jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junyan Chen
Source: Scientific journal of humanities and social sciences. (article, 2025-12-08)
Field: Chemistry — Organic Chemistry

The Protection of Intellectual Property Rights for the Xinqiao Yanglan Bamboo Weaving Craft in Zhaoqing City Empowered by Artificial Intelligence Artificial intelligence (AI) technology is bringing about a revolutionary paradigm shift in the safeguarding and transmission of intangible cultural heritage (ICH). While it enhances conservation efficiency and expands transmission pathways, it simultaneously poses structural challenges to the intellectual property (IP) system, which is traditionally based on the protection of individual intellectual creations. This paper takes the Xinqiao Yanglan Bamboo Weaving Craft in Gaoyao District, Zhaoqing City, Guangdong Province, as a specific case study. It delves into the frontier legal dilemmas concerning the definition of rights holders, the assessment of copyrightability, the delineation of fair use boundaries, and the determination of infringement liability for digital ICH outcomes in the context of AI empowerment. The research indicates that the inherent tension between public and private interests becomes more complex with AI's involvement. Consequently, this paper proposes clarifying the ownership rules for AI-generated content through legislation or judicial interpretation, constructing a special statutory licensing system tailored for the digital utilization of ICH, enhancing the flexibility of fair use doctrines to accommodate technological advancements, and comprehensively utilizing technologies like blockchain to build a credible system for rights confirmation, utilization, and protection. The aim is to construct an IP legal framework that effectively incentivizes innovation, safeguards the legitimate rights and interests of all parties, and promotes the widespread dissemination and sustainable development of ICH knowledge, providing theoretical support and pathway guidance for the living inheritance of ICH in the digital age. Intellectual property ; Craft ; Context (archaeology) ; Legislation ; Safeguarding ; TRIPS architecture ; Public rights ; Statutory law ; Inheritance (genetic algorithm) Wood and Agarwood Research ; Cultural Heritage Management and Preservation ; Archaeological Research and Protection
Authors: Xiang-Shan Kong | Zi-Yang Cao | Kang-ni He | Fang-Fang Ran
Source: AIP Advances (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Temperature-dependent monovacancy diffusion in tungsten: Bridging the gap between experiment and theory Tungsten, a key plasma-facing material for fusion reactors, exhibits self-diffusion behavior that deviates from the simple Arrhenius kinetics. Experiments suggest that monovacancy-mediated diffusion dominates below ∼2400 K; however, theoretical diffusivities overestimate measurements by up to two orders of magnitude. Here, we reconcile this discrepancy through first-principles calculations that explicitly incorporate temperature-dependent effects, including the lattice thermal expansion and vibrational free energy. We found that the vacancy formation energy increases quadratically with temperature, primarily due to vibrational contributions at high temperatures, while the migration barrier remains nearly constant. The resulting diffusion coefficients closely agree with the experimental data in the range 1700–2400 K. Moreover, despite the strong temperature dependence of the activation energy and pre-exponential factor, their variation satisfies a Meyer–Neldel-type compensation relationship, preserving an overall Arrhenius-form for diffusivity. Our results demonstrate that below ∼2400 K, self-diffusion in tungsten is governed by monovacancy-mediated atomic migration, whereas above this threshold, enhanced diffusion might arise from collective atomic mechanisms. These findings resolve a long-standing theory–experiment discrepancy and provide essential input for modeling the microstructural evolution in fusion reactor environments. Tungsten ; Arrhenius equation ; Vacancy defect ; Thermal diffusivity ; Thermodynamics ; Activation energy ; Diffusion ; Materials science ; Condensed matter physics ; Chemistry ; Physical chemistry ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Rare-earth and actinide compounds
Authors: Pierre-Brice Bintein | Pierre-Yves Passaggia | Nicolas Mazellier | Adrien Bussonnière
Source: Physical Review Fluids (article, 2025-09-23)
Field: Materials Science — Surfaces, Coatings and Films

Listening to immersed superhydrophobic surfaces: Acoustic inspection of air plastron layers When immersed in water, a solid coated with a superhydrophobic surface traps an air layer, called a plastron, that shields it from liquid contact. This layer enables underwater respiration in animals and provides drag reduction, anti-corrosion, and antifouling effects. However, plastrons are sensitive to external disturbances and can destabilize. This study demonstrates how plastron acoustic resonance can be used to monitor and measure the trapped air layer volume. Validated against direct observations, the method is portable, noninvasive, and effective for studying plastron stability under realistic conditions and various flow conditions. Drag ; Underwater ; Bubble ; Air bubble ; Layer (electronics) ; Vibrator (electronic) ; Measure (data warehouse) ; Noise (video) Surface Modification and Superhydrophobicity ; Aerosol Filtration and Electrostatic Precipitation ; Ocean Waves and Remote Sensing
Authors: Michał Sanocki | Julija Zavadlav
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their... Generalization ; Space (punctuation) ; Probably approximately correct learning ; Chemical space ; Statistical learning ; Generalization error Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Soohaeng Yoo Willow | Tae Park | Gi Beom Sim | Seung Jae Moon | Seung Kyu Min | D. ChangMo Yang | Hae Lyong Kim | Juho Lee | Chang Woo Myung
Source: arXiv (Cornell University) (preprint, 2025-10-03)
Field: Materials Science — Materials Chemistry

Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current MLPs struggle with uncertainty quantification, limiting their reliability for active learning, calibration, and out-of-distribution (OOD) detection. We address these challenges by developing Bayesian E(3) equivariant MLPs with iterative restratification of many-body message passing. Our approach introduces the joint energy-force negative log-likelihood (NLL$_\text{JEF}$) loss function, which explicitly models uncertainty in both energies and interatomic forces, yielding substantially improved accuracy compared to conventional NLL losses. We systematically benchmark multiple Bayesian approaches, including deep ensembles with mean-variance estimation, stochastic weight averaging Gaussian, improved variational online Newton, and Laplace approximation by evaluating their performance on uncertainty prediction, OOD detection, calibration, and active learning tasks. We further demonstrate that NLL$_\text{JEF}$ facilitates efficient active learning by quantifying energy and force uncertainties. Using Bayesian active learning by disagreement (BALD), our framework outperforms random sampling and energy-uncertainty-based sampling. Our results demonstrate that Bayesian MLPs achieve competitive accuracy with state-of-the-art models while enabling uncertainty-guided active learning, OOD detection, and energy/forces calibration. This work establishes Bayesian equivariant neural networks as a powerful framework for developing uncertainty-aware MLPs for atomistic simulations at scale. Benchmark (surveying) ; Bayesian probability ; Laplace's method ; Uncertainty quantification ; Variable-order Bayesian network ; Bayesian inference ; Limit (mathematics) ; Artificial neural network ; Active learning (machine learning) Machine Learning in Materials Science ; Quantum many-body systems ; Gaussian Processes and Bayesian Inference
Authors: Robin Turnbull | Zhang YingLong | Claudio Cazorla | Akun Liang | Saqib Rahman | Miriam Peña‐Álvarez | Cătălin Popescu | L.G. Pampillo | Daniel Errandonea
Source: Physical review. B./Physical review. B (article, 2025-09-22)
Field: Chemistry — Inorganic Chemistry

Theory-guided discovery of pressure-induced transitions in the fast-ion conductor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>BaSn</mml:mi> <mml:msub> <mml:mi mathvariant="normal">F</mml:mi> <mml:mn>4</mml:mn> </mml:msub> </mml:mrow> </mml:math> Fast-ion conductors such as <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:mi>BaSn</a:mi> <a:msub> <a:mi mathvariant="normal">F</a:mi> <a:mn>4</a:mn> </a:msub> </a:mrow> </a:math> are of significant interest for next-generation solid-state battery technologies due to their high ionic conductivity and chemical stability. However, the behavior of these materials under extreme conditions remains poorly understood, despite the relevance of pressure-induced modifications for tuning functional properties. In this study, we combine density functional theory (DFT) calculations with high-pressure experiments to investigate the structural evolution of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:mrow> <c:mi>BaSn</c:mi> <c:msub> <c:mi mathvariant="normal">F</c:mi> <c:mn>4</c:mn> </c:msub> </c:mrow> </c:math> up to 40 GPa. DFT predicts two pressure-induced phase transitions: from the ambient-pressure tetragonal <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:mrow> <e:mi>P</e:mi> <e:mn>4</e:mn> </e:mrow> </e:math> / phase to a monoclinic <f:math xmlns:f="http://www.w3.org/1998/Math/MathML"> <f:mrow> <f:mi>P</f:mi> <f:msub> <f:mn>2</f:mn> <f:mn>1</f:mn> </f:msub> <f:mo>/</f:mo> <f:mi>m</f:mi> </f:mrow> </f:math> -I structure at 10 GPa, and subsequently to a denser monoclinic <g:math xmlns:g="http://www.w3.org/1998/Math/MathML"> <g:mrow> <g:mi>P</g:mi> <g:msub> <g:mn>2</g:mn> <g:mn>1</g:mn> </g:msub> </g:mrow> </g:math> /m-II phase at 32 GPa. The first transition is experimentally confirmed via angle-dispersive X-ray diffraction, Raman spectroscopy, and electrical resistivity measurements, all performed at ambient temperature. The second transition is supported by distinct changes in high-pressure Raman modes and resistivity behavior, consistent with a further structural reorganization. These findings not only clarify the high-pressure phase diagram of <h:math xmlns:h="http://www.w3.org/1998/Math/MathML"> <h:mrow> <h:mi>BaSn</h:mi> <h:msub> <h:mi mathvariant="normal">F</h:mi> <h:mn>4</h:mn> </h:msub> </h:mrow> </h:math> , but also shed light on the potential for pressure-tuned ionic transport in fluorostannate-based solid electrolytes. Monoclinic crystal system ; Phase diagram ; Electrical resistivity and conductivity ; Raman spectroscopy ; Tetragonal crystal system ; Phase transition ; Phase (matter) Inorganic Fluorides and Related Compounds ; Thermal Expansion and Ionic Conductivity ; High-pressure geophysics and materials
Authors: Yu Liu | Jonathan D. Hirst | Jianfeng Ren | Bencan Tang | Dave Towey
Source:  (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Chemically-aware Attention-based Multi-modal Fusion Framework for Molecular Representation Learning Learning effective molecular representations is crucial for accurate property prediction in artificial intelligence (AI)-aided drug discovery. Graph and fingerprint representations have been widely used to encode molecular topological structures and chemical substructures. To enhance the feature embedding of each modality and leverage their complementary strengths, we propose a novel Chemically-aware Attention-based Multi-modal Fusion Framework (CAMFF) for molecular representation learning, which integrates molecular graphs and extended-connectivity fingerprints by exploiting various attention mechanisms. Specifically, the proposed CAMFF consists of three modules: 1) a graph embedding module incorporating multi-head attention to capture local heterogeneous interactions and all-pair self-attention to capture long-range atomic dependencies from molecular graph representations; 2) a fingerprint embedding module using a pre-trained Mol2Vec model to generate dense chemical substructure representations; and 3) a chemically-aware feature interaction and fusion module incorporating self-attention to enable interactions between various chemical substructures and cross-attention to ensure effective multi-modal alignment and fusion. To evaluate the effectiveness of CAMFF, we compare it with 14 state-of-the-art methods across 9 molecular property prediction benchmarks. CAMFF demonstrates competitive predictive performance and improves interpretability through attention-based visualization, showing its potential for real-world drug discovery. Modal ; Computer science ; Representation (politics) ; Fusion ; Artificial intelligence ; Human–computer interaction ; Materials science ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhixu Dai | Ziyi Tong | Xin Zhang | Ding Li | Tingting Chen | Yong Pan
Source: Journal of Thermal Analysis and Calorimetry (article, 2025-10-17)
Field: Materials Science — Polymers and Plastics

Prediction of glass transition temperatures of polyimide for thermal safety from molecular structures Quantitative structure–activity relationship ; Glass transition ; Molecular descriptor ; Polyimide ; Work (physics) ; Polymer ; Mean squared error ; Loo Synthesis and properties of polymers ; Thermal and Kinetic Analysis ; Epoxy Resin Curing Processes
Authors: Dilafruz Kenjabayeva | Abdisalim Kenjaboyev | Zebo Salimova | O‘g‘iloy Mamaraimova | Feruza Ollokulova | Umida Namozova
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Explainable AI in computational optics: interpreting material property relationships Explainable AI plays a crucial role in material science, particularly for interpreting material-property relationships. Coupled with existing optical theory codes, explainable AI elucidates the underlying physics of material behavior in optical materials. An analysis framework integrates various explain-ability techniques to interpret the interdependence of multiple optical and material properties. The approach is applied to glasses, polymers, and multicomponent composites. Fine-grained materials classification improves the accuracy of optical-property prediction. In glasses, the fundamental composition–property relationship can be elucidated. The segregation of elemental properties accounts for the variation in refractive-index behavior. Formulas with guiding physical insights are automatically discovered. Interpreted AI enables data-driven material design for mould-production-quality control. The proposed method offers an interpretability-driven development paradigm, representing the next step toward explainable, trustworthy material design, which bridges prior physics rules and experimental observations in computational optics. Property (philosophy) ; Trustworthiness ; Variation (astronomy) ; Computational model ; Material properties Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Glass properties and applications
Authors: Sinha, Samjukta | Das, Prabhat
Source: arXiv (Cornell University) (preprint, 2025-11-04)
Field: Materials Science — Materials Chemistry

From data to design: Random forest regression model for predicting mechanical properties of alloy steel This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science. Random forest ; Residual ; Ultimate tensile strength ; Regression analysis ; Regression ; Material properties ; Linear regression ; Artificial neural network ; Correlation coefficient Machine Learning in Materials Science ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Priti Kumari | Ankush Kumar | Rohit Bhatia
Source: Bioorganic Chemistry (article, 2025-11-20)
Field: Chemistry — Organic Chemistry

Green and sustainable approaches to Quinazoline-based anticancer agents: Synthesis and structure activity relationship (SAR) Atom economy ; Quinazoline ; Drug discovery ; Anticancer drug ; Cytotoxicity ; Structure–activity relationship ; Green chemistry Quinazolinone synthesis and applications ; Synthesis and biological activity ; Synthesis and bioactivity of alkaloids
Authors: Yinyan Zhang | Xin Zhang | Shengjie Niu | Yong Pan
Source: Journal of Loss Prevention in the Process Industries (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Prediction of the exothermic onset temperature of aromatic nitro compounds by the SiRMS descriptors from molecular structures Nitro ; Exothermic reaction ; Molecular descriptor ; Representation (politics) ; Nitro compound ; Nonlinear system ; Aromaticity ; Linear regression ; Quantitative structure–activity relationship Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Razan Naji | Zeinah Almulhim | Yassir A. Almofti | Maryam Mahmoud | Mahmoud Kandeel
Source: Journal of Applied Pharmaceutical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Advancements and applications of COX inhibitors: A detailed bibliometric analysis of trending topics and emerging themes Cyclooxygenase (COX) inhibitors are an important aspect of pain and inflammation therapy. This study provides a comprehensive bibliometric analysis of COX inhibitor research in order to identify key patterns, themes, emerging topics, authors, journals, and worldwide collaboration networks. The Scopus database was used to search for research on COX inhibitors from inception to 2024. The study themes, co-authorship networks, citation patterns, and publication growth were analyzed using R bibliometric tools such as Bibliometrix and VOSviewer. The findings show a 17.82% rate of international collaboration among the 3,400 publications examined, which came from 1,219 journals and were produced by 13,993 authors. The annual growth in research production was 4.15 percent. Celecoxib and molecular docking were frequently referenced, and the research was primarily concerned with inflammation, cancer prevention, and COX-2 inhibition. The evolution of trend topics can be summarized as follows: from early 2000s foundational pharmacological investigations to mid-2000s selective COX-2 inhibitors with reduced side effects. Computational techniques like molecular docking, 3D-QSAR, and statistical approaches have lately gained popularity for optimizing drug discovery. COX inhibitor research is expected to focus on using AI-driven drug discovery to develop more selective and less hazardous drugs. Data science ; Computer science ; Computational biology ; Biology Synthesis and biological activity ; Inflammatory mediators and NSAID effects
Authors: S.S. Kulkarni | Seema S. Deshpande | Manjunath Shetty | Priyanka Sahare | Vidya Shinde | Ashwini Patil | Vinayak Walhekar | Shankar G. Alegaon | Shriram D. Ranade | Sateesh Bandaru | Ankit Ganeshpurkar | Ravindra Kulkarni
Source: Proceedings of the Indian National Science Academy (article, 2025-10-13)
Field: Chemistry — Organic Chemistry

2-substituted 3-oxindoles as glycogen synthase kinase 3β inhibitors: insights from ML based QSAR, molecular docking, and dynamics simulations Molecular dynamics ; Random forest ; Quantitative structure–activity relationship ; Linear regression ; Docking (animal) ; Test set ; Regression ; Ligand (biochemistry) ; Virtual screening Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Wnt/β-catenin signaling in development and cancer
Authors: Siyu Liu | Bo Hu | Beilin Ye | Jun Xu | David J. Srolovitz | Tongqi Wen
Source: ArXiv.org (preprint, 2025-05-16)
Field: Materials Science — Materials Chemistry

MatTools: Benchmarking Large Language Models for Materials Science Tools Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design. At the same time, a wide range of physics-based computational approaches have been developed in which materials properties can be calculated. Here, we propose a benchmark application to evaluate the proficiency of LLMs to answer materials science questions through the generation and safe execution of codes based on such physics-based computational materials science packages. MatTools is built on two complementary components: a materials simulation tool question-answer (QA) benchmark and a real-world tool-usage benchmark. We designed an automated methodology to efficiently collect real-world materials science tool-use examples. The QA benchmark, derived from the pymatgen (Python Materials Genomics) codebase and documentation, comprises 69,225 QA pairs that assess the ability of an LLM to understand materials science tools. The real-world benchmark contains 49 tasks (138 subtasks) requiring the generation of functional Python code for materials property calculations. Our evaluation of diverse LLMs yields three key insights: (1)Generalists outshine specialists;(2)AI knows AI; and (3)Simpler is better. MatTools provides a standardized framework for assessing and improving LLM capabilities for materials science tool applications, facilitating the development of more effective AI systems for materials science and general scientific research. Codebase ; Benchmarking ; Benchmark (surveying) ; Python (programming language) ; Property (philosophy) ; Materials informatics Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Inorganic Chemistry and Materials