jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. M. Hu | Haolong Tang | Yanchao Wang | Zhuang Liu | Zhonghua Li | Wenkai Gao
Source: Coatings (article, 2025-12-22)
Field: Materials Science — Surfaces, Coatings and Films

Gradient-Descent-Based Control of Moving Deposition for Large-Aperture Multilayer Films To address the challenge of achieving uniform coating on large-aperture substrates with significant sagittal height differences, this study employs a conventional-sized movable sputtering target combined with substrate rotation to realize high-uniformity control. The research establishes a geometric deposition model for the spatial thickness distribution of thin films on large-aperture, high-sagittal-height substrates and develops a precise control method for the deposition distribution on the substrate surface. Using the gradient descent algorithm, an optimal velocity modulation curve is calculated, and the spatial thickness distribution under this curve is determined. Compared with traditional least-squares optimization, this method effectively overcomes the issues of slow computation speed and poor convergence in large-scale numerical calculations, enabling rapid and uniform spatial control of large-aperture substrates. Calculation results demonstrate that the proposed method reduces the film non-uniformity from over 60% to a final value of 2.66%, with a corresponding PV value of 2.61%, across the 6550 mm aperture, showcasing its high precision. Deposition (geology) ; Substrate (aquarium) ; Coating ; Computation ; Convergence (economics) ; Gradient descent ; Sputter deposition ; Distribution (mathematics) Optical Coatings and Gratings ; Fluid Dynamics and Thin Films ; Optical Polarization and Ellipsometry
Authors: Chukwudi Afamefula Samuel Ojinnaka | Emeka Egbu Emejiaka | Franklin Erepamo Osaisai | Ayoade Kuye
Source: Nigerian Journal of Physics (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Small Modular Reactors: Physical Protection Considerations It is no longer news that Nuclear Energy provides one of the best alternatives for clean energy. In this regard, and to ensure that energy generation from this source is safer and cleaner, advancements have been ongoing and one of such is the concept of Small Modular Reactors (SMRs). SMRs offer more economic, and passive safety features, flexibility, and far less radiological consequences from accidents. Although only the Akademik Lomonosov is currently in use, research and development for other more efficient SMRs are ongoing. This work aims to provide a technical overview of SMRs, evaluate some emerging threats associated with SMRs, and analyze how a robust and effective physical protection system (PPS) could mitigate against these threats using two case studies. The two case studies considered were Akademik Lomonosov and Spar-Type Platform Design for the Offshore Floating Nuclear Power Plant (OFNP). It was observed that one of the impacts of SMR on PPS would be a significantly lower cost for PPS than in the traditional large nuclear power plant, owing largely to the difference in size. Also, in SMRs, security would be more centralized and enhanced. Situating SMRs offshore provides a better physical protection system in terms of natural phenomena or extreme weather events. Areas for improving the PPS of SMRs for the two case studies were identified and the best possible solutions were proffered. Modular design ; SAFER ; Nuclear power ; Work (physics) ; Physical security ; Submarine pipeline ; Best practice Graphite, nuclear technology, radiation studies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Mohammed Mutnbak | Ahmed Elbelbisi | Alaa El-Sisi | Ahmed Elkilani | Hani Salim
Source: Buildings (article, 2025-11-13)
Field: Materials Science — General Materials Science

Experimental Evaluation of Highway Bridge Fingerplates Under Cyclic Loading Fingerplate expansion joints are commonly used in bridges to accommodate large movements in bridge decks, often due to thermal expansion or contraction. Although these joints are designed to last the bridge’s lifetime, they have experienced premature degradation under high-volume vehicular loads. Damage to these joints can compromise structural integrity and endanger public safety. To address this, a series of experimental fatigue tests were conducted to simulate cyclic vehicular loading, with the goal of identifying the controlling failure modes and refining design practices for fingerplate expansion joints. The study involved constructing fingerplate joint specimens based on standard Missouri Department of Transportation (MoDOT) designs, incorporating three design variables: fingerplate thickness, flange stiffeners, and concrete embedment. Additionally, two optimized designs were developed and tested under both fatigue and static loading conditions. Two distinct failure types were observed in the specimens. Specimens with flange stiffeners experienced fatigue failure, characterized by crack propagation through the back weld of the fingerplate to the supporting beam. In contrast, specimens without flange stiffeners failed due to serviceability issues, as they could not sustain the required load before reaching the maximum allowable deformation, leading to buckling of the supporting beam’s top flange. The optimized designs showed no fatigue degradation and exhibited increased ultimate strengths compared to the standard MoDOT designs. Overall, a thicker fingerplate improved the stiffness and fatigue performance of the expansion joint, while bolted connections effectively eliminated the crack propagation fatigue failure observed in many specimens and in the field. Flange ; Serviceability (structure) ; Expansion joint ; Welding ; Stiffness ; Joint (building) ; Truss bridge ; Fatigue testing ; Failure mode and effects analysis Engineering and Material Science Research ; Asphalt Pavement Performance Evaluation ; Transportation Safety and Impact Analysis
Authors: Cheng-Jung Lin | Ping-Hsun Peng | Lin Pan
Source: Preprints.org (preprint, 2025-08-25)
Field: Chemistry — Analytical Chemistry

The Impact of the Number of Sensors on Stress Wave Velocity in 2D Acoustic Tomography of&lt;em&gt; Araucaria cunninghamii&lt;/em&gt; Sweet This study investigated the effect of the number of sensors (8, 12, 16, and 20) on the measurement results of stress wave velocity in two-dimensional acoustic tomography of Hoop pine (Araucaria cunninghamii Sweet) trees, and evaluated the method’s accuracy and operational efficiency in tree health diagnostics. Tests were conducted on five sample trees, two of which were confirmed to have internal damage using the drilling resistance method. The results indicated that increasing the number of sensors improved image resolution and information completeness. However, differences in average stress wave velocities among sensor configurations were not statistically significant (p ≥ 0.05), indicating limited variation overall. In healthy trees, stress wave velocities measured under different sensor quantities (e.g., 8 vs. 20) exhibited weak linear correlations (R2 = 0.06–0.58), reflecting relatively uniform internal structures. In contrast, damaged trees showed strong consistency in velocity results (R2 = 0.82–0.91, p &amp;amp;lt; 0.01), and both minimum and average velocities were significantly lower than in healthy trees. These findings demonstrate that acoustic tomography can effectively identify internal tree defects. Even with only eight sensors, decay and cavities can still be accurately detected, enhancing field inspection efficiency and reducing costs, thus showing strong potential for practical applications. Araucaria ; Mathematics ; Physics ; Horticulture ; Botany ; Biology Spectroscopy and Chemometric Analyses
Authors: Hossein Haghbin | 悦 赵 | Mehdi Maadooliat
Source: Foundations of Data Science (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Regularized multivariate functional principal component analysis for data observed on different domains Multivariate Functional Principal Component Analysis (MFPCA) is a valuable tool for exploring relationships and identifying shared patterns of variation in multivariate functional data. However, controlling the roughness of the extracted Principal Components (PCs) can be challenging. This paper introduces a novel approach called regularized MFPCA (ReMFPCA) to address this issue and enhance the smoothness and interpretability of the multivariate functional PCs. ReMFPCA incorporates a roughness penalty within a penalized framework, using a parameter vector to regulate the smoothness of each functional variable. The proposed method focuses on multivariate functional data on different domains and generates smoothed multivariate functional PCs, providing a concise and interpretable representation of the data. Extensive simulations and real data examples demonstrate the effectiveness of ReMFPCA and its superiority over alternative methods. The proposed approach opens new avenues for analyzing and uncovering relationships in complex multivariate functional datasets. Principal component analysis ; Multivariate statistics ; Multivariate analysis ; Component (thermodynamics) ; Functional principal component analysis ; Computer science ; Statistics ; Mathematics ; Econometrics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Remote-Sensing Image Classification
Authors: Danelle M. Larson | E. J. Lund | Alicia M. Carhart | Seth Fopma | Stephanie Szura
Source: Techniques and methods (article, 2025-01-01)
Field: Materials Science — Biomaterials

Long Term Resource Monitoring procedures—Aquatic vegetation monitoring This standard operating procedure (SOP) manual describes the collection of standardized, long-term data for aquatic vegetation communities in selected study pools of the Upper Mississippi River System in the United States.The primary intent of the data collection is to assess the status and trends that aid in understanding the unique river ecosystem and to guide large-scale ecological restoration of the river and its biological communities, like aquatic plants and their dependent wildlife.This SOP is an update to the version published in 2000 and reflects modifications to sample sizes and additions of new data collection procedures.All long-term monitoring programs and their SOPs must be adapted to changing conditions and be improved through learning, and this SOP clarifies procedures and adds new elements since the initial SOP was written more than 25 years ago.The SOP is intended for multiple audiences, including vegetation specialists through the Upper Mississippi River Restoration Program, data analysts using the publicly available data generated through this SOP, and natural resource managers and restoration practitioners who need data and science to guide some decisions.This SOP may be transferable and adaptable to other ecosystems when the aquatic plant community is the focus. Term (time) ; Vegetation (pathology) ; Resource (disambiguation) ; Hydrology (agriculture) ; Environmental monitoring Diatoms and Algae Research
Authors: Xiaoyu Xie | Yingchao Feng | Piyu Zhou | Di Zhang | Liwei Yao | Haipeng Wang
Source: Applied Sciences (article, 2025-06-09)
Field: Chemistry — Spectroscopy

A Compressed Sequence Tag Index for Fast Peptide Retrieval and Efficient Storage in Protein Identification Search Engines Proteins regulate various cellular processes and are of great biological interest. The protein search engine is a crucial tool in proteomics research, used to analyze high-throughput tandem mass spectrometry data and to identify protein sequence information. A core step in protein search engines is constructing sequence tag indexes and performing the rapid retrieval of protein databases. However, as the scale of protein sequence data continues to grow, traditional protein search engines face the dual challenges of the high storage cost of sequence tag indexes and low retrieval efficiency. To address these issues, we propose a sequence tag index scheme named STIP, which is based on an inverted index and compression techniques. Based on STIP, we design a peptide retrieval algorithm named STIP-Search. This algorithm utilizes the sequence tag index constructed by STIP for peptide sequence retrieval. STIP uses the greedy algorithm to partition the tag index into blocks; in this way, STIP can generate tag indexes for very large protein databases, such as NCBI-nr. Compared to the current four mainstream tag index generation algorithms used in Open-pFind, MODplus, TIIP and PIPI2, STIP has the lowest storage and time consumption. It utilizes delta encoding, index reduction, and dynamic bit width encoding to compress the tag index, reducing the storage cost by 76.2%. Compared to TIIP, which is currently the algorithm with the lowest time complexity, the time cost of the peptide sequence retrieval of STIP-Search is reduced by 8.94% to 23.31%. Identification (biology) ; Computer science ; Information retrieval ; Index (typography) ; Search engine ; Computational biology ; World Wide Web ; Biology Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Jiaming Fan | Chao Qian | Shichong Zhou
Source: ArXiv.org (preprint, 2025-09-22)
Field: Materials Science — Materials Chemistry

MesoNet: A Fundamental Principle for Multi-Representation Learning in Complex Chemical Systems Accurate prediction of molecular properties in complex chemical systems is crucial for accelerating material discovery and chemical innovation. However, current computational methods often struggle to capture the intricate compositional interplay across complex chemical systems, from intramolecular bonds to intermolecular forces. In this work, we introduce MesoNet, a novel framework founded on the principle of multi-representation learning and specifically designed for multi-molecule modeling. The core innovation of MesoNet lies in the construction of context-aware representation-dynamically enriched atomic descriptors generated via Neural Circuit Policies. These parameters efficiently capture both intrinsic atomic properties and their dynamic compositional context through a cross-attention mechanism spanning both intramolecular and intermolecular message passing. Driven by this mechanism, the influence of the mixed system is progressively applied to each molecule and atom, making message passing both efficient and meaningful. Comprehensive evaluations across diverse public datasets, spanning both pure components and mixtures, demonstrate that MesoNet achieves superior accuracy and enhanced chemical interpretability for molecular properties. This work establishes a powerful, interpretable approach for modeling compositional complexity, aiming to advance chemical simulation and design. Interpretability ; Intermolecular force ; Intramolecular force ; Complex system ; Context (archaeology) ; Mechanism (biology) ; Chemical process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Hong Sun | Joshua A. Vita | Amit Samanta | Vincenzo Lordi
Source: ArXiv.org (preprint, 2025-09-15)
Field: Materials Science — Materials Chemistry

Unsupervised Atomic Data Mining via Multi-Kernel Graph Autoencoders for Machine Learning Force Fields Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization. Autoencoder ; Cluster analysis ; Pruning ; Graph ; Outlier ; Representation (politics) ; Unsupervised learning ; Pattern recognition (psychology) ; Feature learning Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Data Quality and Management
Authors: Haiyang Yu | Yun Lin | Xuan Zhang | Xiaofeng Qian | Shuiwang Ji
Source: ArXiv.org (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set of new efficient and powerful SO(2)-equivariant operations and performing all off-diagonal feature updates and message passing within SO(2) local frames, thereby eliminating the need of SO(3) tensor products. Moreover, a continuous SO(2) tensor product is performed within the SO(2) local frame at each node to fuse node features, mimicking the symmetric contraction operation. Extensive experiments on the large QH9 and MD17 datasets demonstrate that our model achieves superior performance across a wide range of molecular structures and trajectories, highlighting its strong generalization capability. The proposed SO(2) operations on SO(2) local frames offer a promising direction for scalable and symmetry-aware learning of electronic structures. Our code will be released as part of the AIRS library https://github.com/divelab/AIRS. Scalability ; Hamiltonian (control theory) ; Matrix multiplication ; Redundancy (engineering) ; Tensor product ; Tensor (intrinsic definition) ; Generalization ; Node (physics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: J. Wang | Yaosen Min | Xun Zhu | Miao Li | Ji Wu
Source:  (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

MIPS: A Multimodal Infinite Polymer Sequence Pre-training Framework for Polymer Property Prediction Polymers, composed of repeating structural units called monomers, are fundamental materials with a wide range of applications in daily life and industry. Accurate property prediction for polymers is essential for their design, development, and application. However, existing modeling approaches, which typically represent polymers by the constituent monomers, struggle to capture the whole properties of polymer, since the properties change during the polymerization process. In this study, we propose a Multimodal Infinite Polymer Sequence (MIPS) pre-training framework, which represents polymers as infinite sequences of monomers and integrates both topological and spatial information for comprehensive modeling. From the topological perspective, we generalize message passing mechanism (MPM) and graph attention mechanism (GAM) to infinite polymer sequences. For MPM, we demonstrate that applying MPM to infinite polymer sequences is equivalent to applying MPM on the induced star-linking graph of monomers. For GAM, we propose to further replace global graph attention with localized graph attention (LGA). Moreover, we show the robustness of the ''star linking'' strategy through an adversarial evaluation method named Repeat and Shift Invariance Test (RSIT). Despite its robustness, ''star linking'' strategy exhibits limitations when monomer side chains contain ring structures, a common characteristic of polymers, as it fails the Weisfeiler-Lehman (WL) test. To overcome this issue, we propose backbone embedding to enhance the capability of MPM and LGA on infinite polymer sequences. From the spatial perspective, we extract 3D descriptors of repeating monomers to capture spatial information. Finally, we design a cross-modal fusion mechanism to unify the topological and spatial information. Experimental validation across eight diverse polymer property prediction tasks reveals that MIPS achieves state-of-the-art performance. Ablation studies further comfirm the efficacy of our infinite polymer sequence modeling approach and multimodal pre-training framework. Polymer ; Embedding ; Sequence (biology) ; Robustness (evolution) ; Graph ; Topology (electrical circuits) ; Monomer ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Petroleum Processing and Analysis
Authors: Niccolò Di Eugenio | Ashley Dickson | Flyura Djurabekova | F. Laviano | Federico Ledda | Daniele Torsello | Erik Gallo | Mark R. Gilbert | D. Nguyen-Manh | Antonio Trotta | Samuel T. Murphy | Davide Gambino
Source: ArXiv.org (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa$_2$Cu$_3$O$_{7-δ}$ High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's superconductivity depends strongly on oxygen stoichiometry and defect structure, atomistic simulations can provide crucial insight into radiation-damage mechanisms and pathways to maintain material performance. In this work, we develop and benchmark four Machine-Learned Interatomic Potentials (MLPs) for YBCO: the Atomic Cluster Expansion (ACE), the Message-Passing Atomic Cluster Expansion (MACE), the Gaussian Approximation Potential (GAP), and the Tabulated Gaussian Approximation Potential (tabGAP), trained on an extensive Density Functional Theory (DFT) database explicitly designed to include irradiation-damaged-like configurations. The resulting models achieve DFT-level accuracy across a wide range of atomic environments, faithfully capturing the interatomic forces relevant to radiation damage processes. Among the tested models, MACE delivers the highest accuracy, although at greater computational cost, while ACE and tabGAP provide an excellent balance between efficiency and fidelity. These machine-learned potentials establish a robust foundation for large-scale molecular dynamics simulations of radiation-induced defect evolution in complex superconducting materials Interatomic potential ; Superconductivity ; Molecular dynamics ; Gaussian ; Density functional theory ; Cluster expansion ; Cluster (spacecraft) ; Stoichiometry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Block Copolymer Self-Assembly