jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sara Sadat Aghamiri | Rada Amin
Source: Kinases and Phosphatases (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Digital Twin-Based Multiscale Models for Biomarker Discovery in Kinase and Phosphatase Tumorigenic Processes Digital twin is a mathematical model that virtually represents a physical object or process and predicts its behavior at future time points. These simulation models enable a deeper understanding of tumorigenic processes and improve biomarker discovery in cancer research. Tumor microenvironment is marked by dysregulated signaling pathways, where kinases and phosphatases serve as critical regulators and promising sources for biomarker discovery. These enzymes operate within multiscale and context-dependent processes where spatial and temporal coordination determine cellular outcomes. Digital Twin technology provides a platform for multimodal and multiscale modeling of kinase and phosphatase processes at the patient-specific level. These models have the potential to transform biomarker validation processes, enhance the prediction of therapeutic responses, and support precision decision-making. In this review, we present the major alterations affecting kinases and phosphatase functions within the tumor microenvironment and their clinical relevance as biomarkers, and we address how digital twins in oncology can augment and refine each stage of the biomarker discovery pipeline. Introducing this emerging technology for cancer biomarker discovery will assist in accelerating its adoption and translation into precision diagnostics and targeted therapies. Biomarker ; Computational biology ; Biology ; Computer science ; Cancer research ; Genetics Machine Learning in Materials Science ; 3D Printing in Biomedical Research ; Cell Image Analysis Techniques
Authors: Aline Dobrovsky | Konstantin Schekotihin | Christian Burmer
Source: Proceedings - International Symposium for Testing and Failure Analysis (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Intelligent Assistants for the Semiconductor Failure Analysis with LLM-Based Planning Agents Abstract Failure Analysis (FA) is a highly intricate and knowledge-intensive process. The integration of AI components within the computational infrastructure of FA labs has the potential to automate a variety of tasks, including the detection of non-conformities in images, the retrieval of analogous cases from diverse data sources, and the generation of reports from annotated images. However, as the number of deployed AI models increases, the challenge lies in orchestrating these components into cohesive and efficient workflows that seamlessly integrate with the FA process. This paper investigates the design and implementation of an agentic AI system for semiconductor FA using a Large Language Model (LLM)-based Planning Agent (LPA). The LPA integrates LLMs with advanced planning capabilities and external tool utilization, allowing autonomous processing of complex queries, retrieval of relevant data from external systems, and generation of human-readable responses. The evaluation results demonstrate the agent's operational effectiveness and reliability in supporting FA tasks. Workflow ; Reliability (semiconductor) ; Variety (cybernetics) ; Autonomous agent ; Data integration ; Intelligent agent Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; AI-based Problem Solving and Planning
Authors: Ankit Roy | Ram Devanathan | Mohan Sai Kiran Kumar Yadav Nartu | Vineet V. Joshi
Source: Scripta Materialia (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Autonomous alloy composition optimization using molecular dynamics guided by a large language model Molecular dynamics ; Alloy ; Language model ; Composition (language) ; Dynamics (music) Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Nikita A. Krotkov | Dmitrii A. Sbytov | Anna A. Chakhoyan | Polina I. Kornienko | А. А. Старікова | M. E. Stepanov | Anastasiia Piven | Timur A. Aliev | Tetiana Orlova | Mushegh Rafayelyan | Ekaterina V. Skorb
Source: Journal of Chemical Information and Modeling (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Nanostructured Material Design via a Retrieval-Augmented Generation (RAG) Approach: Bridging Laboratory Practice and Scientific Literature The increasing complexity in designing nanostructured materials for electronics, biomedicine, and energy applications requires advanced computational methods to enhance research efficiency and minimize experimental costs. This study proposes an innovative agent-based retrieval-augmented generation (RAG) system integrated with large language models (LLMs) to automate the extraction and analysis of scientific information from extensive literature databases, specifically targeting nanostructured materials developed via two-photon polymerization (2PP). In addition to extracting and analyzing scientific data, our approach emphasizes understanding how these nanostructured materials interact with cells, which is crucial for controlling their application in biomedicine. The developed platform demonstrates robust semantic accuracy (cosine similarity: 0.82) and high overall task precision (0.81), significantly reducing the likelihood of misinformation by incorporating dynamic query refinement mechanisms. The intuitive, user-friendly interface facilitates quick access to relevant scientific data, thereby improving researchers' productivity and enabling more accurate experimental planning. Although the system exhibits certain limitations regarding domain-specific terminology coverage, further fine-tuning and specialized training are anticipated to enhance its performance and reliability for advanced scientific applications. Bridging (networking) ; Terminology ; Materials informatics ; Scientific literature ; Interface (matter) ; Reliability (semiconductor) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Morgan Thomas | Albert Bou | Gianni De Fabritiis
Source: Journal of Chemical Information and Modeling (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Test-Time Training Scaling Laws for Chemical Exploration in Drug Design Chemical language models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their exploration capabilities. Inspired by test-time training (TTT) in large language models, we propose scaling TTT for CLMs to enhance chemical space exploration. We introduce MolExp, a novel benchmark emphasizing the discovery of structurally diverse molecules with similar bioactivity, simulating real-world drug design challenges. Our results demonstrate that scaling TTT by increasing the number of independent RL agents follows a log-linear scaling law, significantly improving exploration efficiency as measured by MolExp. In contrast, increasing TTT training time yields diminishing returns, even with exploration bonuses. We further evaluate cooperative RL strategies to enhance exploration efficiency. These findings provide a scalable framework for generative molecular design, offering insights into optimizing AI-driven drug discovery. Scalability ; Chemical space ; Benchmark (surveying) ; Limiting ; Scaling ; Reinforcement learning ; Generative grammar ; Training (meteorology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhenwen Sheng | Huijuan Zhu | Bo Shao | Yu He | Zhuang Liu | Suqin Wang | Ming Sheng
Source: Inorganics (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Energy Materials via Graph Neural Network Graph neural networks (GNNs) have rapidly matured into a unifying, end-to-end framework for energy-materials discovery. By operating directly on atomistic graphs, modern angle-aware and equivariant architectures achieve formation-energy errors near 10 meV atom−1, sub-0.1 V voltage predictions, and quantum-level force fidelity—enabling nanosecond molecular dynamics at classical cost. In this review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, including multi-GPU training, calibrated ensembles, and multimodal fusion with large language models, followed by a discussion of a wide range of recent applications of GNNs in the rapid screening of battery electrodes, solid electrolytes, perovskites, thermoelectrics, and heterogeneous catalysts. Artificial neural network ; Range (aeronautics) ; Battery (electricity) ; Graph ; Deep neural networks ; Voltage Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Graph Neural Networks
Authors: Gad Licht | Stuart Licht
Source: Crystals (review, 2025-10-14)
Field: Materials Science — Materials Chemistry

Carbon Nanotube Production Pathways: A Review of Chemical Vapor Deposition and Electrochemical CO2 Conversion, Such as C2CNT Graphene Nano-Carbons (GNCs) have a huge potential, but current production methods limit their exploration and use. Many GNCs will be explored here with a focus on CNTs (Carbon NanoTubes) (which have some of the highest strengths of any known material, conductivity, EMF absorptivity, and many other useful properties. Manufacturing them abundantly, inexpensively, and in eco-friendly ways remains a significant challenge. Two CNT/GNCs production methods are compared and reviewed. Traditional Chemical Vapor Deposition (CVD) production heats organic reactants with metal catalysts to form GNC/CNTs. As of now, the CVD CNT production has been limited by the high-energy costs, costs per weight comparable to precious metals, and a high CO2-footprint. C2CNT is an electrochemical methodology that overcomes the constraints of CVD, while producing high-quality CNT/GNCs. C2CNT is a molten carbonate CO2-electrolysis that makes GNCs. The C2CNT process also selectively produces a wider variety of CNTs (including helical, magnetic, and doped) and GNCs with higher product specificity than CVD by fine-tuning electrolysis parameters. The wide variety of CNTs/GNCs that can be produced by each of these methods is reviewed and discussed. The goal of this perspective is to compare GNC production methods. Electrolysis ; Electrochemistry ; Chemical vapor deposition ; Carbon nanotube ; Production (economics) ; Deposition (geology) ; Process (computing) Carbon Nanotubes in Composites ; Advancements in Battery Materials ; Graphene research and applications
Authors: Xintong Zhao | Jair Fernando Fajardo Rojas | Jacob Furst | Katherine Ardila | Kyle Langlois | Yuan An | Xiaohua Hu | Fernando J. Uribe‐Romo | Diego A. Gómez‐Gualdrón | Jane Greenberg
Source: ChemRxiv (article, 2025-08-19)
Field: Chemistry — Inorganic Chemistry

Expert-Guided LLM Approach for Sequence-Aware Extraction of MOF Synthesis Metal-organic frameworks (MOFs) offer vast potential for numerous applications but their deceptively simple synthesis remains poorly understood. This fact hinders translating proposed MOF designs from computational high-throughput screening methods into successful synthesis, and slows down MOF development in general. In addition, much of the practical chemical intuition on MOF synthesis remains embedded in natural language across thousands of MOF reports. These limitations call for research exploring data-driven understanding of MOF synthesis and MOF synthesis automation. This paper reports on novel research developing an MOF expert-guided framework that leverages large language models (LLMs) to extract and codify synthesis procedures of metal-organic frameworks (MOFs) in a sequence-aware manner from experimental literature. Specifically, we developed an end-to-end pipeline that combines literature matching, synthesis paragraph classification, and prompt-based entity and relation extraction using GPT-4. Guided by MOF experts, we designed a comprehensive and FAIR-compliant synthesis codification schema that captures synthesis actions, precursors, conditions, and their interrelations as a sequence-aware directed graph. Our model achieves high accuracy in synthesis paragraph classification (F1 score: 0.93) and in entity and relation extraction (F1 score: 0.96 and 0.94, respectively). This work enables large-scale, structured synthesis data collection and paves the way for AI-assisted synthesis prediction and knowledge discovery in materials science. Extraction (chemistry) ; Sequence (biology) ; Computer science ; Chemistry ; Chromatography Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Akhil Sharma | Sonu Sharma | Mohit Sharma | Vikas Sharma | Shruti Sharma | Iyyakkannu Sivanesan
Source: Polymers (review, 2025-10-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polymeric Frontiers in Next-Generation Energy Storage: Bridging Molecular Design, Multifunctionality, and Device Applications Across Batteries, Supercapacitors, Solid-State Systems, and Beyond Polymer materials have become promising candidates for next-generation energy storage, with structural tunability, multifunctionality, and compatibility with a variety of device platforms. They have a molecular design capable of customizing ion and electron transport routes, integrating redox-active species, and enhancing interfacial stability, surpassing the drawbacks of traditional inorganic systems. New developments have been made in multifunctional polymers that have the ability to combine conductivity, mechanical properties, thermal stability, and self-healing into a single scaffold system, which is useful in battery, supercapacitor, and solid-state applications. By incorporating polymers with carbon nanostructures, ceramics, or two-dimensional materials, hybrid polymer nanocomposites improve electrochemical performance, durability, and mechanical compliance, and the solid polymer electrolytes, as well as artificial solid electrolyte interphases, resolve dendrite growth and safety issues. The multifunctionality also extends to flexibility, stretchability, and miniaturization, which implies that polymers are suitable for use in wearable devices and biomedical devices. At the same time, sustainable polymer innovation focuses on bio-based feedstocks, which can be recycled, and green synthesis pathways. Polymer discovery using artificial intelligence and machine learning is faster than standard methods, predicts structure-property-performance relationships, and can be rationally engineered. Although there are difficulties in stability during long periods, scalability, and trade-offs between indeedness and mechanical endurance, polymers are a promising avenue with regard to dependable, safe, and sustainable power storage. This review presents the molecular strategies, multifunctional uses, and prospects, where polymers are at the center of the next-generation energy technologies. Polymer ; Polymer electrolytes ; Bridging (networking) ; Polymer nanocomposite ; Nanocomposite ; Smart polymer ; Supercapacitor ; Energy storage ; Electronics Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Dielectric materials and actuators
Authors: Ruzhao Chen | Yang Liu | Zhe Chen | Yinwu Li | Fei Yang | Jiaxin Lin | Zhuofeng Ke
Source: Communications Chemistry (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Large language model guided automated reaction pathway exploration Fast and efficient automated exploration of reaction pathways is essential for studying reaction mechanisms and advancing data-driven approaches for reaction development and catalyst design. Here, we present a new program (utilizing Python and Fortran), capable of conducting automated, fast, and efficient exploration of reaction pathways for potential energy surfaces (PES) studies. This program integrates quantum mechanics and rule-based methodologies, underpinned by a Large Language Model-assisted chemical logic. Both active-learning methods in transition states sampling and parallel multi-step reaction searches with efficient filtering help enhance efficiency and accelerate PES searching. Its effectiveness and versatility in automating searches are exemplified through case studies of multi-step reactions, including the organic cycloaddition reaction, asymmetric Mannich-type reaction, and organometallic Pt-catalyzed reaction. ARplorer's capability to scale up for high-throughput screening significantly enhances its utility, positioning it as an efficient tool for data-driven reaction development and catalyst design. Computer science ; Programming language ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Abhishu Chand | Kyoungtae Kim
Source: Journal of Nanotheranostics (article, 2025-05-06)
Field: Chemistry — Inorganic Chemistry

Current Advances in the Cancer Therapeutic Applications of Metal–Organic Frameworks Associated with Quantum Dots Metal–organic frameworks (MOFs) along with quantum dots (QDs) are independent structures that have garnered attention in the biomedical field due to their unique chemo-physical characteristics. MOFs are highly porous and tunable structures, while QDs are nanomaterials with excellent optical and fluorescent properties which make these potent diagnostic tools for sensing, detection, and therapeutics. Despite their potential, both materials have their shortcomings in terms of long-term stability and toxicity. However, the integration of these two materials to form QD–MOF hybrid systems has emerged to combine their strengths and overcome their limitations, introducing new possibilities for advanced therapeutic applications. In this mini review, we explore the evolution of the QD–MOF hybrid systems, focusing on their functional properties and applications in sensing, drug delivery and cancer therapy. Furthermore, we discuss the current implementation of this system and its future possibilities, exhibiting the novel impacts of the QD–MOF hybrids in biomedical research and clinical applications. Quantum dot ; Nanotechnology ; Cancer ; Metal-organic framework ; Medicine ; Materials science ; Computer science ; Chemistry ; Internal medicine Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Advanced Nanomaterials in Catalysis
Authors: Boyu Xu | Gaoyang Li | Bohan Wang | Jiawei Bian | Hui Pan | Yulin Min | Guodong Qi | Jun Xu | Feng Deng | Feng Ju | Hao Ling | Zhendong Wang
Source: Advanced Science (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Knowledge Graph for Methane Selective Conversion: Revisiting and Predicting Product Selectivity and Methane Conversion Selective conversion of methane to carbon-based compounds is promising but currently limited by issues related to scale. Unraveling the interconnected relationships between products selectivity and methane conversion plays a pivotal role in understanding of this complex process. In this work, a knowledge graph (KG) is constructed for methane conversion with the help of a robust large language model basing on the literature reported methane conversion over different catalysts under various conditions. This KG, structured around 11 entity types and 32 relationship types, allows to effectively analyze advancements in methane conversion - including identifying optimal catalytic processes, evaluating reaction conditions, and tracking development trends. A deep neural network analysis of the KG highlighted catalysts with metal active sites and multifunctional supports as particularly effective for methanol production under conditions suitable for industrial-scale applications. These findings provide valuable insights for targeted catalyst development and industrial applications. Methane ; Catalysis ; Selectivity ; Methanol ; Knowledge graph ; Graph Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Konstantin S. Jakob | Karsten Reuter | Johannes T. Margraf
Source: Advanced Intelligent Discovery (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) have revolutionized the field of atomistic materials simulation, both due to their remarkable accuracy and their computational efficiency compared to established ab initio methods. Very recently, several general purpose MLIPs have been reported, which are broadly applicable across the periodic table. These represent a fascinating opportunity for materials discovery, provided that they are robust and transferable. In order to stress test current general purpose MLIPs, we evaluate the performance of M3GNet and MACE models in element‐substitution based structure prediction workflows for a diverse range of inorganic, crystalline materials. Importantly, these results are compared with a full density functional based workflow, shifting the focus from merely evaluating single‐point energy and force predictions of MLIPs toward an end‐to‐end perspective. We find that general purpose MLIPs are in general well‐suited to accelerate computational materials discovery and structure prediction, but also display certain systematic biases. To address these, a simple metric to quantify MLIP reliability for materials discovery is introduced. As a by‐product, we also predict novel ground state structures for 15 out of 100 analyzed compositions. Stress (linguistics) ; Stress testing (software) ; Computer science ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ying Zhang | Fang Zheng | Mingwei Wang | Chaolu Niu | Zhe Liu | Qichang Liu | Wenxu Tang | Jiameng Xu | Qiulin Chen | Potao Sun
Source: Journal of Applied Polymer Science (article, 2025-04-02)
Field: Materials Science — Surfaces, Coatings and Films

Magnetic‐Targeted and Light‐Triggered Self‐Healing Microcapsules for Targeted Restoration of Damage‐Prone Insulating Material Zones ABSTRACT Recent advances in microencapsulation technology have emerged as a promising approach for achieving self‐healing of electrical and mechanical damage in epoxy resin. Nevertheless, current implementations face dual challenges: (1) Dependency on external stimuli (elevated temperature, pH variations, or humidity) for microcapsule activation, precluding non‐contact self‐healing mechanisms; (2) Compromised matrix performance through reduced mechanical strength and dielectric integrity. To address these issues, this work innovatively presents a method to fabricate magnetic‐targeted natural photosensitive microcapsules. By incorporating Fe 3 O 4 @SiO 2 magnetic nanoparticles into the shells of photosensitive microcapsules via interfacial polymerization with a core‐shell structure, the microcapsules gain targeting migration ability and UV shielding function. The resulting magnetic‐targeted UV light‐sensitive microcapsules/epoxy composite insulating materials can achieve non‐contact damage repair using natural light. Moreover, the magnetic targeting enables directional migration of the microcapsules to the damage site in the matrix, significantly enhancing their utilization rate. Results show that, at a microcapsule doping concentration of 2 wt%, the tensile properties of the epoxy resin increased by 16.3% compared to pure epoxy resin, with minimal change in insulation strength and dielectric properties. Furthermore, the magnetically targeted natural photosensitive microcapsules can effectively repair surface cracks and electrical tree damage in epoxy resin, restoring the composite material's insulating property to 92.1% of its pre‐damage state after self‐healing. Self-healing ; Materials science ; Composite material ; Self-healing material ; Nanotechnology ; Medicine ; Pathology Surface Modification and Superhydrophobicity ; Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications