jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shikha Baghel Chauhan | Indu Singh | Yakshit Chauhan | Chirag Jain
Source: Biomedical Materials & Devices (article, 2025-12-07)
Field: Materials Science — Biomaterials

Polythioesters as Biodegradable and Stimuli-Responsive Biomaterials for Targeted Drug Delivery Applications Drug delivery ; Flexibility (engineering) ; Targeted drug delivery ; Drug ; Adaptability ; Intracellular ; Biodegradable polymer ; Drug discovery biodegradable polymer synthesis and properties ; RNA Interference and Gene Delivery ; Advanced Polymer Synthesis and Characterization
Authors: Güngör, Ahmet | Saritas, Eminenur | Toloman, Dana | Popa, Adriana | Rostas, Arpad Mihai | Erdem, Emre
Source: Sabanci University (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanoscale high-entropy alloys and oxides for supercapacitor electrodes: size effects, structure-property relationships, and energy storage potential High-entropy alloys and oxides (HEAs and HEOs), composed of multiple principal elements in near-equiatomic ratios, have emerged as promising candidates for supercapacitor electrodes. Their intrinsic features-configurational entropy stabilization, sluggish diffusion, and lattice distortion-enable unique structure-property relationships. When synthesized at the nanoscale, these materials exhibit enhanced surface area, high defect density, and finite-size effects that boost electrochemical activity and stability. This review outlines the evolution of high-entropy materials, their synthesis strategies, and the advantages of nanoscale design for energy storage. We highlight correlations between electronic structure, defect engineering, charge storage mechanisms, and device-level demonstrations in symmetric, asymmetric, and flexible supercapacitors. Remaining challenges include synthesis reproducibility, compositional control, and scalability, while emerging directions point toward hybrid composites, sustainable synthesis, and artificial intelligence-guided discovery. Nanoscale high-entropy alloys and oxides thus provide a versatile platform to advance supercapacitor performance through systematic tuning of size effects and structure-property relationships. Supercapacitor ; Nanoscopic scale ; Energy storage ; Electrochemical energy storage ; Electrochemistry ; Nanostructure Supercapacitor Materials and Fabrication ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Xin Wang | Yunhao Xiao | Rui Qiao
Source: ArXiv.org (preprint, 2025-11-01)
Field: Materials Science — Materials Chemistry

PolyRecommender: A Multimodal Recommendation System for Polymer Discovery We introduce PolyRecommender, a multimodal discovery framework that integrates chemical language representations from PolyBERT with molecular graph-based representations from a graph encoder. The system first retrieves candidate polymers using language-based similarity and then ranks them using fused multimodal embeddings according to multiple target properties. By leveraging the complementary knowledge encoded in both modalities, PolyRecommender enables efficient retrieval and robust ranking across related polymer properties. Our work establishes a generalizable multimodal paradigm, advancing AI-guided design for the discovery of next-generation polymers. Recommender system ; Similarity (geometry) ; Ranking (information retrieval) ; Graph ; Exploit Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kavathekar, Tabbu Salim
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

The Evolution of Chemistry: From Ancient Wisdom to AI Innovation Abstract The history of chemistry shows how humans have always been curious about the world around them and how things change. Starting with early work on metals and medicines, and moving on to complex ideas like quantum mechanics and research powered by artificial intelligence, chemistry has always grown by combining knowledge, experiments, and new technologies. This paper looks at how chemistry has changed over time, from its beginnings in ancient alchemy to today’s advanced methods that use AI. It covers important moments that shaped how chemists think, discusses big changes in how they do their work, and explains how AI and data science are changing the way chemists study molecules, predict chemical reactions, and create new materials. In the end, the story of chemistry shows a path from relying on guesswork and seeing things to using precise methods and making accurate predictions—mixing old knowledge with new technology. Alchemy ; Big data ; History of science ; Work (physics) ; Science history Machine Learning in Materials Science ; History and advancements in chemistry ; Historical Philosophy and Science
Authors: Raphael F. Moral | Maher B. Alghalayini | R.N. Nurdillayeva | Do-Kyoung Lee | Tim Kodalle | Paulo E. Marchezi | David P. Fenning | Marcus M. Noack | Craig P. Schwartz | Carolin M. Sutter‐Fella
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Dissymmetry Factor Enhancement: A Data-Driven Approach to Unravel the Synthesis Knobs of Chiral 2D Perovskites Factor (programming language) ; Perovskite (structure) ; Materials science ; Nanotechnology ; Computer science ; Business ; Engineering ; Chemical engineering ; Programming language Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Li Yuan | Yongyu Wu | Haohui Ou | Di Wu | Yuxin Ji | Dianyu Qi | Wenjing Zhang
Source: Nanomaterials (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Hydrazine Intercalation into 2D MoTe2 Field Effect Transistor as Charge Trapping Sites for Nonvolatile Memory Applications Driven by the demands of artificial intelligence, big data and the Internet of Things, non-volatile memory has become the cornerstone of modern computing. However, at present, most of the preparation processes are quite complex and have high requirements for the materials. Here, we discovered that hydrazine (N<sub>2</sub>H<sub>4</sub>) molecules can be efficiently intercalated into the MoTe<sub>2</sub>, acting as stable charge-trapping centers. This intercalation not only induces a controllable reversible polar conversion but also causes a huge hysteretic window (>60 V) lasting over one hour in air. Leveraging this giant hysteresis, we fabricated a simplified memory device. The device demonstrates a large erase/program current ratio of ~10<sup>4</sup> and excellent retention characteristics. Our work pioneers the use of interlayer molecular intercalation for electronic modulation in 2D semiconductors, offering a new paradigm for developing memory devices with fabrication processes. Intercalation (chemistry) ; Non-volatile memory ; Transistor ; Fabrication ; Trapping ; Field-effect transistor ; Heterojunction ; Molecule 2D Materials and Applications ; Organic and Molecular Conductors Research ; Molecular Junctions and Nanostructures
Authors: Lurdhumary Jl | N Venkateswaran
Source:  (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Assisted Enhanced Thermal and Electrical Performance of Epoxy–GO–MgO Nanocomposites via Hybrid Filler Integration Multifunctional epoxy nanocomposites that are more thermal and electrical conductive have led to the investigation of hybrid filler systems. In this paper, an artificial intelligence (AI)-aided design and optimization methodology of epoxy composites reinforced with a hybrid blend of graphene oxide (GO) and magnesium oxide (MgO) nanoparticles has been introduced. Surface-functionalized hybrid fillers were incorporated into a bisphenol-A epoxy resin through a controlled dispersion test based on mechanical stirring and ultrasonication. It was found that the ratio of a GO:MgO of 1.5:0.5 and a total filler content of 2wt% yielded the best results in terms of giving significant changes in thermal and electrical properties. Thermal conductivity was 1.25 W/m K, which was 78% better than plain epoxy, and still a high-volume resistivity of 8.5 x 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup>0·cm upheld great insulation abilities. The ANN and SVM AI models have been trained using experimental data when the filler ratio, sonication time, and curing temperature are used as inputs. The most successful AI model (GA-ANN) had R2=0.971, and MAPE= 2.7, which confirmed the precision in its predictions and the application in the real-time formulation tuning. The paper shows how material science and data-driven modelling can successfully meet in intelligent material design. The proposed hybrid composite can be used in other areas such as electronics packaging, energy systems and sophisticated thermal interface materials. Future research and development will involve expanding this type of AI-driven method to other polymer-based matrices and the study of the future of self-healing, dynamic composites based on next-generation smart materials. Epoxy ; Graphene ; Nanocomposite ; Filler (materials) ; Curing (chemistry) ; Thermal conductivity ; Composite number ; Thermal ; Nanoparticle Thermal properties of materials ; High voltage insulation and dielectric phenomena ; Carbon Nanotubes in Composites
Authors: Yu Cao | Jiawei Dai | Tzu-Wei Liu | Jinbang Fu | Po−Liang Liu
Source: Surface and Coatings Technology (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

AI-enabled construction and prediction of atomic models for thin-film heterostructures via materials genome approach Heterojunction ; Genome ; Materials science ; Thin film ; Nanotechnology ; Optoelectronics ; Biology ; Genetics ; Gene Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Iroda Ilkhamova | Abutolib Sobirjonov | Gulnora Ernazarova | Guzal Ergasheva | Dilbar Qarshiyeva | Gazalkhon Salohiddinova
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Multiscale simulation frameworks for light-matter interaction in AI-designed materials We describe multiscale simulation protocols that connect light–material interaction and materials-by-design strategies. The frameworks include classical, semiclassical and quantum-mechanical descriptions of photons, electrons and atoms, and link ab initio approaches to large-scale dynamics and optimization algorithms. The schemes help provide insight into the fundamental physics and design of new materials-photonic crystals, plasmonic nanostructures, and organic photovoltaics-that offer promise for resolving fundamental energy and information processing problems. They represent proof-of-principle demonstration of the ability of multiscale methods to tackle the interplay of atomistic, continuum, and field-level phenomena as well as the potential of artificial intelligence in materials design. One of the long-term goals is also to include the schemes in a general data-driven-discovery framework for the computational design of functional materials in the context of different fields related to industrial and mission of government. Context (archaeology) ; Multiscale modeling ; Field (mathematics) ; Semiclassical physics ; Energy (signal processing) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Andrew J. Medford | Todd N. Whittaker | Bjarne Kreitz | David W. Flaherty | John R. Kitchin
Source: ArXiv.org (preprint, 2025-10-21)
Field: Materials Science — Materials Chemistry

Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the "many-to-one" challenge of linking intrinsic kinetics to observables. Advances in machine-learned force fields, microkinetics, and reactor modeling enable rapid exploration of chemical spaces, while operando and transient data provide unprecedented insight. Yet, inconsistent data quality and model complexity limit mechanistic discovery. Generative and agentic AI can automate model generation, quantify uncertainty, and couple theory with experiment, realizing "self-driving models" that produce interpretable, reproducible, and transferable understanding of catalytic systems. Limit (mathematics) ; Key (lock) ; Experimental data ; Kinetics ; Quality (philosophy) ; Catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Zhuoli Jiang | Mufan Li
Source: Nano Research (article, 2025-10-30)
Field: Chemistry — Organic Chemistry

Advances in alcohols electrooxidation catalyzed by atomically dispersed and isolated active sites Alcohol electrooxidation reactions (AORs)—including methanol (MOR), ethanol (EOR), ethylene glycol (EGOR), and benzyl alcohol oxidation (BAOR)—are central to clean energy conversion and selective synthesis of value-added chemicals. Beyond their direct roles in fuel cells, AORs intersect with urgent global priorities such as CO₂ valorization, biomass upgrading, and the green hydrogen economy. Yet their practical deployment is hampered by sluggish kinetics, severe CO poisoning, and poor product selectivity, compounded by inefficient utilization of precious metals. Atomically dispersed and isolated active site catalysts—including single atom catalysts (SACs), dual atom catalysts (DACs), single atom alloys catalysts (SAAs), and nano-single atom ensembles catalysts (NSAs)—have emerged as a transformative paradigm to overcome these bottlenecks. Their unparalleled atomic efficiency, well-defined coordination environments, and tunable electronic structures enable precise regulation of oxophilicity, dynamic control of reaction intermediates, and near-complete suppression of poisoning pathways. This review highlights recent advances in the rational design and synthesis of isolated active site catalysts for AORs, with a focus on their structure–performance relationships, mechanistic insights into alcohol electrooxidation, and emerging principles of selectivity control across mono- and polyhydric alcohols. We further discuss critical challenges—such as catalyst stability, large-scale production, and real-world operando validation—and outline future opportunities in green synthetic strategies, multi-field coupling, and data-driven discovery. By bridging atomic precision with electrochemical function, isolated active sites open a frontier for reimagining alcohol electrooxidation as a cornerstone of sustainable energy and chemical innovation. Catalysis ; Ethylene glycol ; Active site ; Corrole ; Methanol ; Atom economy ; Electrochemistry ; Atom (system on chip) ; Selectivity Oxidative Organic Chemistry Reactions ; Electrocatalysts for Energy Conversion ; Catalysis for Biomass Conversion
Authors: Jeffrey A. Bennett | Milad Abolhasani
Source: Chem Catalysis (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Multimodal robotics and AI for electrocatalyst discovery Electrocatalyst ; Bayesian optimization ; Robotics ; Path (computing) ; eHealth Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Yaksha Verma | Gaurav Sharma | Amit Kumar | Tongtong Wang | Pooja Dhiman
Source: Current Organic Chemistry (review, 2025-10-15)
Field: Chemistry — Organic Chemistry

Nanocatalysts in Modern Organic Reactions: Current Trends and Future Perspective: A Mini Review Nanocatalysts have emerged as transformative tools in present-day organic transformation, facilitating synthesis with exceptional catalytic performance, high selectivity, and sustainability. This review presents advancements in nanocatalysts, emphasizing their structural properties, synthesis methods, and current trends for applications in various organic transformations. From Fe-doped MnO2 for oxidation reactions to biomass-derived chitin-supported Ag catalysts for hydrogen borrowing, diverse catalytic systems have demonstrated superior performance. Core-shell architectures, such as Au@NiOx and MoNi@TiO2, highlight the significance of strong metal-support interactions in enhancing reactivity. Additionally, green and recyclable catalysts, including covalent triazine frameworks (CTF-SO3H) and UiO-66-NH2@SA-PS/CuI, have been developed for selective cross-coupling and carbohydrate valorization. Advances in MOFs, magnetic nanocatalysts, and hybrid systems further underscore the potential of nanocatalysts in sustainable organic synthesis. Future perspectives suggest a shift toward bio-inspired catalysts, AI-driven catalyst optimization, and multifunctional hybrid systems to enhance efficiency and industrial applicability. By integrating advanced characterization techniques, computational modeling, and eco-friendly synthetic approaches, nanocatalysis is poised to revolutionize organic chemistry. This review provides insights into current trends and future directions, emphasizing the role of nanocatalysts in shaping the next generation of sustainable organic transformations. Nanomaterial-based catalyst ; Catalysis ; Current (fluid) ; Transformative learning ; Catalytic efficiency ; Material chemistry Nanomaterials for catalytic reactions ; Inorganic and Organometallic Chemistry ; Multicomponent Synthesis of Heterocycles
Authors: Ruihan Zhou | Francesco Paesani
Source: ChemRxiv (article, 2025-10-10)
Field: Chemistry — Spectroscopy

Toward Chemical Accuracy in Biomolecular Simulations through Data-Driven Many-Body Potentials: II. Polyalanine in Water Biomolecule ; Intramolecular force ; Intermolecular force ; Molecular dynamics ; Aqueous solution ; Transferability ; Modular design ; Formalism (music) Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Bharath L | N Nagabhushana | Kumaraswamy Jayappa | Girisha H N
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Composites communications on AW2024/E-glass/TiC hybrid material by data-driven technique using linear regression model Yield (engineering) ; Linear regression ; Orthogonal array ; Residual ; Material properties ; Regression analysis ; Hybrid system ; Fracture (geology) Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Cesar Ramirez (4705306) | Eman Ahmed (7418249) | Elena Di Mare (22139181) | Maria Pineiro-Goncalves (22139184) | Apostolos Maroulis (22139187) | Prajakatta Mulay (5931410) | D. Christopher Radford (1318776) | Adam J. Gormley (1544047)
Source: Figshare (article, 2025-08-28)
Field: Chemistry — Organic Chemistry

Automation-AssistedPhotoinduced Atom Transfer RadicalPolymerization Oxygen-tolerant reversible-deactivation radical polymerizations (RDRP) now allow many of these reactions to proceed in open labware, such as well plates. This enables the high-throughput synthesis of tailored polymers and lowers the knowledge barrier required to obtain these materials. Building on our previous work automating photoinduced electron/energy transfer reversible addition–fragmentation chain transfer (PET–RAFT) and enzyme-assisted RAFT (Enz-RAFT) polymerization, we now introduce automated atom transfer radical polymerization (ATRP). Here, we demonstrate the potential of this platform for the high-throughput optimization of ATRP chemistry. Furthermore, we demonstrate that this workflow can help provide insights into the selection of reaction components, such as ligands and initiators, for the polymerization of kinetically difficult monomers such as methyl methacrylate with smaller rates of propagation than acrylates. This coupling paves the way for data-driven optimization of ATRP reactions, accelerated by the generation of high-throughput data sets. To facilitate the integration of robotics for high-throughput applications in polymer synthesis and optimization of photo-ATRP, we have made a Python package available to assist with experimental planning. The tool accepts Excel sheets with user-defined molar ratios, target monomer concentrations, and reagent stock concentrations and outputs an Excel sheet with actionable recipes that can be readily implemented in liquid handling transfer steps via manual or automated pipetting. Atom-transfer radical-polymerization ; Chain transfer ; Reversible addition−fragmentation chain-transfer polymerization ; Radical polymerization ; Polymerization ; Monomer ; Cobalt-mediated radical polymerization ; Polymer Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Click Chemistry and Applications
Authors: Miao Cao
Source: Applied and Computational Engineering (article, 2025-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical Study of Photoluminescence Mechanisms in Semiconductor Materials With the advancement of digitalization, artificial intelligence, high-speed communications, vehicle electrification, and carbon neutrality, the demand for optoelectronics and power semiconductors is increasing. Device efficiency and stability depend on the luminescence behavior of semiconductor materials. This study constructs a theory-driven framework for interpreting photoluminescence (PL) by integrating band structure, exciton effects, defect state physics, and temperature-phonon interactions. Theoretical analysis reveals that near-band edge and exciton peaks indicate band gaps and binding energies, while broad low-energy bands indicate deep defects and stronger nonradiative losses. Power and temperature dependence help separate radiative pathways, and the full-width at half-maximum provides a practical indicator of crystal quality and disorder. This framework also clarifies why direct-bandgap materials exhibit efficient luminescence, while indirect-bandgap materials require phonon assistance and exhibit weak PL. However, this framework is limited to theoretical studies, and future research should combine it with quantitative studies for more in-depth analysis. Photoluminescence ; Semiconductor ; Exciton ; Phonon ; Enhanced Data Rates for GSM Evolution ; Radiative transfer ; Semiconductor materials ; Wide-bandgap semiconductor Ga2O3 and related materials ; ZnO doping and properties ; Silicon Nanostructures and Photoluminescence