jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shujie Yang | Xiaopeng Zhao | Yuqi Zhang | Yansong Tang | Kaichen Dong
Source: ArXiv.org (preprint, 2025-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

CoSP: Reconfigurable Multi-State Metamaterial Inverse Design via Contrastive Pretrained Large Language Model Metamaterials, known for their ability to manipulate light at subwavelength scales, face significant design challenges due to their complex and sophisticated structures. Consequently, deep learning has emerged as a powerful tool to streamline their design process. Reconfigurable multi-state metamaterials (RMMs) with adjustable parameters can switch their optical characteristics between different states upon external stimulation, leading to numerous applications. However, existing deep learning-based inverse design methods fall short in considering reconfigurability with multi-state switching. To address this challenge, we propose CoSP, an intelligent inverse design method based on contrastive pretrained large language model (LLM). By performing contrastive pretraining on multi-state spectrum, a well-trained spectrum encoder capable of understanding the spectrum is obtained, and it subsequently interacts with a pretrained LLM. This approach allows the model to preserve its linguistic capabilities while also comprehending Maxwell's Equations, enabling it to describe material structures with target optical properties in natural language. Our experiments demonstrate that CoSP can design corresponding thin-film metamaterial structures for arbitrary multi-state, multi-band optical responses, showing great potentials in the intelligent design of RMMs for versatile applications. Reconfigurability ; Metamaterial ; Inverse ; Encoder ; Inverse problem ; Design strategy ; Flexibility (engineering) ; Natural language Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Wireless Communication Technologies
Authors: Seoyoung Park | Minsuk Koo | Sungjun Kim
Source: Journal of Physics D Applied Physics (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Advancing phase-change random access memory: materials innovation neuromorphic applications, and scalability challenges Abstract Phase-change random access memory (PRAM) is an emerging high-speed, non-volatile memory technology that leverages the reversible phase transitions of chalcogenide materials such as Ge–Sb–Te to enable efficient data storage and retrieval. This paper provides a comprehensive analysis of PRAM’s core operational mechanisms, including rapid switching speeds, high endurance, and multilevel storage enabled through precise control of phase transitions. Recent advancements in materials engineering, such as nitrogen and carbon doping, superlattice-like structures, and thermoelectric effect integration, are explored for their contributions to improving thermal stability, endurance, and energy efficiency. Beyond traditional memory applications, PRAM’s unique ability to achieve tunable resistance states makes it a promising candidate for neuromorphic computing. The paper discusses PRAM’s role in emulating biological synaptic functions, such as synaptic weight modulation, spike-timing-dependent plasticity, and long-term potentiation/depression, enabling efficient learning and inference in artificial neural networks. The integration of PRAM into crossbar arrays and multi-memristive synapses is examined to develop scalable and energy-efficient neural network architectures for artificial intelligence applications. Key challenges, including sneak current phenomena, endurance degradation over repeated switching cycles, and power consumption in large-scale PRAM arrays, are critically analyzed. Potential solutions, such as selector devices, voltage scaling techniques, advanced thermal management strategies, and processing-in-memory architectures, are evaluated to address these issues. These advancements position PRAM as a pivotal technology for next-generation memory and neuromorphic computing, offering scalable, energy-efficient solutions for future high-performance and artificial intelligence-driven applications. Neuromorphic engineering ; Scalability ; Synaptic weight ; Artificial neural network ; Crossbar switch ; Resistive random-access memory ; Memristor ; Phase-change memory ; Random access Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Sourav Singh Sehgal | Rahul Shrivastava | Tanu Bansal | Megha Bansal
Source:  (article, 2025-11-28)
Field: Chemistry — Organic Chemistry

MolBioART: Molecular Biological ART for Drug Discovery This work presents a hybrid framework combining Adaptive Resonance Theory (ART) clustering with deep neural network–based molecular restoration for efficient, target-specific drug optimization. Drug–protein pairs are first encoded into vector representations and clustered using ART to form interpretable prototype classes. For a new protein target, the nearest ART prototype serves as an initial candidate. A Protein-Guided Drug Restoration (PGDR) module then refines these candidates by learning to reconstruct optimal drug vectors from perturbed, protein-conditioned inputs. The restored molecules are further optimized in latent space under efficacy and pharmacological constraints. Experiments on benchmark DTI datasets show that the framework enhances interpretability and candidate quality, providing a scalable and biologically grounded approach to drug repurposing and de novo design. Drug discovery ; Drug ; Drug industry ; Key (lock) Click Chemistry and Applications ; Advanced Fluorescence Microscopy Techniques ; Cell Image Analysis Techniques
Authors: Thouraya Hamdi | Faouzi Khedher | Mohamed Jmali | Boubaker Jaouachi
Source: Autex Research Journal (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Calculation and prediction of thread consumption in technical textile products using the neural network method Abstract This paper describes a method developed to improve the accuracy of thread consumption predictions for technical textile industrialists, particularly for products such as the automotive leatherette cover, by applying a neural network method. The approach relies on analysing a database regarding different fabrics, garment designs, and relevant production parameters to develop a suitable prediction model. The developed model using advanced supervised learning techniques and statistical methodologies, including multiple linear regression, captures the intricate relationships between many parameters to predict thread consumption accurately. The results show that the model without interaction achieves an R 2 of 0.76, while the model with linear regression including interactions achieves an R 2 of 0.91. In contrast, the use of neural networks achieves an R 2 of 0.94. This framework is advantageous for manufacturers, as it allows them to optimize the organization of their production schedules and other operations. By integrating predictive analytics, manufacturers can source and manage raw materials more accurately, avoiding excess or insufficient production output, while improving productivity. Meanwhile, reducing the waste of materials helps in cost-reduction strategies and improves the financial efficacy of the business. This practice becomes sustainable by addressing the environmental concerns due to overproduction, one of the modern issues. In addition, accurately predicting thread consumption makes it easier to control the stock level of inventories, which is important for effective supply chain management. With optimized inventory, companies will save on storage expenses and minimize the downtime between operations, resulting in increased productivity. The combined application of neural nets and statistical techniques increases the accuracy of forecasts, which is essential to manufacturers in a highly competitive environment. Artificial neural network ; Thread (computing) ; Downtime ; Automotive industry ; Statistical model ; Supply chain ; Linear regression ; Production (economics) Textile materials and evaluations ; Forecasting Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Masayoshi Matsubara | Sasaki Ryo | J Takahara | Shinji Moritake | Yasuyuki Harada | Hiromasa Kaneko
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Development of a Model for Predicting the Thermophysical Properties of Carbon Materials and Proposal of Manufacturing Conditions Using the Model Carbon fibers ; Materials science ; Thermodynamics ; Composite material ; Physics Thermal and Kinetic Analysis ; Material Properties and Applications ; Engineering Diagnostics and Reliability
Authors: Najla Salim ALShamsi
Source: International Journal of Science and Research (IJSR) (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

A Review of the Correlation Between SF6 Decomposition Products and Partial Discharge in Gas-Insulated Switchgear Gas-insulated switchgear (GIS) relies on sulfur hexafluoride (SF<sub>6</sub>) due to its superior dielectric and arc-quenching characteristics. However, partial discharge (PD) activity within GIS leads to SF<sub>6</sub> decomposition, forming gaseous by-products that serve as diagnostic indicators. This technical review synthesizes literature review linking specific PD types-such as corona, voids, moving particle, and floating electrodes-with corresponding SF<sub>6</sub> breakdown products like SO<sub>2</sub>, SOF<sub>2</sub>, SO<sub>2</sub>F<sub>2</sub>, and CF<sub>4</sub>. Emphasis is placed on correlating these chemical signatures with defect types to enable early fault detection and condition-based maintenance. The paper also identifies limitations in current diagnostic frameworks and proposes future research incorporating multi-parameter analysis and machine learning models. Switchgear ; Partial discharge ; Sulfur hexafluoride ; Decomposition ; Fault (geology) ; Current (fluid) ; Dielectric gas High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Yifan Meng | Dapeng Yan | Xuquan Wang | Zhiyuan Ma | Yongfu Li | Shanwen Xu | Shan Qiu | Lili Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Real-Time Monitoring Of Escherichia Coli Fermentation Via Hyperspectral Imaging and Lightweight Edge Artificial Intelligence Hyperspectral imaging ; Enhanced Data Rates for GSM Evolution ; Escherichia coli ; Artificial intelligence ; Computer science ; Remote sensing ; Environmental science ; Computer vision ; Chemistry ; Geology ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Кришнан Каннабиран | G. Jagadeesh Kumar | N. Venkatesh | Krishna Chaitanya | P. Manikanta | K. V. Gangadharan
Source: Annual Research & Review in Biology (article, 2025-10-07)
Field: Chemistry — Analytical Chemistry

Smart Detection System for Pesticide Contamination in Fruits and Vegetables Pesticide contamination in fruits and vegetables has become a serious concern due to its detrimental effects on human health and the environment. Currently, the widespread utilization of pesticides such as Glyphosate, Chlorpyrifos, Neonicotinoids, Mancozeb, and Pyrethroids during fruit and vegetable cultivation has been associated with adverse health effects in humans. To grow more food for more people, people developed innovative techniques. These days, fruits and vegetables are essential for providing us with the nutrition and energy we require. But occasionally, chemicals are applied to aid in their growth. The goal of the present study is to use an Arduino Mega 2560 microprocessor, which is integrated with an LCD display, spectral triad sensor, pH sensor, gas sensor, and buzzer, to identify pesticides in fruits and vegetables. The system uses real-time sensor data collection and Random Forest analysis powered by machine learning (ML) and the Internet of Things (IoT). The spectral triad sensor captures comprehensive spectral data while the gas and pH sensors monitor the presence of pesticides and acidity. Using the Random Forest method, the machine learning model examines the sensor data to identify potential pesticide contamination. The results are displayed on LCD in addition to a buzzer alert. Pesticide ; Arduino ; Buzzer ; Contamination ; Pesticide residue ; Internet of Things Spectroscopy and Chemometric Analyses
Authors: Siron, Martin | Djafar, Inel | Ramlaoui, Ali | Fayette, Etienne du | Rossello, Amandine | Fako, Edvin | McDermott, Matthew | Therrien, Felix | Barroso-Luque, Luis | Cipcigan, Flaviu | Schwaller, Philippe | Wolf, Thomas | Duval, Alexandre
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability. Robust standardization and de-duplication methods are needed to address these issues and streamline materials research. We present LeMat-Bulk, a unified dataset combining Materials Project, OQMD, and Alexandria, encompassing over 5.3 million PBE-calculated materials and also representing the largest collection of PBESol and SCAN functional calculations. Our methodology standardizes calculations across databases that utilize different parameters, effectively addressing redundancy and enhancing cross-compatibility. To de-duplicate, we propose a hashing function which we termed the Bonding Algorithm Weisfeiller-Lehman (BAWL). We comprehensively benchmark this fingerprint under atomic noise, lattice strain, and symmetry transformations, demonstrating that it outperforms existing fingerprinting techniques such as SLICES, and CLOUD in robustness while offering greater computational efficiency than similarity-based approaches such as Pymatgen's StructureMatcher. Additionally, the fingerprint facilitates the analysis of functional-dependent trends (PBE, PBESol, SCAN) offering a scalable framework for data-driven materials science. Scalability ; Robustness (evolution) ; Cloud computing ; Redundancy (engineering) ; Standardization ; Hash function ; Fingerprint (computing) Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Materials Characterization Techniques
Authors: Rupam Bhaduri | S Manasa | S. Sakthivel | Mani Karthik
Source: Journal of Materials Science Materials in Electronics (article, 2025-11-27)
Field: Chemistry — Inorganic Chemistry

Hybrid ML model of quantum-embedded Bi-LSTM classifier for the prediction of stable spinel oxides Spinel ; Cluster analysis ; Mean squared error ; Embedding ; Generative grammar ; Classifier (UML) ; Feature (linguistics) ; k-means clustering Pigment Synthesis and Properties ; Magnetic Properties and Synthesis of Ferrites ; Heusler alloys: electronic and magnetic properties
Authors: Lingyu Wang | Li’an Zhu | Wenjian Guo | Zhen Wang | Bin Yang | Shuxin Bai | Yicong Ye
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Accelerated Hardness Optimization of Hfb₂-Sic-X Ultra-High Temperature Ceramics Via Generative Adversarial Network - Enhanced Active Learning Ceramic ; Generative adversarial network ; Materials science ; Generative grammar ; Adversarial system ; Active learning (machine learning) ; Composite material ; Computer science ; Artificial intelligence ; Deep learning Advanced ceramic materials synthesis ; Advanced materials and composites ; Advanced machining processes and optimization
Authors: Adel Ali Abulifa | Fatma Ahmed Enbais | Dunia M. Shwehdy
Source: International Journal of Electrical Engineering and Sustainability (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Nuclear Waste Management: Opportunities, Challenges, and Future Prospects This study explores the transformative potential of artificial intelligence (AI) in revolutionizing nuclear waste management (NWM) by optimizing key processes, including waste classification, treatment, storage, and disposal. Leveraging advanced machine learning algorithms and data analytics, AI significantly enhances the precision and efficiency of waste categorization, facilitating more informed and systematic decision-making. Furthermore, AI-driven optimization techniques refine treatment methodologies, mitigate operational risks, and ensure stringent compliance with regulatory frameworks, thereby contributing to the safer and more sustainable handling of radioactive materials. These advancements not only enhance overall operational efficiency but also strengthen predictive modeling capabilities, enabling more accurate risk assessments and strategic planning. The integration of AI into NWM empowers stakeholders to navigate complex regulatory landscapes more effectively while minimizing ecological impacts and reinforcing public safety. This study identifies key research and development priorities for advancing AI-augmented NWM, including the refinement of AI algorithms for real-time monitoring, predictive analytics, and early anomaly detection, facilitating a proactive approach to risk mitigation. Additionally, emerging technologies such as robotic automation and autonomous systems present unprecedented opportunities to reduce human exposure to hazardous environments by streamlining waste handling operations. The continuous evolution of AI underscores its transformative potential in addressing the critical challenges associated with NWM, ensuring the secure, responsible, and long-term stewardship of radioactive materials for future generations. Transformative learning ; SAFER ; Automation ; Hazardous waste ; Key (lock) ; Radioactive waste ; Sustainability ; Stewardship (theology) ; Sustainable development Graphite, nuclear technology, radiation studies ; Nuclear materials and radiation effects ; Innovation, Sustainability, Human-Machine Systems
Authors: Siron, Martin | Djafar, Inel | Ramlaoui, Ali | Fayette, Etienne du | Rossello, Amandine | Fako, Edvin | McDermott, Matthew | Therrien, Felix | Barroso-Luque, Luis | Cipcigan, Flaviu | Schwaller, Philippe | Wolf, Thomas | Duval, Alexandre
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability. Robust standardization and de-duplication methods are needed to address these issues and streamline materials research. We present LeMat-Bulk, a unified dataset combining Materials Project, OQMD, and Alexandria, encompassing over 5.3 million PBE-calculated materials and also representing the largest collection of PBESol and SCAN functional calculations. Our methodology standardizes calculations across databases that utilize different parameters, effectively addressing redundancy and enhancing cross-compatibility. To de-duplicate, we propose a hashing function which we termed the Bonding Algorithm Weisfeiller-Lehman (BAWL). We comprehensively benchmark this fingerprint under atomic noise, lattice strain, and symmetry transformations, demonstrating that it outperforms existing fingerprinting techniques such as SLICES, and CLOUD in robustness while offering greater computational efficiency than similarity-based approaches such as Pymatgen's StructureMatcher. Additionally, the fingerprint facilitates the analysis of functional-dependent trends (PBE, PBESol, SCAN) offering a scalable framework for data-driven materials science. Scalability ; Robustness (evolution) ; Cloud computing ; Redundancy (engineering) ; Standardization ; Hash function ; Fingerprint (computing) Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Materials Characterization Techniques
Authors: Wei Wang | Yao Zhang | Hongyang Zheng | Shuju Fang | Xuewen Tan | Jianjun Wang | Yi Tang
Source: Journal of environmental chemical engineering (article, 2025-08-18)
Field: Chemistry — Inorganic Chemistry

Synergistic random forest-genetic algorithm: A new strategy for preparation of europium-functionalized Zr-MOF fluorescent ratio probe Europium ; Fluorescence ; Genetic algorithm ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Algorithm ; Chemical engineering ; Biological system ; Engineering ; Optoelectronics ; Luminescence ; Biology ; Machine learning ; Physics ; Optics Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Riley, Kevin
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

Connecting Bits-to-Atoms: The Φ(M) Algebraic-Information Framework for Representing Matter as Information This paper introduces Φ(M), a deterministic information-theoretic framework for representing matter as information. By encoding atoms and materials as fixed-length algebraic-information vectors derived from conserved quantities and Shannon-type measures, Φ(M) defines a continuous manifold where distances correspond to information divergence. The resulting geometry reconstructs the periodic law, separates superconducting mechanisms into distinct informational branches, and enables deterministic generation of novel compositions. Unlike stochastic machine-learning approaches, Φ(M) provides a reversible, auditably generative mapping between composition and property, reducing materials discovery to a closed-form information-processing task. The framework also serves as a geometric substrate for AI-driven exploration, converting stochastic search into guided navigation through informational space. A patent application covering aspects of this work has been filed. Manifold (fluid mechanics) ; Encoding (memory) ; Generative grammar ; Stochastic process ; Generative model ; Work (physics) Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuanhao Xie | Zheng Shen | Jing Jin
Source: Sensors and Actuators B Chemical (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Machine learning-driven colorimetric sensing and monitoring of optically-engineered liquid marbles Microreactor ; Microfluidics ; RGB color model ; Colorimetry ; Preprocessor ; Fabrication ; Analyser Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Proteins in Food Systems