jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrés Sebastián Pirolo
Source: Research Square (preprint, 2026-01-07)
Field: Materials Science — Materials Chemistry

100 % Deterministic Spectral Retrosynthesis at Industrial Scale via Topological First Principles Retrosynthetic analysis ; Mode (computer interface) ; Topology (electrical circuits) ; Block cipher mode of operation ; Preprocessor ; Scale (ratio) ; Function (biology) ; Troubleshooting Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Axial and Atropisomeric Chirality Synthesis
Authors: Shanxue Jiang | Wenting Jia | Haishu Sun | Chenyu Zhang | Yushuang Li | Ruiping Liu | Wenwen Gong
Source: LWT (article, 2026-02-17)
Field: Materials Science — Biomaterials

An integrated experimental and deep learning modeling approach to harness plant-derived antimicrobials for biodegradable food packaging films The significant threat of microbial contamination to food safety necessitates the development of innovative antibacterial packaging strategies. To address this challenge, this study developed biodegradable antibacterial films using the solution casting method, incorporating plant extracts into biodegradable polymer matrices such as PLA, PBAT, and PVA. Extracts from 13 plant species were obtained using various solvents and extraction procedures, and their antimicrobial efficiency was assessed through inhibition zone tests. Among them, Syzygium aromaticum , Schisandra chinensis , Punica granatum peel, Scutellaria baicalensis , and Prunus mume exhibited strong antibacterial activity against common foodborne pathogens, including S. aureus , MRSA, L. monocytogenes , and E. coli . A Transformer-based deep learning model identified 175 compounds in Prunus mume and 152 in Scutellaria baicalensis as potential antibacterial agents, using a probability threshold of 0.99. Further analysis revealed that organic acids in Prunus mume and phenolic compounds in Scutellaria baicalensis were the main antimicrobial constituents, indicating distinct antibacterial pathways. The resulting films demonstrated significant food preservation effects on beef samples and desirable biodegradability after six months of soil burial. This study provides an effective strategy that integrates experimental research with artificial intelligence to advance the development of plant-based antibacterial packaging. • Films containing plant extract effectively inhibit bacterial growth on beef surface • A deep learning model identifies key antimicrobial compounds in plant extracts • Plant extracts exert antimicrobial activity via multiple synergistic compounds • Antimicrobial mechanisms vary between plant species based on chemical compositions Antimicrobial ; Scutellaria baicalensis ; Syzygium ; Food packaging ; Antibacterial activity ; Active packaging ; SAGE Nanocomposite Films for Food Packaging ; Pomegranate: compositions and health benefits ; Essential Oils and Antimicrobial Activity
Authors: David Noever
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Atomic Autoresearch: Autonomous Hyperparameter Optimization in a Browser-Based Scalar Autograd Transformer This work ports Karpathy's autoresearch framework to a dependency-free, browser-executable implementation: a single 38 KB HTML file containing a complete GPT transformer, scalar autograd engine, Adam optimizer, evaluation harness, and autonomous experiment controller. The system implements architectural features from the original H100-scale codebase — Rotary Position Embeddings (RoPE), ReluSquared activations, value embeddings with gated mixing, logit soft-capping, and residual skip connections — each as a toggleable feature the search can enable or disable. Evaluation spans seven character-level sequence domains: English names (32K and 200 documents), antimicrobial peptides, SMILES molecular notation, startup company names, dinosaur names, and cocktail names. The autonomous loop discovers domain-dependent architectural preferences: data-rich domains benefit from feature additions, while data-poor domains benefit primarily from learning rate tuning. RoPE emerges as the most broadly beneficial feature (kept in 4 of 7 domains). Improvements range from 0.2% to 41.7% BPB reduction, achieved through up to 19 automated experiments per domain with zero human intervention. The system executes in any modern browser on commodity hardware with no perceptible lag, demonstrating that the autoresearch paradigm remains viable at the absolute minimum scale. Codebase ; Hyperparameter ; Feature (linguistics) ; Python (programming language) ; On the fly ; Domain (mathematical analysis) ; Traverse ; Sine ; Range (aeronautics) ; Feature extraction Machine Learning in Materials Science ; Machine Learning and Data Classification ; Scientific Computing and Data Management
Authors: Chao Shang | Gregory D. Fuchs
Source: arXiv (Cornell University) (preprint, 2026-03-14)
Field: Materials Science — Materials Chemistry

Fast Single Nitrogen-Vacancy Center Ramsey Characterization using a Physics-Informed Neural Network Precise characterization of the local spin environment of single diamond nitrogen-vacancy (NV) centers is crucial for advancing quantum sensing, quantum networking, and the optimization of quantum materials. However, single NV center fluorescence measurements requires long averaging times to obtain clean data that is suitable for conventional model fitting, and that constitutes a key experimental bottleneck for high-throughput characterization. To address this, we introduce \textsc{NVRNet}, a physics-informed simulation-to-reality machine learning pipeline that maps minimal-sweep, noisy Ramsey data to a denoised waveform while directly estimating the hyperfine coupling to proximal ${}^{13}\mathrm{C}$ nuclear spins. The pipeline's denoiser utilizes a two-stage time-frequency U-Net and an attention-augmented time-domain U-Net, pretrained on Hamiltonian-based spin-dynamics simulations with experimentally calibrated noise. To effectively bridge the simulation-to-reality gap, parameter-efficient adapters are attached to the backbone and fine-tuned on targeted experimental data. Across three distinct NV centers, this experimentally fine-tuned model reduces the median reconstruction error on held-out, few-sweep traces to $0.44\text{-}0.67\times$ of the raw experimental noise level. Subsequently, a transformer-based estimator extracts the underlying hyperfine parameters. Forward reconstructions derived from these inferred parameters faithfully reproduce the dominant experimental time- and frequency-domain features, yielding representative normalized fast Fourier transform (FFT) reconstruction errors of $0.10\text{-}0.19$. By reducing both the required data volume and acquisition time, \textsc{NVRNet} enables up to $\sim 40\times$ acceleration of the measurement process, establishing a fast, hardware-compatible pathway for robust hyperfine inference and autonomous qubit characterization. Hyperfine structure ; Spins ; Estimator ; Deep learning ; Diamond ; Characterization (materials science) ; Waveform ; Pipeline (software) Diamond and Carbon-based Materials Research ; Magnetic properties of thin films ; Electronic and Structural Properties of Oxides
Authors: Genmao Zhuang | Amir Barati Farimani
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent Accelerating the discovery of high-performance materials remains a central challenge across energy, electronics, and aerospace technologies, where traditional workflows depend heavily on expert intuition and computationally expensive simulations. Here we introduce the Materials Knowledge Navigation Agent (MKNA), a language-driven system that translates natural-language scientific intent into executable actions for database retrieval, property prediction, structure generation, and stability evaluation. Beyond automating tool invocation, MKNA autonomously extracts quantitative thresholds and chemically meaningful design motifs from literature and database evidence, enabling data-grounded hypothesis formation. Applied to the search for high-Debye-temperature ceramics, the agent identifies a literature-supported screening criterion (Theta_D > 800 K), rediscovers canonical ultra-stiff materials such as diamond, SiC, SiN, and BeO, and proposes thermodynamically stable, previously unreported Be-C-rich compounds that populate the sparsely explored 1500-1700 K regime. These results demonstrate that MKNA not only finds stable candidates but also reconstructs interpretable design heuristics, establishing a generalizable platform for autonomous, language-guided materials exploration. Workflow ; Executable ; Property (philosophy) ; Intuition ; Natural language ; Knowledge base ; Knowledge extraction ; Expert system Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: Paul Rossener Regonia | Christian Mark Pelicano
Source: Advanced Intelligent Discovery (article, 2026-02-22)
Field: Materials Science — Materials Chemistry

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO <sub>2</sub> Conversion Developing high‐performance photocatalysts is essential for sustainable solar fuel production, offering a pathway to mitigate global warming and address pressing environmental challenges. With the rapid growth of data in materials science, machine learning (ML) has emerged as a transformative tool capable of revealing intricate structure–property relationships that are often inaccessible through conventional methods. This capability significantly accelerates the screening and rational design of photocatalysts with enhanced activity and durability. In this review, we provide a critical overview of recent advances in supervised ML applied to photocatalysis, with a particular focus on optimizing photocatalyst properties and designing materials for solar‐driven water splitting and CO 2 reduction. We first introduce the most commonly employed supervised ML techniques relevant to photocatalyst discovery. Key studies are then emphasized to illustrate how ML has been applied to fine‐tune material properties and guide the development of efficient photocatalysts for targeted solar‐to‐chemical conversion. In the final section, we highlight persistent challenges and future directions in leveraging ML to accelerate photocatalyst innovation. By focusing on domain‐specific applications, this review offers a practical framework for researchers aiming to integrate ML into the rational design of photocatalysts. Transformative learning ; Key (lock) ; Focus (optics) ; Sustainable development ; Photocatalysis ; Supervised learning Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Abdulelah S. Alshehri
Source: Applied Sciences (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Fine-Tuning Large Language Models for the Efficient and Concurrent Extraction of Fuel Properties Large datasets of fuel properties are indispensable for predictive combustion modeling and next-generation fuel design. However, resource-intensive experiments restrict existing databases to 200–500 compounds, capturing an infinitesimal fraction of the C1-20 hydrocarbon space. Furthermore, conventional rule-based and supervised learning extraction methods are constrained by poor scalability, domain-specific nomenclature, and weak contextual inference. To address these limitations, we introduce IgnitionGPT, a large language model fine-tuned on GPT-4.1-mini for the automated, concurrent extraction of three ignition metrics: Research Octane Number, Motor Octane Number, and Cetane Number. The model was trained on a human-annotated JSONL dataset of 304 sources (263 peer-reviewed articles, 41 patents) encompassing 581 diverse compounds. By evaluating IgnitionGPT directly against its zero-shot foundation, we isolate the impact of domain-specific fine-tuning. The model overcomes baseline overgeneralization (47.8% F1) to achieve saturated extraction accuracy on unseen data (i.e., 100% for the best model). Remarkably, it reaches this saturation on an 85% held-out test split using a mere 10% of the data for fine-tuning, demonstrating true robustness across heterogeneous literature. Ultimately, by open-sourcing our data and methods, this fine-tuning framework transitions chemical information retrieval from fragmented, rule-based heuristics to unified, concurrent extraction towards bridging the gap between experimental limitations and data-driven molecular design and modeling. Heuristics ; Robustness (evolution) ; Unary operation ; Cetane number ; Bridging (networking) ; Experimental data ; Boosting (machine learning) ; Extraction (chemistry) ; Combustion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Combustion Engine Technologies
Authors: Sachin Jaidka | Aayush Gupta | Daksh Shelly | Yashpreet Kaur | Anushka Garg | Seul‑Yi Lee | Soo‑Jin Park
Source: Small (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Structure‐Property‐Application Correlations of Early Transition Metal Chalcogenides: A Dichalcogenide‐Centered Perspective Early transition metal (ETM)-based chalcogenides constitute a diverse family of layered materials with tunable structural, electronic, and chemical properties. While this materials class includes dichalcogenides, sesquichalcogenides, and polychalcogenides, research efforts and technological applications have been predominantly concentrated on layered transition metal dichalcogenides. This review provides a dichalcogenide-centered perspective on early transition metal chalcogenides, linking their crystal chemistry and structural polymorphism to functional performance. This review provides a detailed look at various types of ETM-based chalcogenides, including disulfides, sesquichalcogenides, trichalcogenides, and polychalcogenides, along with their crystal structures and coordination geometries. The review also explains how properties can be modified through doping, intercalation, and strain engineering, and how phase transitions and defects influence their performance. Special attention is given to their use in 2D materials, phase-change memory devices, and energy-related applications. By summarizing key experimental findings and structural features, this review offers insight into how ETM-based chalcogenides can be engineered for better functionality. The combination of their rich chemistry and practical tunability makes them promising materials for next-generation electronic, catalytic, and energy technologies. Finally, key challenges related to scalability, phase control, interfacial engineering, and environmental impact are critically discussed, and future research is outlined to guide the rational development of next-generation dichalcogenide-based technologies. Perspective (graphical) ; Transition metal ; Phase transition ; Transition (genetics) ; Key (lock) ; Characterization (materials science) ; Phase (matter) 2D Materials and Applications ; Crystal Structures and Properties ; Iron-based superconductors research
Authors: Xue FANG | Kan Kan | Yuliang Ma | Shumeng LI | Xiaochen ZHANG | Yang WANG
Source: Materials Science (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

Research Progress on the Application of Fe&lt;sub&gt;3&lt;/sub&gt;O&lt;sub&gt;4&lt;/sub&gt;/PI Magnetic Nanoparticles in Catalysts The rapid development of the chemical industry has produced a large amount of industrial wastewater, posing a huge threat to the ecological environment and human health. Efficient treatment of sewage is an urgent problem to be solved at present. The combination of efficient nanoparticle catalysts and economical wastewater treatment technologies is an important method to solve water pollution problems. However, nanoparticles have the disadvantages of easy agglomeration, poor stability and difficulty in recovery, which reduces their catalytic performance. Fe3O4 magnetic catalyst, because of its structural characteristics of polyimide composite material and its magnetic field reaction characteristics of inorganic nanoparticles, makes the nanocomposite able to disperse and treat rapidly under the influence of an external magnetic field and can be reused. This paper provides a review of the preparation methods and their applications of Fe3O4 magnetic catalysts in recent years, and analyzes and prospects the application and existing problems of future research development. Catalysis ; Nanocomposite ; Magnetic nanoparticles ; Environmental pollution ; Nanoparticle ; Industrial wastewater treatment ; Composite number Nanomaterials for catalytic reactions ; Magnesium Oxide Properties and Applications ; Environmental remediation with nanomaterials
Authors: Khushbu Sharma | Yogita Dahiya | Takayuki Ichikawa
Source: Integrated nano. (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Recent advances in nanomaterials: Synthesis, properties, and applications Nanomaterials, defined by at least one dimension below 100 nanometres, have emerged as a transformative class of materials due to their unique size-dependent properties. From medicine and energy to electronics and environmental remediation, nanomaterials are enabling breakthroughs across disciplines. This review highlights recent developments in nanomaterial synthesis, examines their distinctive properties, and explores diverse applications. Challenges related to toxicity, scalability, and regulatory frameworks are also discussed, with a forward-looking view on future directions. Transformative learning ; Class (philosophy) ; Dimension (graph theory) ; Energy (signal processing) ; Electronics Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions ; Carbon and Quantum Dots Applications
Authors: Pankaj Kumar
Source: International Journal Of Trendy Research In Engineering And Technology (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

NANOPARTICLES: CLASSIFICATION, SYNTHESIS, CHARACTERIZATION, AND MULTIFUNCTIONAL APPLICATIONS Nanoparticles (NPs) typically have dimensions of less than 100 nm. The potential applications of nanotechnology in a variety of fields, such as catalysis, gas sensing, renewable energy, electronics, medicine, diagnostics, medication delivery, cosmetics, the construction industry, and the food industry. The main factors influencing the characteristics of NPs are their shapes and sizes. The special properties of NPs could be investigated for applications in energy (oil recovery), electronics (transistors, LEDs, reusable catalysts), medical (imaging, tumour detection, drug administration), and more. The creation of NPs with the proper size, structure, monodispersion and morphology is essential for these applications. Nanotechnology has produced new methods that can consistently produce NPs and nanomaterials while being safe for the environment. The goal of this study is to demonstrate both top-down and bottom-up approaches for producing nanomaterials, as well as various characterization techniques, characteristics of NPs, and industry-specific uses of nanotechnology. Characterization (materials science) ; Nanomaterials ; Applications of nanotechnology ; Nanoparticle ; Electronics Chemical and Physical Properties of Materials ; Nanotechnology research and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Mahdi Astaraki | Mohammad Arshi Saloot | Ali Shiraee Kasmaee | Hamidreza Mahyar | Soheila Samiee
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outperform static RAG, particularly in scientific domains with multi-hop reasoning, sparse domain knowledge, and heterogeneous evidence. We provide the first controlled, mechanism-level diagnostic study of whether synchronized iterative retrieval and reasoning can surpass an idealized static upper bound (Gold Context) RAG. We benchmark eleven state-of-the-art LLMs under three regimes: (i) No Context, measuring reliance on parametric memory; (ii) Gold Context, where all oracle evidence is supplied at once; and (iii) Iterative RAG, a training-free controller that alternates retrieval, hypothesis refinement, and evidence-aware stopping. Using the chemistry-focused ChemKGMultiHopQA dataset, we isolate questions requiring genuine retrieval and analyze behavior with diagnostics spanning retrieval coverage gaps, anchor-carry drop, query quality, composition fidelity, and control calibration. Across models, Iterative RAG consistently outperforms Gold Context, with gains up to 25.6 percentage points, especially for non-reasoning fine-tuned models. Staged retrieval reduces late-hop failures, mitigates context overload, and enables dynamic correction of early hypothesis drift, but remaining failure modes include incomplete hop coverage, distractor latch trajectories, early stopping miscalibration, and high composition failure rates even with perfect retrieval. Overall, staged retrieval is often more influential than the mere presence of ideal evidence; we provide practical guidance for deploying and diagnosing RAG systems in specialized scientific settings and a foundation for more reliable, controllable iterative retrieval-reasoning frameworks. Ideal (ethics) ; Context (archaeology) ; Oracle ; Iterative method ; Benchmark (surveying) ; Parametric statistics ; Iterative refinement ; Question answering Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks