial intelligence. Fabrication ; Atomic units ; Sublimation (psychology) ; Miniaturization ; Atomic layer deposition ; Nanowire ; Transmission electron microscopy ; Thermal Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Junctions and Nanostructures ; Nanomaterials and Printing Technologies
Authors: Anshu Raj | Xin Wang | Matthew Luebbe | Haiming Wen | Kun Lu | Shuozhi Xu
Source: Data in Brief (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

A dataset of precipitate-containing multi-principal element alloys We report a curated dataset that brings together composition, processing conditions, microstructural details, and mechanical properties for 396 combinations of alloy composition and processing condition drawn from 100 peer-reviewed research articles on precipitate-containing multi-principal element alloys (MPEAs). The dataset was created by first utilizing a generative large language model for information extraction, followed by expert review to ensure accurate recovery of materials data. Compositional information was taken directly from tables and text, while processing routes - including homogenization, rolling, recrystallization, and aging - were converted into uniform temperature and time metrics. Microstructural descriptors, including precipitate phases and sizes, were consolidated into a consistent labeling scheme to accommodate the wide range of terminology used in published literature. Finally, mechanical property data, such as strength and ductility, were compiled together with the temperatures at which they were measured. These data provide a coherent view of the composition-processing-microstructure-property features explored in existing MPEA research and establish a resource that supports data-driven alloy design as well as future development of automated materials information-extraction methodologies. The complete dataset is available on Zenodo. Terminology ; Element (criminal law) ; Resource (disambiguation) ; Range (aeronautics) ; Generative grammar ; Property (philosophy) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Kai Li | Xiaoyang Lu | Baozhen Hao | Xinyi Yao | Qiyang Gu | Jiaqi He | Sha Liu
Source: Microchemical Journal (article, 2026-02-01)
Field: Chemistry — Analytical Chemistry

Harnessing nanomaterials for enhanced detection of radioactive metal ions: recent advances and analysis Human health ; Environmental pollution ; Nanomaterials ; Nuclear imaging ; Heavy metals ; Emerging technologies Analytical chemistry methods development ; Chemical Synthesis and Characterization ; Molecular Sensors and Ion Detection
Authors: Xing Yang | Tianqi Wang | Yan Zhang | Lei Qin | Chen Zhang | Xiaohong Liu | Cheng‐Xing Cui | Wei Zhang | Lili Zhao
Source: The Journal of Physical Chemistry Letters (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Integrating DFT and Machine Learning for Mechanistic Elucidation and Catalyst Optimization in DAP-Catalyzed Asymmetric Aza-Mislow–Evans Reactions We report a data-driven framework that integrates density functional theory (DFT) with machine learning (ML) to elucidate the reaction mechanism and guide catalyst optimization in the DAP-catalyzed reductive asymmetric aza-Mislow-Evans reaction. DFT calculations reveal a five-step catalytic cycle comprising hydride generation (<b>DAP-H</b>), conjugate addition to the acrylamide substrate, [2,3]-σ rearrangement to form a boron enolate, reductive cleavage of the sulfenate ester with catalyst turnover, and hydrolysis/tautomerization to deliver the chiral α-hydroxyamide product <b>(<i>R</i>)-2a</b>. The conjugate addition is identified as the rate-determining step, with a calculated free energy barrier of 20.6 kcal/mol. By training an ML model on DFT-derived descriptors, we identified heterolytic P-H bond dissociation energy, entropy, and the LUMO energy as the key predictors of reactivity. Guided by these features, we designed a series of new DAP catalysts (<b>DAP-H-x</b>, <b>x</b> = <b>a</b>-<b>e</b>) that are predicted to lower the rate-determining barrier (16.2-19.6 kcal/mol) while achieving near-perfect enantioselectivity (ee > 99.9%). This study demonstrates a generalizable DFT-ML strategy that not only deepens mechanistic understanding but also accelerates the rational development of asymmetric main-group catalysis. Heterolysis ; Density functional theory ; Catalysis ; Conjugate ; Dissociation (chemistry) ; Catalytic cycle ; Enantioselective synthesis ; Reaction mechanism Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Mohd Saeed | Taha Y. Alqahtani | Humood Al Shmrany | Garima Gupta | Khang Wen Goh | Amirhossein Sahebkar | Prashant Kesharwani
Source: Expert Opinion on Therapeutic Targets (article, 2026-01-28)
Field: Materials Science — Biomaterials

Molecular targets in gastric cancer for nanomedicine therapeutics: mechanistic insights and translational progress Nanomedicine holds strong potential to transform gastric cancer therapy through selective, target-driven interventions. However, successful translation requires better molecular stratification, deeper insight into nano - bio interactions, standardized toxicity evaluation, and scalable manufacturing. Integrating genomics, biomarkers, and AI-driven design will be crucial for developing nanotherapeutics that address specific molecular vulnerabilities in gastric cancer. Nanomedicine ; Cancer ; Translation (biology) ; Cancer therapy ; Bystander effect ; Translational research ; Translational medicine Nanoparticle-Based Drug Delivery ; Gastric Cancer Management and Outcomes ; Helicobacter pylori-related gastroenterology studies
Authors: Lifei Chen | Xuetao Yan | Tianliang Li | Kaixuan Cui | Lin Li | Zeyu Li | Yingying Chen | Zhenzhen Li | Lei Feng
Source: Aggregate (article, 2026-02-28)
Field: Chemistry — Organic Chemistry

Interpretable Active Learning for Discovering G‐Quadruplex Deep‐Eutectic Circularly Polarized Luminescence Materials With High Dissymmetry Factor and Quantum Yield ABSTRACT Circularly polarized luminescence (CPL) materials, which exhibit their unique chiroptical properties, display great potential for applications in optoelectronics and bioimaging. However, it remains a significant challenge to synthesize CPL materials with high dissymmetry factors (g lum ) and photoluminescence quantum yields (PLQY) simultaneously. Herein, we report a deep‐eutectic‐solvent (DES)‐assisted self‐assembly protocol integrated with an active‐learning (AL) framework that enables the targeted fabrication of G‐quadruplex (G4) supramolecular gels with high g lum and PLQY. AL pinpointed the optimal synthesis parameters in just four iterations, dramatically accelerating material development. The top‐performing gel achieved a g lum of 0.29, setting a new benchmark for nucleoside/nucleotide‐based CPL materials. The maximum PLQY reached 10.64%, which represents a substantial level of performance. Furthermore, by integrating SHapley Additive exPlanations (SHAP), we elucidated the relationship between reaction parameters and target properties. Building on this result, we also demonstrated multicolor fluorescence resonance energy transfer (FRET) by incorporating dyes, successfully developing a series of multicolor CPL‐active materials. This work not only provides new insights into the design of bio‐based chiral CPL materials but also highlights the promising role of artificial intelligence in advancing material development. Luminescence ; Photoluminescence ; Quantum yield ; Förster resonance energy transfer ; Fabrication ; Quantum dot ; Benchmark (surveying) ; Quantum Synthesis and Properties of Aromatic Compounds ; Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials
Authors: Meng He | Qiongya Huang | Yu Zhang | Xing Guo | Shaobing Zhou
Source: Polymer science & technology. (article, 2026-03-18)
Field: Materials Science — Biomaterials

Zwitterion-Based Polymeric Carriers for Efficient Drug Delivery across Biological Barriers Polymeric carriers, by virtue of their tunable physicochemical properties, serve as critical platforms for drug delivery. To enhance therapeutic efficacy, considerable effort has been devoted to functionalizing polymeric carriers and exploring alternative routes of administration. However, regardless of the administration modality, polymeric carriers must overcome multiple physiological barriers during delivery. In this context, zwitterionic-rich polymeric carriers exhibit superior penetration across physiological barriers, attributable to their remarkable hydration capacity and resistance to protein adsorption. Understanding how the zwitterion structure governs barrier penetration is critical for designing drug delivery systems with enhanced tissue targeting. This review summarizes the design strategies of zwitterion-based polymeric carriers, focusing on their structure–activity relationships and mechanisms for overcoming specific physiological barriers via different administration routes. Finally, we provide perspectives on harnessing artificial intelligence for the intelligent design of zwitterion-based polymeric carriers toward their accelerated clinical translation. Drug delivery ; Drug carrier ; Zwitterion ; Penetration (warfare) ; Drug ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Lanthanide and Transition Metal Complexes ; Polymer Surface Interaction Studies
Authors: Haoyue Lu | Yan Wang | Hao Sun | Jing Liu
Source: Advanced Functional Materials (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Dual‐Gate Ambipolar MoTe <sub>2</sub> Transistor Enabled Reconfigurable Logic and Memory for Next‐Generation Computing ABSTRACT The rapid expansion of artificial intelligence (AI) and other data‐driven applications demands energy‐efficient and compact computing architectures that overcome the limitations of conventional von Neumann systems. 2D semiconductors offer promise for reconfigurable logic, but existing designs face a trilemma between integration density, functionality, and operational stability. Here, we present a dual‐gate field‐effect transistor based on a graphene/h‐BN/MoTe 2 /h‐BN heterostructure that unifies four reconfigurable logic operations (AND, OR, NAND, NOR) and nonvolatile n‐/p‐type memory within a single device. All logic operations are achieved under an identical back‐gate voltage, enabled by synergistic optoelectronic modulation of MoTe 2 polarity, yielding a 90 % reduction in transistor count compared to conventional CMOS. The device demonstrates robust reconfigurability through more than 24 full cycles of direct, arbitrary switching among four distinct polarities without intermediate states. Dynamic switching between logic and memory modes is controlled by the graphene top gate, where memory operation delivers a 10 4 on/off ratio and &gt;1000 s retention. Finally, we demonstrate logic‐in‐memory computing and image processing tasks, underscoring the device's potential for next‐generation energy‐efficient computing architectures. Reconfigurability ; Von Neumann architecture ; Transistor ; Logic gate ; Neuromorphic engineering ; Programmable logic device ; Reconfigurable computing ; Reduction (mathematics) ; Dram ; Logic family 2D Materials and Applications ; Graphene research and applications ; Advanced Memory and Neural Computing