jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Vondrák | Karsten Reuter | Johannes T. Margraf
Source: The Journal of Chemical Physics (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

<tt>q-pac</tt> : A Python package for machine learned charge equilibration models Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They, therefore, lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models. Python (programming language) ; Electronegativity ; Computer science ; Electrostatics ; Charge (physics) ; Computational science ; Kernel (algebra) ; Range (aeronautics) ; Representation (politics) ; Statistical physics ; Chemistry ; Chemical physics ; Physics ; Materials science ; Physical chemistry ; Programming language ; Quantum mechanics ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Pengfei Hou | Yumiao Tian | Yu Xie | Fei Du | Gang Chen | Aleksandra Vojvodić | Jianzhong Wu | Xing Meng
Source: Angewandte Chemie (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active‐Learning‐Potential Molecular‐Dynamics Simulation Abstract MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super‐sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes. MXenes ; Aqueous solution ; Molecular dynamics ; Chemistry ; Process (computing) ; Materials science ; Nanotechnology ; Computer science ; Chemical physics ; Computational chemistry ; Organic chemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Mingkang Liu | Yanbo Han | Yonghong Cheng | Xiang Zhao | Hongxing Zheng
Source: Carbon (article, 2023-06-10)
Field: Materials Science — Materials Chemistry

Exploring exohedral functionalization of fullerene with automation and Neural Network Potential Fullerene ; Surface modification ; Nanoclusters ; Computer science ; Automation ; Artificial intelligence ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Fullerene Chemistry and Applications ; Catalysis and Oxidation Reactions
Authors: Christoph Dösinger | Max Hodapp | Oleg E. Peil | Alexander Reichmann | Vsevolod I. Razumovskiy | Daniel Scheiber | Lorenz Romaner
Source: Physical Review Materials (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Efficient descriptors and active learning for grain boundary segregation Segregation of solutes to grain boundaries (GBs) is an important process having a large impact on mechanical properties of metallic alloys. In this work, we show how accurate density functional theory (DFT) calculations can be combined with machine learning methods to obtain reliable GB segregation energies with significantly lower computational efforts compared to a full ab initio approach. First we compare various descriptor sets with respect to their efficiency in predicting segregation energies for arbitrary GB types. Second, we demonstrate that active learning can be employed to optimize the initial training set obtained by DFT calculations. The methodology is applied to the GB segregation of Re in a WRe alloy, which is a well-studied system of technological relevance. Materials science ; Grain boundary ; Density functional theory ; Work (physics) ; Relevance (law) ; Ab initio ; Boundary (topology) ; Process (computing) ; Set (abstract data type) ; Alloy ; Chemical physics ; Thermodynamics ; Computational chemistry ; Metallurgy ; Computer science ; Microstructure ; Mathematical analysis ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hyuna Kwon | Wenyu Sun | Tim Hsu | Wonseok Jeong | Fikret Aydin | Shubham Sharma | Fanchen Meng | Matthew R. Carbone | Xiao Chen | Deyu Lu | Liwen F. Wan | Michael H. Nielsen | Tuan Anh Pham
Source: The Journal of Physical Chemistry C (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparison between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. We also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp2:sp3 ratio. XANES ; Interpretability ; Amorphous solid ; Bond length ; Absorption (acoustics) ; Spectral line ; Materials science ; Representation (politics) ; Chemical physics ; Biological system ; Computer science ; Chemistry ; Crystallography ; Artificial intelligence ; Physics ; Crystal structure Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Hongzhen Wang | Qin Du | Yalin Liu | Shijie Cheng
Source: Cellulose (article, 2023-05-26)
Field: Materials Science — Biomaterials

Prediction and analysis of preparation of cellulose nanocrystals with machine learning Crystallinity ; Cellulose ; Random forest ; Decision tree ; Machine learning ; Yield (engineering) ; Artificial neural network ; Artificial intelligence ; Materials science ; Computer science ; Biological system ; Chemical engineering ; Composite material ; Engineering Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Nanocomposite Films for Food Packaging
Authors: Mahmoud Shaban | A. I. Alateyah | Mohammed F. Alsharekh | Majed O. Alawad | Amal BaQais | Mokhtar Kamel | Fahad N. Alsunaydih | Waleed H. El-Garaihy | Hanadi G. Salem
Source: Journal of Manufacturing and Materials Processing (article, 2023-02-22)
Field: Materials Science — Biomaterials

Influence of ECAP Parameters on the Structural, Electrochemical and Mechanical Behavior of ZK30: A Combination of Experimental and Machine Learning Approaches Several physics-based models have been utilized in material design for the simulation and prediction of material properties. In this study, several machine-learning (ML) approaches were used to construct a prediction model to analyze the influence of equal-channel angular pressing (ECAP) parameters on the microstructural, corrosion and mechanical behavior of the biodegradable magnesium alloy ZK30. The ML approaches employed were linear regression, the Gaussian process, and support vector regression. For the optimization of the alloy’s performance, experiments were conducted on ZK30 billets using different ECAP routes, channel angles, and number of passes. The adopted ML model is an adequate predictive model which agreed with the experimental results. ECAP die angles had an insignificant effect on grain refinement, compared to the route type. ECAP via four passes of route Bc (rotating the sample 90° on its longitudinal axis after each pass in the same direction) was the most effective condition producing homogenous ultrafine grain distribution of 1.92 µm. Processing via 4-Bc and 90° die angle produced the highest hardness (97-HV) coupled with the highest tensile strength (344 MPa). The optimum corrosion rate of 0.140 mils penetration per year (mpy) and the optimum corrosion resistance of 1101 Ω·cm2 resulted from processing through 1-pass using the 120°-die. Grain refinement resulted in reducing the corrosion rates and increased corrosion resistance, which agreed with the ML findings. Materials science ; Ultimate tensile strength ; Corrosion ; Magnesium alloy ; Die (integrated circuit) ; Metallurgy ; Alloy ; Pressing ; Grain size ; Composite material Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Microstructure and mechanical properties
Authors: Niki Pouyanfar | Mahnaz Ahmadi | Seyed Mohammad Ayyoubzadeh | Fatemeh Ghorbani-Bidkorpeh
Source: Materials Today Communications (article, 2023-12-23)
Field: Chemistry — Inorganic Chemistry

Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach Materials science ; Drug delivery ; Metal-organic framework ; Biocompatibility ; Nanotechnology ; Biochemical engineering ; Computer science ; Machine learning ; Systems engineering ; Process engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Muammer Y. Yaman | Sergei V. Kalinin | Kathryn N. Guye | David S. Ginger | Maxim Ziatdinov
Source: Small (article, 2023-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems. Plasmon ; Plasmonic nanoparticles ; Photonics ; Autoencoder ; Hyperspectral imaging ; Nanoparticle ; Nanophotonics ; Materials science ; Range (aeronautics) ; Dual (grammatical number) ; Nanotechnology ; Computer science ; Physics ; Artificial neural network ; Artificial intelligence ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Fluorescence Microscopy Techniques ; Plasmonic and Surface Plasmon Research
Authors: Roberta Motta Neves | Heitor Luiz Ornaghi | Fillip Cortat Alves | Ademir J. Zattera | Milanta Tom | Hiran Mayookh Lal | Arya Uthaman | Sabu Thomas
Source: Cellulose (article, 2023-01-11)
Field: Materials Science — Polymers and Plastics

Creep and stress relaxation behavior of functionalized microcrystalline cellulose/epoxy composites Materials science ; Microcrystalline cellulose ; Composite material ; Creep ; Epoxy ; Silane ; Stress relaxation ; Polymer ; Cellulose ; Relaxation (psychology) ; Stress (linguistics) ; Chemical engineering Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes ; High voltage insulation and dielectric phenomena
Authors: Jingtao Huang | Jingteng Xue | Mingwei Li | Jiaying Chen | Yuan Cheng | Zhonghong Lai | Jin Hu | Fei Zhou | Nan Qu | Yong Liu | Jingchuan Zhu
Source: Applied Surface Science (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Adsorption and modification behavior of single atoms on the surface of single vacancy graphene: Machine learning accelerated first principle computations Graphene ; Vacancy defect ; Adsorption ; Materials science ; Computation ; Atom (system on chip) ; Node (physics) ; Computer science ; Machine learning ; Nanotechnology ; Chemical physics ; Artificial intelligence ; Algorithm ; Chemistry ; Physical chemistry ; Physics ; Condensed matter physics ; Quantum mechanics ; Parallel computing Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Chen Niu | Mario Phaneuf | Puyan Mojabi
Source: IEEE Open Journal of Antennas and Propagation (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Diffusion Model for Multi-Layered Metasurface Unit Cell Synthesis A deep learning approach based on a diffusion model is proposed to yield metasurface unit cell designs. This method takes desired two-port scattering parameters along with the frequency of operation in an attempt to synthesize three-layered metasurface unit cells. The core of this approach lies in casting the three-layered unit cell synthesis process as conditional three-channel binary image synthesis. The conditions are governed by the desired scattering parameters at a given frequency whereas the binary nature implies the presence and absence of metallic traces. Once synthesized, these unit cells are placed beside each other, without any further tuning, to form the final metasurface. The performance of the whole metasurface, for three different design scenarios, is then tested against full-wave simulation and/or experimental data. Binary number ; Scattering ; Unit (ring theory) ; Yield (engineering) ; Computer science ; Process (computing) ; Dimension (graph theory) ; Diffusion ; Optics ; Electronic engineering ; Materials science ; Physics ; Mathematics ; Engineering ; Arithmetic Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Energy Harvesting in Wireless Networks
Authors: Jia-Hong Ke
Source: Computational Materials Science (article, 2023-09-23)
Field: Materials Science — Materials Chemistry

Microstructure modeling of nuclear structural materials: Recent progress and future directions Multiscale modeling ; Statistical physics ; Kinetic Monte Carlo ; Ab initio ; Leverage (statistics) ; Embrittlement ; Monte Carlo method ; Computer science ; Materials science ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Nuclear physics ; Mathematics Nuclear Materials and Properties ; Fusion materials and technologies ; High Temperature Alloys and Creep
Authors: Nicolas Bertin | Fei Zhou
Source: Journal of Computational Physics (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Accelerating discrete dislocation dynamics simulations with graph neural networks Dislocation ; Bottleneck ; Computer science ; Algorithm ; Artificial neural network ; Range (aeronautics) ; Statistical physics ; Graph ; Physics ; Artificial intelligence ; Materials science ; Theoretical computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Fuel Cells and Related Materials
Authors: Ali Ahmed Salem | Ahmed Hussien Radie Alawady | Kwan Yiew Lau | Wan Rahiman | Zulkurnain Abdul‐Malek | Samir A. Al-Gailani | Salem Mgammal Al-Ameri | Enas Ali | Sherif S. M. Ghoneim
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Investigating and Modeling Ageing Effects on Polymeric Insulator Electrical Properties Polymeric insulators, in particular silicone rubber (SIR) are lightweight, have good hydrophobicity characteristics, and are easy to carry and install. They are commonly used for outdoor insulation in power lines. However, pollution, UV radiation, temperature, discharge, wetness, and stress can cause them to degrade over time, losing their electrical properties. Therefore, evaluating the ageing and degradation of polymeric insulators under different conditions becomes crucial. This paper investigates the ageing effects of the polymeric insulators with differences in pollution, applied voltage, hydrophobicity class, and geometrical structures of insulators. The investigation includes the experimental tests of the insulators’ electrical properties such as leakage current and flashover voltage, after assessing the initial characteristics of insulators based on their age and supply voltage. In addition, the aged polymeric insulator model based on an equivalent circuit model was developed to determine the leakage current and breakdown voltage of aged insulators. Moreover, an artificial neural network model is carried out to predict the critical leakage current and flashover voltage of the insulator under the ageing effect. The experiment results were used to validate the accuracy of the proposed models; with an aggregate error of less than 10%, the proposed models appeared to be satisfactory. These models can serve as a scholarly resource for designing, operating, and maintaining insulators, especially in polluted environments. Insulator (electricity) ; Silicone rubber ; Arc flash ; Materials science ; Voltage ; Leakage (economics) ; Composite material ; High voltage ; Forensic engineering ; Environmental science ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Siwei Song | Yi Wang | Xiaolan Tian | Wei He | Fang Chen | Junnan Wu | Qinghua Zhang
Source: The Journal of Physical Chemistry A (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model Melting point prediction for organic molecules has drawn widespread attention from both academic and industrial communities. In this work, a learnable graph neural fingerprint (GNF) was employed to develop a melting point prediction model using a dataset of over 90,000 organic molecules. The GNF model exhibited a significant advantage, with a mean absolute error (MAE) of 25.0 K, when compared to other featurization methods. Furthermore, by integrating prior knowledge through a customized descriptor set (i.e., CDS) into GNF, the accuracy of the resulting model, GNF_CDS, improved to 24.7 K, surpassing the performance of previously reported models for a wide range of structurally diverse organic compounds. Moreover, the generalizability of the GNF_CDS model was significantly improved with a decreased MAE of 17 K for an independent dataset containing melt-castable energetic molecules. This work clearly demonstrates that prior knowledge is still beneficial for modeling molecular properties despite the powerful learning capability of graph neural networks, especially in specific fields where chemical data are lacking. Generalizability theory ; Artificial neural network ; Molecule ; Graph ; Melting point ; Training set ; Fingerprint (computing) ; Organic molecules ; Biological system ; Computer science ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Chemistry ; Statistics ; Theoretical computer science ; Organic chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics