jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sana Kashgouli | S. Shayan Mousavi Masouleh | Ali Malek | Robert W. Black | Parth Popli | Vishaal Bharadwaj Hokkalike Dinakara | Michael S. Freund | Mita Dasog | Khalid Fatih
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Accelerating Green Hydrogen Research: An AI-Driven Framework for Efficient Knowledge Discovery and Benchmarking in Proton Exchange Membrane Water Electrolysis Addressing the global challenge of climate change demands transformative technologies capable of decarbonizing key sectors of the economy. Green hydrogen, produced via renewable energy-powered water electrolysis, has emerged as a cornerstone of this transition, offering a pathway to reduce carbon emissions. Proton exchange membrane water electrolysis (PEMWE) plays a pivotal role in this transition, in particular through the utilization of earth-abundant catalysts for the key reactions of oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), which requires targeted analysis. Traditional search methods often fail to capture the complexity of specific queries, leading to time-consuming and inefficient processes for researchers. To address these challenges, we present an AI-powered framework that integrates large language models (LLMs), domain-specific ontologies, and knowledge graphs to streamline the discovery, ranking, and benchmarking of relevant publications. By leveraging ontologies like the Elementary Multiperspective Material Ontology (EMMO), this approach organizes and standardizes material science data, ensuring semantic interoperability and enabling precise querying. Advanced natural language processing (NLP) techniques allow LLMs to extract and structure key information from publications, which is then mapped into knowledge graphs that reveal interconnected relationships between experimental parameters, material properties, and performance metrics. This framework not only reduces the time and effort required to locate and analyze the most pertinent studies but also enhances efficiency and precision. Additionally, it provides actionable recommendations for academia and industry, demonstrating the transformative potential of AI-driven tools in accelerating advancements in PEMWE and green hydrogen technologies. Benchmarking ; Electrolysis ; Electrolysis of water ; Membrane ; Hydrogen ; Computer science ; Chemistry ; Business ; Biochemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Topic Modeling
Authors: Carlos M. O. Bastos | Alexandre C. Dias | Ana Carolina Ferreira de Brito | Ingrid D. Barcelos | Andréia Luísa da Rosa | Danilo Neves Silveira | Maurício J. Piotrowski | Wolfgang Wenzel | Celso R. C. Rêgo | Diego Guedes‐Sobrinho
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

First-principles statistical investigation of thermodynamic behavior with excitonic effects in Mo <sub> 1− <i>x</i> </sub> W <sub> <i>x</i> </sub> Se <sub>2</sub> alloys through a data-driven workflow approach We investigate the thermodynamic stability of the Mo 1− x W x Se 2 alloy at the atomistic level and introduce an innovative cost-effective protocol for predicting and characterizing its optoelectronic properties, explicitly incorporating excitonic effects. Stability (learning theory) ; Workflow ; Alloy ; Material properties ; Work (physics) Machine Learning in Materials Science
Authors: Heng Zhang | Jibin Zhou | Feiyang Xu | Jian Zhong Cui | Yi Li | Fan Yang | Hao Wang | Xin Li | Mao Ye
Source: ArXiv.org (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

A large language model system for the field of chemical engineering technology The development of chemical engineering technology is a multi-stage process that encompasses laboratory research, scaling up, and industrial deployment. This process demands interdisciplinary col laboration and typically incurs significant time and economic costs. To tackle these challenges, we have developed a system based on ChemELLM in this work. This system enables users to interact freely with the chem ical engineering model, establishing a new paradigm for AI-driven in novation and accelerating technological advancements in the chemical sector.If you would like to experience our system, please visit our official website at: https://chemindustry.iflytek.com/chat. Process (computing) ; Field (mathematics) ; Work in process ; Scaling ; Process systems Machine Learning in Materials Science ; Process Optimization and Integration ; Scientific Computing and Data Management
Authors: Karen M. James | Md. Sazedul Islam | Yufang He | Lin Wang | Jarek Viera | Elena Scivally | Susanne Schneider | Xinsong Lin | Peng Xiong | Hanwei Gao | Yan Zeng | Biwu Ma | Bin Ouyang
Source: Chemistry of Materials (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

A Predictive Framework for Discovering Organic Metal Halide Hybrids beyond Perovskites Hybrid metal halides have transformed optoelectronics, yet predictive synthesis remains elusive beyond the perovskite family, to which the Goldschmidt tolerance factor offers guidance. Here, we present an open-source platform (https://apollo-omhh.com) that integrates a density functional theory (DFT) database of 7439 organic metal halide hybrids (OMHHs), derived from 118 experimentally reported compounds spanning diverse electronic structures. Leveraging this data set and remnant stability theory, we introduce a predictive framework to assess OMHH synthesizability. This framework successfully guided the experimental synthesis of eight previously unreported compounds and achieved an 85.2% prediction accuracy across 27 synthesis attempts. This framework is further extended to predict 268 synthesizable OMHHs among 1144 examined OMHHs. Together, these advances establish a data-driven foundation for accelerating the discovery and rational design of hybrid materials beyond perovskite structures. Halide ; Perovskite (structure) ; Density functional theory ; Metal ; Rational design ; Characterization (materials science) ; Stability (learning theory) ; Metal halides Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Balamurugan H | Pujari Harish Kumar | Swetha Shekarappa G | Soumya Ranjan Mishra | Maria Kumar Thelagthoti
Source: Next research. (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Insights into partial discharge mechanisms and space charge dynamics in polymeric insulating materials for electrical power systems Partial discharge ; Reliability (semiconductor) ; Fuzzy logic ; Partial charge ; Dielectric ; Space charge ; Power (physics) ; Dependability ; Probabilistic logic ; Residual High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Kayla James (21571473) | Md Sazedul Islam (17013766) | Yufang He (5196587) | Lin Wang (11986) | Jarek Viera (19850615) | Elena Scivally (21571470) | Spencer Schneider (7363910) | Xinsong Lin (1947037) | Peng Xiong (1459000) | Hanwei Gao (1302297) | Yan Zeng (120271) | Biwu Ma (1280805) | Bin Ouyang (452434)
Source: Figshare (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

A PredictiveFramework for Discovering Organic MetalHalide Hybrids beyond Perovskites Hybrid metal halides have transformed optoelectronics, yet predictive synthesis remains elusive beyond the perovskite family, to which the Goldschmidt tolerance factor offers guidance. Here, we present an open-source platform (https://apollo-omhh.com) that integrates a density functional theory (DFT) database of 7439 organic metal halide hybrids (OMHHs), derived from 118 experimentally reported compounds spanning diverse electronic structures. Leveraging this data set and remnant stability theory, we introduce a predictive framework to assess OMHH synthesizability. This framework successfully guided the experimental synthesis of eight previously unreported compounds and achieved an 85.2% prediction accuracy across 27 synthesis attempts. This framework is further extended to predict 268 synthesizable OMHHs among 1144 examined OMHHs. Together, these advances establish a data-driven foundation for accelerating the discovery and rational design of hybrid materials beyond perovskite structures. Halide ; Perovskite (structure) ; Density functional theory ; Metal ; Stability (learning theory) ; Rational design ; Characterization (materials science) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Zhaoyu Lin (170763) | Xin Luo (428648) | Jianhui Zeng (15237778) | Taoying Rao (19450667) | Yucheng Jiang (1434559) | Linlin Ren (1932709) | Rong Sun (532754) | Yimin Yao (1425382)
Source: Figshare (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Engineering Poly(ionicliquid) Composites for Silicone-FreeThermal Interface Materials: Enhanced Thermal Conductivity and InterfacialAdhesion The rapid advancement of artificial intelligence, coupled with increasing device miniaturization and power density, has made thermal management a critical bottleneck limiting reliability and performance. Thermal interface materials (TIMs), which bridge chips and heat sinks, are essential in reducing thermal resistance. Silicone-based TIMs, despite their widespread use, suffer from weak interfacial bonding, micropore expansion, and delamination under thermal stress, undermining long-term reliability. This study proposes a novel silicon-free TIM system based on poly(ionic liquid)s (PILs), which systematically addresses the interfacial adhesion and thermal stability limitations. Poly(1-dodecyl-3-vinylimidazolium) bis(trifluoromethylsulfonyl)imide (P[lm<sub>12</sub>V]TFSI) exhibits strong interfacial adhesion to metal and semiconductor substrates (5.52 MPa for Cu and 3.92 MPa for Si), with a 5% weight-loss decomposition temperature reaching 280 °C, and reversible ionic bonding that enables self-healing and recyclability. To further enhance thermal conductivity, a dual-particle filler strategy employing high-sphericity silver particles was adopted. The resulting P[lm<sub>12</sub>V]TFSI/Ag composite exhibited a thermal conductivity of 17.2 W m<sup>–1</sup> K<sup>–1</sup> at 80 vol % loading, while maintaining robust interfacial adhesion (1.19 MPa on Si and 1.26 MPa on Cu). The material demonstrates exceptional interfacial compliance, thermal conductivity, and processing durability, presenting a disruptive solution for thermal management in electronics. Thermal conductivity ; Thermal grease ; Thermal ; Composite number ; Thermal stability ; Delamination (geology) ; Adhesion ; Miniaturization Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Advanced ceramic materials synthesis
Authors: Lauren Way | Veronique Charbonnier | Julien O. Fadonougbo | Rebecca Clulow | Lei Lei | Sanliang Ling | David Grant | Martin Dornheim | Tanumoy Banerjee | Hanna Breunig | Claudia Zlotea | Andrew J. E. Rowberg | Pin-Wen Guan | Vitalie Stavila | Mark D. Allendorf | Martin Sahlberg | Kouji Sakaki | Norman C. Bartelt | Matthew D. Witman
Source: Journal of Materials Chemistry A (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides A first principles sampling-based approach to PCT prediction in compositionally complex alloys is employed to improve out-of-distribution prediction accuracy compared to data-driven models. Metal ; Set (abstract data type) ; Key (lock) ; Phase diagram ; Work (physics) Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics
Authors: Noor Ikhsan Kurniawan Setyo Kitton | Muhammad Harabi Sapanlia Hukama | Dina Fitriyah
Source: IOP Conference Series Earth and Environmental Science (article, 2025-12-01)
Field: Materials Science — Biomaterials

Microbial Biopolymers for A Sustainable Materials Food System: A Review Abstract The massive contribution of synthetic polymers (specifically in the food packaging sector, projected to generate over 40 million tonnes of plastic waste annually) necessitates an urgent shift toward sustainable materials. This systematic review addresses the critical research gap regarding the performance discrepancies and economic viability of microbial-derived biopolymers as viable alternatives. We critically analyzed 125 sources published between 2020 and 2024 from Scopus and Web of Science databases, focusing on Polyhydroxyalkanoates (PHA) and Polylactic Acid (PLA). The review quantitatively synthesizes comparative data on barrier properties, degradation kinetics, and life cycle assessment (LCA). Key findings reveal that while PHA exhibits superior biodegradability (often degrading fully within 6 months in composting environments) and better moisture barrier performance, it faces challenges regarding cost-competitiveness and scale-up. Furthermore, the synthesis establishes a new framework for evaluating biopolymer film performance under food-contact conditions, demonstrating that microbial biopolymers offer a 60% lower environmental footprint than polyethylene. This review is intended to provide stakeholders in the food industry with data-driven guidance for transitioning to biopolymers as a genuinely sustainable and environmentally responsible packaging solution. Biopolymer ; Polylactic acid ; Bioplastic ; Life-cycle assessment ; Polyhydroxyalkanoates ; Biodegradation ; Ecological footprint ; Food packaging ; Sustainable agriculture biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Mattia Perrone | David Daniel Girardier | Giovanni M. Pavan | Fabio Pietrucci
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

A rigorous data-driven approach to the nucleation of defects in metals exploiting the link between kinetic properties and (dis)order parameters Nucleation processes, through which a new structure progressively forms within a pre-existing homogeneous phase, are fundamental in materials science, but are also typically non-trivial to elucidate. Cases in which to nucleate are defects (or disorder) in an initially ordered structure make no exception. A prominent example is the nucleation of dislocations in metals, which critically govern their mechanical, electronic, thermal, and chemical properties. While atomic-level insights can be attained using, \textit{e.g.}, molecular dynamics simulations, systematically characterizing nucleation mechanisms and accurately quantifying kinetic rates remain challenging tasks. In this work, we demonstrate how the choice of the order parameter used to track the transition has a very strong effect on the accuracy of the kinetic rate predicted from the corresponding free-energy barrier and diffusion coefficient, a fact that has been often overlooked in the past. By exploiting this systematic error to our advantage, we demonstrate that it is possible to rigorously characterize the nucleation process using a data-driven scheme based on a variational principle, leading to optimal order parameters and a faithful mechanistic description. We apply this method to characterize, as a representative case study, the nucleation of dislocations in crystalline $fcc$ copper by analyzing replica molecular dynamics simulations at the elastic-plastic limit. By means of committor analysis and Langevin modeling, our approach allows to systematically rank candidate (dis)order parameters, identify the critical nuclei (transition states), and infer the free-energy landscapes. Given its general foundations, this method can be extended to nucleation phenomena in a broad class of materials. Link (geometry) ; Nucleation ; Kinetic energy ; Order (exchange) ; Statistical physics ; Materials science ; Computer science ; Physics ; Thermodynamics ; Economics ; Classical mechanics Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: Kailong Xu | Jinzhao Huang | Heng Zhang | Dan Huang | Lulu Liu | Wei Chen | T.E. Tay
Source: Composite Structures (article, 2025-09-22)
Field: Materials Science — Polymers and Plastics

A unified finite strain visco-elastic visco-plastic constitutive model for a thermosetting polymer from low to high strain rates: Experiments, Validation and Bayesian optimization Thermosetting polymer ; Constitutive equation ; Strain rate ; Subroutine ; Finite element method ; Solid mechanics ; Bayesian inference ; Phase transition Polymer crystallization and properties ; Mechanical Behavior of Composites ; Elasticity and Material Modeling
Authors: Bei Xiang | Junjie Yan | Kade A. McGarrity | A. Murugaiah
Source:  (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

High-Performance Low-Loss Ceramic Filler with Enhanced Surface for Next-Generation Thermal Management in Electronics Modern electronic components in electric vehicles (EVs), artificial intelligence (AI), and high-frequency transmission demand higher power, faster data transfer, lighter weight, and compact designs. These advancements increase heat generation and data loss, necessitating thermal management materials with high thermal conductivity, superior electrical insulation, and low loss. Thermally conductive dielectric polymer composites, enhanced with ceramic fillers like hexagonal boron nitride ($\mathbf{h}-\mathbf{B N}$), are key to achieving these requirements. $h-B N$ improves thermal conductivity while maintaining electrical insulation and low transmission loss, though its anisotropic properties can challenge performance. Agglomerated and surface-modified h-BN powders address these issues, enabling uniform thermal performance, reduced viscosity, thinner bond line thickness, and improved adhesion. Surface modification of h-BN enhances compatibility with epoxy resins, reducing viscosity by up to two orders of magnitude and increasing adhesion strength by over $\mathbf{1 0} \boldsymbol{\%}$ compared to epoxy compounded filled with unmodified BN powders. These advancements provide high-performance solutions for next-generation electronics in EVs, AI, and highfrequency applications. Boron nitride ; Epoxy ; Thermal conductivity ; Electronics ; Ceramic ; Electrical conductor ; Thermal stability ; Dielectric ; Filler (materials) Thermal properties of materials ; Advanced ceramic materials synthesis ; Dielectric materials and actuators
Authors: Barbara Smolak | Klaudia Dynarowicz | Dorota Bartusik-Aebisher | Gabriela Henrykowska | David Aebisher | Wiesław Guz
Source: Pharmaceuticals (article, 2025-12-26)
Field: Materials Science — Biomaterials

Combining Fluorescence and Magnetic Resonance Imaging in Drug Discovery—A Review Drug discovery is a complex and multi-stage process that requires advanced analytical technologies capable of accelerating preclinical evaluation and improving the precision of therapeutic design. The combination of fluorescence and magnetic resonance imaging (MRI) within multimodal imaging plays an increasingly important role in modern pharmacokinetics, integrating the high molecular sensitivity of fluorescence with the non-invasive anatomical visualization offered by MRI. Fluorescence enables real-time monitoring of cellular processes, including drug-target interactions and molecular dynamics, whereas MRI provides detailed structural information on tissues without exposure to ionizing radiation. Hybrid probes-such as superparamagnetic iron oxide nanoparticles (SPIONs) functionalized with near-infrared (NIR) fluorophores or gadolinium-based complexes linked to optical dyes-enable simultaneous acquisition of molecular and anatomical data in a single examination. These multimodal systems are being explored in oncology, neurology, and cardiology, where they support improved visualization of tumor biology, amyloid pathology, and inflammatory processes in vascular disease. Although multimodal imaging shows great promise for enhancing pharmacokinetic and pharmacodynamic studies, several challenges remain, including the potential toxicity of heavy-metal-based contrast agents, limited tissue penetration of fluorescence signals, probe stability in vivo, and the complexity and cost of synthesis. Advances in nanotechnology, particularly biodegradable carriers and manganese-based MRI contrasts, together with the integration of artificial intelligence algorithms, are helping to address these limitations. In the future, fluorescence-MRI hybrid imaging may become an important tool in personalized medicine, supporting more precise therapy planning and reducing the likelihood of clinical failure. Molecular imaging ; Fluorescence-lifetime imaging microscopy ; Magnetic resonance imaging ; Visualization ; Fluorescence ; Drug discovery ; Drug Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Lanthanide and Transition Metal Complexes
Authors: *Mr. Abhay Mane, Mr. Kunal Dolas, Ms. Sakshi Dalvi, Mr. Magan Choudhary,       Prof. G. K. Brahma
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-01)
Field: Materials Science — Biomaterials

POLYMERIC NANOPARTICLES IN CANCER THERAPY: A COMPREHENSIVE REVIEW OF TARGETED DRUG DELIVERY SYSTEMS, MECHANISMS, AND CLINICAL TRANSLATION Cancer treatment is still hindered by systemic toxicity, multidrug resistance, and inadequate specificity for tumors. Polymeric nanoparticles (PNPs) have gained recognition as sophisticated drug delivery systems that can overcome these issues due to their biodegradability, compatibility with biological systems, and capability to encapsulate various therapeutic agents. Their nanoscale size allows for passive targeting through the enhanced permeability and retention (EPR) effect, while attaching ligands such as folate, transferrin, and antibodies enables active targeting specific to tumors. This review discusses the main categories of polymeric carriers, including poly(lactic-co-glycolic acid) (PLGA), polyhydroxyalkanoates (PHAs), cyclodextrins, and smart polymers that respond to pH, redox, or enzymatic changes. These systems facilitate controlled drug release, shield drugs from early degradation, and increase their concentration at tumor locations. Their applications encompass chemotherapy, gene therapy, and immunotherapy. and theragnostic strategies that combine therapy with imaging. Although there is substantial preclinical evidence, the clinical application of polymeric nanoparticles (PNPs) is still limited by challenges such as large-scale production, variations in tumor microenvironments, and regulatory hurdles. Nevertheless, advancements in polymer design, biomimetic functionalization, and the integration of artificial intelligence provide hope for addressing these issues. In summary, polymeric nanoparticles represent a groundbreaking approach in precision oncology, facilitating safer, more efficient, and personalized cancer treatment. Drug delivery ; Nanomedicine ; Drug ; Cancer ; Targeted drug delivery ; Cancer therapy ; Gene delivery Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications