jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Monzure-Khoda Kazi | E. Mahdi
Source: Composites Part C Open Access (article, 2024-02-03)
Field: Materials Science — Polymers and Plastics

Crashworthiness optimization of composite hexagonal ring system using random forest classification and artificial neural network This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10−7 and 6.40 × 10−7, respectively, for X1 and X2 loading conditions at 45o angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort. Crashworthiness ; Artificial neural network ; Random forest ; Composite number ; Structural engineering ; Algorithm ; Artificial intelligence ; Materials science ; Machine learning ; Computer science ; Engineering ; Finite element method Natural Fiber Reinforced Composites ; Cellular and Composite Structures ; Mechanical Behavior of Composites
Authors: Maurits Vuijk | Gianmarco Ducci | Luis Sandoval | Markus Ruben Pietsch | Karsten Reuter | Thomas Lunkenbein | Christoph Scheurer
Source: ChemRxiv (preprint, 2024-05-16)
Field: Materials Science — Materials Chemistry

Physics-Based Synthetic Data Model for Automated Segmentation in Catalysis Microscopy In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for non-automated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model. We study environmental SEM (ESEM) data collected from isopropanol oxidation to acetone over cobalt oxide. Upon applying a temperature program during the reaction a phase transition occurs, reducing the catalyst selectivity towards acetone. This is accompanied on the μm ESEM by the formation of cracks between the pores of the catalyst surface. We aim to generate synthetic data to train a neural network capable of semantic segmentation (pixel-wise labelling) of this ESEM data. This analysis will lead to insights into this phase transition. To generate synthetic data that approximates this transition, our algorithm composes the ESEM images of the room-temperature catalyst with dynamically evolving synthetic cracks satisfying physical construction principles, gathered from qualitative knowledge accessible in the ESEM data. We mimic the surface crack growth propagation along surface paths, avoiding close vicinity to nearby pores. This physics-based approach results in a lowered rate of false positives compared to a random approach. Segmentation ; Microscopy ; Computer science ; Nanotechnology ; Artificial intelligence ; Data science ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Hydrocarbon exploration and reservoir analysis
Authors: Xueqian Xia | Zengying Ma | Yucheng Huang
Source: Chinese Journal of Chemical Physics (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Oxygen reduction reaction activity of Fe-based dual-atom catalysts with different local configurations via graph neural representation The performance of proton exchange membrane fuel cells depends heavily on the oxygen reduction reaction (ORR) at the cathode, for which platinum-based catalysts are currently the standard. The high cost and limited availability of platinum have driven the search for alternative catalysts. While FeN4 single-atom catalysts have shown promising potential, their ORR activity needs to be further enhanced. In contrast, dual-atom catalysts (DACs) offer not only higher metal loading but also the ability to break the ORR scaling relations. However, the diverse local structures and tunable coordination environments of DACs create a vast chemical space, making large-scale computational screening challenging. In this study, we developed a graph neural network (GNN)-based framework to predict the ORR activity of Fe-based DACs, effectively addressing the challenges posed by variations in local catalyst structures. Our model, trained on a dataset of 180 catalysts, accurately predicted the Gibbs free energy of ORR intermediates and overpotentials, and identified 32 DACs with superior catalytic activity compared to FeN4 SAC. This approach not only advances the design of high-performance DACs, but also offers a powerful computational tool that can significantly reduce the time and cost of catalyst development, thereby accelerating the commercialization of fuel cell technologies. Catalysis ; Dual (grammatical number) ; Oxygen ; Oxygen atom ; Representation (politics) ; Graph ; Oxygen reduction reaction ; Reduction (mathematics) ; Atom (system on chip) ; Chemistry ; Biological system ; Photochemistry ; Combinatorial chemistry ; Computer science ; Computational chemistry ; Mathematics ; Physical chemistry ; Organic chemistry ; Theoretical computer science ; Molecule ; Parallel computing ; Geometry ; Biology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Vincent Nebel | Lisa Beran | Veit Königer | Amir Haghipour | Marcel Mutz | Andriy Taranovskyy | Dirk Werth | Volker Knoblauch | Tobias Kraus
Source: Advanced Engineering Materials (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Developing an Ontology on Battery Production and Characterization with the Help of Key Use Cases from Battery Research Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li‐ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability. Battery (electricity) ; Key (lock) ; Ontology ; Materials science ; Characterization (materials science) ; Production (economics) ; Systems engineering ; Manufacturing engineering ; Computer science ; Nanotechnology ; Engineering ; Physics ; Philosophy Machine Learning in Materials Science ; Semantic Web and Ontologies ; Service-Oriented Architecture and Web Services
Authors: J. Thorben Frank | Stefan Chmiela | Klaus‐Robert Müller | Oliver T. Unke
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions. Self-attention offers a compelling mechanism for capturing these global effects, but its quadratic complexity presents a significant practical limitation. This problem is particularly pronounced in computational chemistry, where the stringent efficiency requirements of machine learning force fields (MLFFs) often preclude accurately modeling long-range interactions. To address this, we introduce Euclidean fast attention (EFA), a linear-scaling attention-like mechanism designed for Euclidean data, which can be easily incorporated into existing model architectures. A core component of EFA are novel Euclidean rotary positional encodings (ERoPE), which enable efficient encoding of spatial information while respecting essential physical symmetries. We empirically demonstrate that EFA effectively captures diverse long-range effects, enabling EFA-equipped MLFFs to describe challenging chemical interactions for which conventional MLFFs yield incorrect results. Euclidean geometry ; Computer science ; Euclidean distance ; Artificial intelligence ; Theoretical computer science ; Arithmetic ; Machine learning ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Meiyan Wang | Rishi Rao | Li Zhu
Source: Journal of Materials Informatics (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Exploring phase transitions in CdSe: a machine learning and swarm intelligence approach The phase transition of cadmium selenide (CdSe) from wurtzite to rocksalt structure has been the subject of extensive research. In this study, we present a novel approach combining machine learning potentials with swarm intelligence-based pathway sampling to elucidate the complex phase transition mechanisms in CdSe. We developed an accurate machine-learning (ML) potential for CdSe, validated against density functional theory calculations, achieving mean absolute errors (MAEs) of 1.8 meV/atom for energies and 33 meV/Å for forces. This potential was integrated with the pathway sampling via swarm intelligence and graph theory (PALLAS) method to explore the potential energy landscape and identify low-energy transition pathways. Our simulations revealed a complex network of transition pathways, and we discovered a multi-step transition mechanism involving an unexpected zinc blende intermediate phase, which appears to play a crucial role in facilitating the transition between wurtzite and rocksalt structures. This finding provides new insights into the structural flexibility of CdSe and offers an explanation for experimentally observed phenomena such as wurtzite/zinc blende coexistence in nanostructures. Our approach not only advances the fundamental understanding of phase transitions in CdSe but also establishes a powerful computational framework for exploring complex materials phenomena, opening new avenues for materials design and discovery in semiconductor systems. Swarm intelligence ; Phase (matter) ; Artificial intelligence ; Swarm behaviour ; Computer science ; Materials science ; Machine learning ; Physics ; Particle swarm optimization ; Quantum mechanics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials
Authors: Jingjing Hu | Dan Guo | Zhan Si | Deguang Liu | Yunfeng Diao | Jing Zhang | Jinxing Zhou | Meng Wang
Source: arXiv (Cornell University) (preprint, 2024-12-21)
Field: Chemistry — Physical and Theoretical Chemistry

MOL-Mamba: Enhancing Molecular Representation with Structural &amp; Electronic Insights Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom &amp; Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets. Representation (politics) ; Computer science ; Chemistry Various Chemistry Research Topics ; Inorganic and Organometallic Chemistry ; Chemical Synthesis and Reactions
Authors: Jaeyun Moon | Zhiting Tian
Source: arXiv (Cornell University) (preprint, 2024-05-12)
Field: Materials Science — Materials Chemistry

Crystal-like thermal transport in amorphous carbon Thermal transport properties of amorphous carbon has attracted increasing attention due to its extreme thermal properties: It has been reported to have among the highest thermal conductivity for bulk amorphous solids up to $\sim$ 37 Wm\textsuperscript{-1}K\textsuperscript{-1}, comparable to crystalline sapphire ($α$-Al\textsubscript{2}O\textsubscript{3}). Further, large density dependence in thermal conductivity demonstrates a potential for largely tunable thermal conductivity. However, mechanism behind the high thermal conductivity and its large density dependence remains elusive due to many variables at play. In this work, we perform large-scale ($\sim$ 10\textsuperscript{5} atoms) molecular dynamics simulations utilizing a machine learning potential based on neural networks. Through spectral decomposition of thermal conductivity which enables a quantum correction to classical heat capacity, we find that propagating vibrational excitations govern thermal transport in amorphous carbon ($\sim$ 100 \% of thermal conductivity) in sharp contrast to the conventional wisdom that diffusive vibrational excitations dominate thermal transport in amorphous solids. Instead, this remarkable behavior resembles thermal transport in simple crystals. Moreover, our temperature dependent spectral diffusivity and velocity current correlation analyses reveal that the density dependent thermal conductivity originates from anharmonicity sensitive propagating excitations. Our work suggests a novel insight and design principle into developing mechanically hard, thermally conductive amorphous solids. Amorphous solid ; Crystal (programming language) ; Thermal ; Materials science ; Carbon fibers ; Amorphous carbon ; Crystallography ; Chemistry ; Composite material ; Physics ; Computer science ; Thermodynamics ; Composite number Diamond and Carbon-based Materials Research ; Thermal properties of materials ; Graphene research and applications
Authors: D. Mazumder | Md. Touhid Islam | Arnab Naha Ushna
Source: SSRN Electronic Journal (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Band Gap Prediction of Inorganic Materials Using Artificial Intelligence Through Feature Transformation Transformation (genetics) ; Feature (linguistics) ; Artificial intelligence ; Materials science ; Computer science ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Chunyi Zhang | Marcos F. Calegari Andrade | Zachary K. Goldsmith | Abhinav S. Raman | Yifan Li | Pablo M. Piaggi | Xifan Wu | Roberto Car | Annabella Selloni
Source: arXiv (Cornell University) (preprint, 2024-03-29)
Field: Chemistry — Physical and Theoretical Chemistry

Electrical double layer and capacitance of TiO2 electrolyte interfaces from first principles simulations The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurately calculate the electrostatic potential drop at the interface. The computed capacitance originating from the adsorbed charges and the potential drop agrees with experiments, supporting the reliability of our description of the EDL. The larger interfacial capacitance of basic relative to acidic solutions originates from the higher affinity of the cations for the oxide surface and gives rise to distinct charging mechanisms on negative and positive surfaces. Capacitance ; Electrolyte ; Materials science ; Layer (electronics) ; Double layer (biology) ; Engineering physics ; Composite material ; Chemistry ; Physics ; Electrode ; Physical chemistry Electrostatics and Colloid Interactions ; Fuel Cells and Related Materials ; Electrophoretic Deposition in Materials Science
Authors: Rubin Huo | Chenyi Zhou | Boyi Situ | Zihan Yan | Hao Yang | Zhengyang Fei | Qin Jin | Liang Zhao | Zhe Zhang | Yusong Tu
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Hydroxyl-assisted globally spontaneous dynamic oxygen migration on biphenylene Biphenylene ; Covalent bond ; Chemical physics ; Oxygen ; Monolayer ; Photochemistry ; Chemistry ; Molecular dynamics ; Materials science ; Dangling bond ; Nanotechnology ; Computational chemistry ; Organic chemistry ; Hydrogen ; Phenylene Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electrochemical Analysis and Applications
Authors: Meiheng Lv | Yifan Zhang | Runze Liu | Yinhua Ma | Li Liu | Wenze Li | Huaxin Liu | Jianyong Liu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploring the Thermal Decomposition Mechanism of Nitromethane Via a Neural Network Potential Nitromethane ; Mechanism (biology) ; Artificial neural network ; Thermal decomposition ; Decomposition ; Computer science ; Biochemical engineering ; Chemistry ; Artificial intelligence ; Engineering ; Philosophy ; Epistemology ; Organic chemistry Machine Learning in Materials Science ; Energetic Materials and Combustion ; Thermal and Kinetic Analysis
Authors: Yujian Sun | Hongyi Pan | Bitong Wang | Yu Li | Xuelong Wang | Jizhou Li | Xiqian Yu
Source: Applied Physics Letters (article, 2024-10-21)
Field: Materials Science — Surfaces, Coatings and Films

High-fidelity reconstruction of porous cathode microstructures from FIB-SEM data with deep learning Accurate modeling of lithium-ion battery (LIB) electrode microstructures provides essential references for understanding degradation mechanisms and optimizing materials. Traditional segmentation methods often struggle to accurately capture the complex microstructures of porous LIB electrodes in focused ion beam scanning electron microscopy (FIB-SEM) data. In this work, we develop a deep learning model based on the Swin Transformer to segment FIB-SEM data of a lithium cobalt oxide electrode, utilizing fused secondary and backscattered electron images. The proposed approach outperforms other deep learning methods, enabling the acquirement of 3D microstructure with reduced particle elongated artifacts. Analyses of the segmented microstructures reveal improved electrode tortuosity and pore connectivity crucial for ion and electron transport, emphasizing the necessity of accurate 3D modeling for reliable battery performance predictions. These results suggest a path toward voxel-level degradation analysis through more sensible battery simulation on high-fidelity microstructure models directly twinned from real porous electrodes. Cathode ; Materials science ; Microstructure ; Porosity ; Nanotechnology ; High fidelity ; Optoelectronics ; Metallurgy ; Composite material ; Engineering ; Electrical engineering Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Kweeni Iduoku | Marvellous Ngongang | Jayani Kulathunga | Amirreza Deghighi | Gerardo M. Casañola‐Martín | Şenay Şimşek | Bakhtiyor Rasulev
Source: Preprints.org (preprint, 2024-06-06)
Field: Chemistry — Analytical Chemistry

Phenolic Acid – β-cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine-Learning-based QSAR Study The need for solvating and encapsulating hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in pharmaceutical industry, food, polymer, materials, and agricultural science. Among them, &amp;beta;-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for a good predictive model that assesses &amp;beta;-cyclodextrin bitterness masking capabilities for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked to the &amp;beta;-cyclodextrin cavity to generate three different binding constants. The data from docking study were combined with topological, topographical and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of Genetic Algorithm (GA) and Multiple Linear Regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984, for training and test sets, respectively. The models revealed several factors responsible for a binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area. Cyclodextrin ; Bran ; Quantitative structure–activity relationship ; Chemistry ; Food science ; Organic chemistry ; Stereochemistry Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; GABA and Rice Research