jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bilge Taskin | Wenxiong Xie | Teddy Lazebnik
Source: Scientific Reports (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Knowledge integration for physics-informed symbolic regression using pre-trained large language models Symbolic regression (SR) has emerged as a powerful tool for automated scientific discovery, enabling the derivation of governing equations from experimental data. A growing body of work illustrates the promise of integrating domain knowledge into the SR to improve the discovered equation’s generality and usefulness. Physics-informed SR (PiSR) addresses this by incorporating domain knowledge, but current methods often re- quire specialized formulations and manual feature engineering, limiting their adaptability only to domain experts. In this study, we leverage pre-trained Large Language Models (LLMs) to facilitate knowledge integration in PiSR. By harnessing the contextual understanding of LLMs trained on vast scientific literature, we aim to automate the incorporation of domain knowledge, reducing the need for manual intervention and making the process more accessible to a broader range of scientific problems. Namely, the LLM is integrated into the SR’s loss function, adding a term of the LLM’s evaluation of the SR’s produced equation. We extensively evaluate our method using three SR algorithms (DEAP, gplearn, and PySR) and three pre-trained LLMs (Falcon, Mistral, and LLama 2) across three physical dynamics (dropping ball, simple harmonic motion, and electromagnetic wave). The results demonstrate that LLM integration consistently improves the reconstruction of physical dynamics from data, enhancing the robustness of SR models to noise and complexity. We further explore the impact of prompt engineering, finding that more informative prompts significantly improve performance. Generality ; Leverage (statistics) ; Robustness (evolution) ; Domain knowledge ; Adaptability ; Domain (mathematical analysis) ; Process (computing) ; Applicability domain ; Data integration Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: S Kangar Lou | Chengchun Liu | Dongxiao Zhang | Yuntian Chen | Fanyang Mo
Source: Advanced Science (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems Recent advances in artificial intelligence have produced powerful predictive models for chemical analysis, but model interpretability remains a challenge. Here, we introduce Unsupervised Hierarchical Symbolic Regression (UHSR), providing an explainable solution while maintaining competitive predictive performance. With a focus on thin-layer chromatography (TLC), a crucial technique in molecular polarity analysis, UHSR automatically distills chemically-intuitive retention indices and discovers explainable equations that link molecular structures to chromatographic behavior. Experiments have shown UHSR's capability to derive concise and accurate governing equations linking polarity to molecular structures from the TLC dataset. A survey of 100 expert chemists demonstrates that our UHSR model gains more trust from chemists compared to traditional models. Additionally, we also show its adaptability to other property prediction tasks beyond molecular polarity. Interpretability ; Property (philosophy) ; Polarity (international relations) ; Adaptability ; Regression ; Symbolic regression ; Quantitative structure–activity relationship ; Complex system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Nicolas Schmid | Marc Wanner | Giulia Fischetti | Andreas Henrici | Mohsen Meshkian | Simon Bruderer | Rudolf M Füchslin | Bjoern Heitmann | J. D. Wegner | Roland K O Sigel | D. Wilhelm
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra Accurate interpretation of one-dimensional proton nuclear magnetic resonance (1H NMR) spectra remains a rate-limiting step in molecular structure elucidation, particularly when severe signal overlap, strong spin coupling, and instrumental distortions mask key features. Existing automated approaches depend on computationally intensive and sensitive iterative quantum-mechanical fitting and still require expert oversight. Here we introduce MolDeTr, a chemistry-informed deep-learning framework derived from the detection-transformer architecture that unifies peak picking, multiplet identification, and extraction of chemical shifts, scalar coupling constants, relaxation-dependent decay times, and proton counts in a single-network pass. The method targets prototypical spin systems of single-component small molecules in 1D 1H NMR, with up to ten distinct groups of chemically equivalent spins (multiplets). MolDeTr is trained exclusively on synthetic spectra generated by spin-dynamics simulations and augmented with realistic experimental artefacts, enabling it to generalize to unseen compounds—including experimental spectra with crowded regions and strongly coupled multiplets—without reference standards or prior spin-system knowledge. Unlike structure-conditioned shift-prediction or calculation models, e.g., density functional theory (DFT), that assume the molecular structure is known, MolDeTr addresses the spectrum-conditioned inverse problem and extracts spin-system parameters directly from measured 1D 1H NMR spectra, thereby substantially improving chemical-shift prediction precision by one to two orders of magnitude compared to existing structure-conditioned approaches. Benchmarking against challenging experimental 1H NMR spectra of small molecules shows median absolute errors of 0.89 Hz for chemical shifts and 0.20 Hz for coupling constants, while absolute proton counts are predicted with 93.5% accuracy, outperforming state-of-the-art spectrum analysis software and experienced spectroscopists. Owing to its physics-guided, modality-agnostic design, MolDeTr can be extended to other spectroscopic and diffraction techniques. By eliminating iterative fitting and expert intervention, MolDeTr offers a scalable route to fully automated spectral analysis, accelerating molecular discovery across the chemical sciences. Spins ; Spectral line ; Multiplet ; Proton ; Chemical shift ; Spin (aerodynamics) ; Scalar (mathematics) ; NMR spectra database ; Coupling (piping) ; Undersampling Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Gong Zhang | Jie Liu | Zhaoxuan Li | Longqi Meng | Zeyu Zou | Xin Li | Y Chen
Source: ACS Applied Materials & Interfaces (article, 2026-02-16)
Field: Chemistry — Inorganic Chemistry

Research Progress in Machine Learning Techniques for Metal–Organic Framework Screening Metal-organic frameworks (MOFs) are prime candidate materials for gas adsorption and separation owing to their exceptional porosity and structural tunability. However, the nearly infinite chemical space and exponentially growing number of candidate structures pose insurmountable challenges to traditional experimental methods and brute-force computational screening. Data-driven machine learning (ML) offers a transformative solution for efficiently navigating this vast materials library. This review analyzes the current state of ML-based MOF screening, evaluates the limitations of mainstream MOF databases, and highlights how data authenticity and update frequency affect model reliability. The evolution of feature engineering─from manual geometric descriptors to automated representation learning using graph neural networks (GNNs) and molecular fingerprints─is also outlined. Furthermore, we discuss the specific applicability of advanced algorithmic frameworks, including deep learning, active learning, and transformers, to MOF screening tasks. Future development should focus on integrating high-fidelity experimental data with model interpretability to enable closed-loop autonomous discovery systems. Interpretability ; Transformative learning ; Focus (optics) ; Graph ; Feature (linguistics) ; Deep learning ; Artificial neural network ; Representation (politics) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Tianqi Zhang | Hang Xu | Xiaorui Zhang | Ling Weng | X. Wang | Dong Yue | Ming Cheng | Li Kong
Source: Polymers for Advanced Technologies (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Development of High‐Toughness Epoxy Composite Insulating Varnish for Cold‐Region Dry‐Type Reactors ABSTRACT Epoxy insulating varnishes are widely used in motors, transformers, and other electrical equipment because of their excellent electrical properties. However, their inherently rigid molecular structure results in brittleness and insufficient mechanical strength, restricting their applicability under high stress, particularly in cold regions. To prevent insulation cracking in dry‐type air‐core reactors operating in extreme cold regions, this study evaluates the modification of E51 epoxy resin using four agents: Polyaryletherketone (PAEK), epoxy‐terminated butadiene nitrile rubber (ETBN), 4,5‐epoxyhexane‐1,2‐dicarboxylic acid diglycidyl ester (TDE‐85), and dicyclopentadiene (DCPD). Experimental results reveal that TDE‐85 achieves the most significant reinforcement; at an optimal loading of 20 wt%, the flexural strength and modulus reach 151.8 MPa and 4.04 GPa, respectively. PAEK and DCPD exhibit peak flexural strengths of 119 MPa (at 15 wt%) and 122 MPa (at 30 wt%), while ETBN modification leads to a gradual decline in mechanical stiffness. Regarding dielectric properties, all systems maintain a low dissipation factor (tan δ &lt; 0.012). Notably, the TDE‐85 (15 wt%) system exhibits the highest breakdown voltage of 29.15 kV/mm and a high volume resistivity of 7.8 × 10 14 Ω m. Low‐temperature tests at −40°C further demonstrate the superior stability of the TDE‐85 system, which maintains a flexural strength of 133 MPa (a slight increase from 131 MPa at 25°C), whereas the strengths of PAEK, ETBN, and DCPD systems drop significantly to 82 MPa, 66.2 MPa, and 78.2 MPa, respectively. These findings identify TDE‐85 as the optimal candidate for developing high‐toughness, cold‐resistant insulating varnishes for power equipment. Epoxy ; Flexural strength ; Dicyclopentadiene ; Flexural modulus ; Brittleness ; Composite number ; Dielectric strength ; Drop (telecommunication) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Epoxy Resin Curing Processes
Authors: Jinghui Zhang | Aming Xie | Weijin Li | Wei Dong | Ruru Gao
Source: Small (article, 2026-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Optimized Electromagnetic Wave Absorption in Metal/C Nanocomposites Traditional carbon-based electromagnetic wave absorbers suffer from limited tunability due to the intricate coupling of multiple synthesis parameters, hindering the rational design of high-performance materials. Herein, we apply a genetic algorithm (GA) to optimize electromagnetic wave absorption (EWA) performance in Metal/C Nanocomposites. Over three generations of GA evolution, five key synthesis parameters-carbon precursor type, metal type, molar ratio of carbon precursor to metal ions, carbonization temperature, and filler loading ratio (wt.%)-are simultaneously tuned. Progressive optimization enhances the Enhanced Absorption Band (EAB) from an initial average of 1.24 to 4.08 GHz, while the minimal reflection loss (RL<sub>min</sub>) improves from -20.29 to -41.9 dB. The champion sample achieves a remarkable RL<sub>min</sub> of -25.9 dB at 7.04 GHz with an EAB of 7.56 GHz. Random Forest and XGBoost models further quantify parameter importance, consistently identifying carbon precursor type (32.5% and 31.4%) and filler loading ratio (33% and 38.4%) as the dominant factors-validating the GA-driven optimization pathway. This work demonstrates the potential of evolutionary algorithms in materials design and provides a transferable framework for high-performance EWA materials. Reflection loss ; Absorption (acoustics) ; Carbon fibers ; Carbonization ; Coupling (piping) ; Electromagnetic radiation ; Nanocomposite ; Filler (materials) ; Metal Electromagnetic wave absorption materials ; Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials
Authors: Yuzi Han | Wutong Du | Yonglin Zhang | Cheng Qiu | M. Law | Ying Zhao | Bo Li | Yang Wang | Jinglei Yang
Source: Advanced Materials (article, 2026-01-18)
Field: Chemistry — Organic Chemistry

Programming Interfacial Polymerization: Machine Learning Unveils Quantitative Rational Design Rules for Microcapsules and Beyond Interfacial polymerization (IP) serves as a versatile platform technology for designing polymeric membranes, yet its extension to applications such as microencapsulation (MIP) remains hindered by empirical methodologies, largely due to the absence of quantitative rational design principles. Unlike separation membranes, which prioritize nanostructural control, MIP emphasizes encapsulation efficiency (EE%), rendering conventional membrane-derived theories and thermodynamic descriptors insufficient. In this work, we transcend these limitations by employing interpretable machine learning to program interfacial polymerization, thereby deciphering mechanism-informed quantitative design rules. Our data-driven platform integrates molecular thermodynamics, polymerization kinetics, and emulsion-stabilized interfacial parameters to identify previously overlooked descriptors governing microcapsule formation. We establish a predictive chemical-process-structure-performance relationship and demonstrate programmable control over key performances, including EE% (30%-95%), particle size (100-400 µm), and shell thickness-to-radius ratios (0.005-1) for diverse payloads spanning hydrophobic, hydrophilic, and highly reactive compounds such as toluene diisocyanate and amines. This work not only resolves long-standing challenges in understanding complex multiphase interactions in MIP but also establishes a new paradigm for the quantitative design of polymeric microcapsules, with broad implications for functional particles, catalytic microreactors, digital cells, and membranes. Rational design ; Polymerization ; Rendering (computer graphics) ; Polymer ; Design elements and principles Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Zhaoxiang Shen | Raúl I. Sosa | Jakub Lengiewicz | Alexandre Tkatchenko | Stéphane Bordas
Source: Machine Learning Science and Technology (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

Machine learning surrogate models of many-body dispersion interactions in polymer melts Abstract Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations. van der Waals force ; Surrogate model ; Generalization ; Dispersion (optics) ; Key (lock) ; Computational model ; Cutoff ; Polymer Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Muhammad Luqman HASHMI | Fabio Scaffidi Muta | Gianluca Deninno | C. Parmar | R. Verma | F. Mazaleyrat | S. N. Kane | Salil Modak | V. Raghavendar Reddy | P. Tiberto | M. Coisson
Source: Journal of Magnetism and Magnetic Materials (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Engineering MgFe2O4 nanoparticles to enhance magnetic, optical, and dielectric performance This research investigated the effects of solution pH during synthesis and annealing on the structural, magnetic, optical, and dielectric properties of MgFe 2 O 4 synthesised using the citric acid-assisted sol-gel auto-combustion method. Structural data showed that crystallinity, lattice parameters, and cation distribution were strongly influenced by both pH and annealing temperature. Optimal morphology and crystallinity were achieved at pH 7 and an annealing temperature of 550 °C for 3 h, under which cation ordering and defect reduction were effectively achieved. Magnetic characterisation revealed a significant improvement in saturation magnetisation (20.6 emu/g) under optimal conditions, attributed to increased super-exchange interactions. The optical bandgap was modulated by more than 11% (2.04 to 2.30 eV) by adjusting pH-mediated surface chemistry and subsequent defect states. The sample synthesised at pH 9 exhibited the highest values of ε’ , ε” , and σ ac . These results conclusively demonstrate that accurate control of synthesis pH and annealing enables the design of multifunctional properties in MgFe 2 O 4 nanoparticles, providing a solid foundation for their application in high-end magnetic, optical, and electronic devices. • Designing multifunctional properties in MgFe 2 O 4 nanoparticles. • Rational modification of the synthesis conditions of MgFe 2 O 4 nanoparticles. • Optimum properties at pH 7 and annealing temperatures of 550 °C for 3 h. • Saturation magnetization of the MgFe 2 O 4 is 20.6 emu/g at optimum conditions. • Optical bandgap and the dielectric properties can be systematically varied. Annealing (glass) ; Crystallinity ; Dielectric ; Band gap ; Magnetization ; Nanoparticle ; Saturation (graph theory) ; Citric acid Magnetic Properties and Synthesis of Ferrites ; Magnesium Oxide Properties and Applications ; Nanoparticle-Based Drug Delivery
Authors: Shaowei Cui | Site Li | Zi-Xin Huang | Yanzhe Yu | Mingxue Cai | Xiangyin Bao | C. Zhang | T. S. Zhang | Lei Cheng | Wenxuan Zhang | Zheng Lou | S. -G. Wang | Wen Gong | Chao-Feng Wu | Lili Wang | Yu Wang
Source: Nature Communications (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Superelastic Tellurium Thermoelectric Coatings for Advanced Trimodal Microsensing Tactile endoscopes can provide physicians with rich sensory information, enabling fast and accurate medical diagnoses. However, existing tactile endoscopy sensors do not consider temperature perception, which is a very important diagnostic indicator in medicine. Here, we report for the first time a tellurium-based superelastic thermoelectric visual-tactile sensor. This platform achieves a breakthrough by combining tellurium thermocouples designed based on crystal structures with viscoelastic silicone encapsulation, enabling simultaneous microscale visual, thermal, and force measurements in a single device. By employing a morphologically optimized tellurium-polymer heterointerface and advanced deep neural network algorithms, we address the inherent trade-off between transparency and responsiveness, achieving artifact-free imaging, real-time thermal mapping, and microstructure force feedback. We conduct clinical endoscopic palpation experiments on live rabbits and successfully achieve tactile diagnosis of inflamed tissue including temperature distribution, especially in cases where visual distinction is difficult, pointing out possible development directions for intelligent endoscopy systems. Microscale chemistry ; Transparency (behavior) ; Palpation ; Thermoelectric effect ; Thermocouple ; Tellurium ; Microstructure ; Silicone ; Optical transparency Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermography and Photoacoustic Techniques
Authors: Fatma Tuba Kıraç Demirel | Çiğdem Akdemir Işık | Mahmut Ekrem Parlak | Halil İbrahim Odabaş | Adnan Fatih Dağdelen | Furkan Türker Saricaoglu
Source: Journal of Food Process Engineering (article, 2026-01-01)
Field: Materials Science — Biomaterials

Ohmic Heating ( <scp>OH</scp> )‐Assisted Extraction of Bovine Hide Gelatin: Functional and Physicochemical Comparison With Halal Commercial Gelatins ABSTRACT Conventional gelatin extraction from high‐collagen tissues requires prolonged thermal processing (8–10 h), resulting in high energy consumption and potential degradation of functional properties. To address this limitation, this study investigated ohmic heating (OH) as an alternative extraction method for bovine hide gelatin. Gelatin was extracted using voltage gradients of 5–20 V/cm for 1–5 h and compared with both conventional extraction parameters and commercial halal gelatins. OH markedly reduced the extraction time to 1–5 h while maintaining a maximum temperature of 70°C, resulting in significantly improved extraction efficiency and higher dry matter and protein contents ( p &lt; 0.05). Although gel strength (122.12–176.81 g) was lower than that of commercial bovine gelatin (242.44 g), all OH‐treated samples except those extracted at 20 V/cm formed stable gels. SDS‐PAGE and compositional analyses indicated that OH induced partial degradation of high‐molecular‐weight fractions, consistent with the observed gel strength trends. The amino acid profiles, dominated by glycine, proline, and hydroxyproline, and the viscoelastic behavior ( G ′ &gt; G ″) were comparable to commercial references. Thermal analyses further revealed sharper melting transitions and higher Δ H values, suggesting improved thermal stability. Microstructural changes in pore size and distribution supported the observed functional differences. Overall, OH‐assisted extraction provides an efficient, energy‐saving, and thermally controlled approach to gelatin production. To our knowledge, this is the first study to demonstrate the applicability of ohmic heating for extracting gelatin from bovine hide, underscoring its potential as a sustainable alternative to conventional processing methods. Gelatin ; Extraction (chemistry) ; Degradation (telecommunications) ; Ohmic contact ; Joule heating ; Thermal Collagen: Extraction and Characterization ; Meat and Animal Product Quality ; Microbial Inactivation Methods