jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y. Shiozaki | Hyeon‐Gu Jeon | Haruo Ihori
Source: IEEJ Transactions on Fundamentals and Materials (article, 2023-07-31)
Field: Materials Science — Materials Chemistry

Recognition and Classification of Electrical Treeing Images using Machine Learning The electrical tree is degradation traces of insulating materials. The mechanism of this electrical tree progress is not yet understood. Therefore, we wondered if machine learning could be used to elucidate the progress mechanism. First, we investigated the applicability of machine learning to electrical tree. Mechanism (biology) ; Tree (set theory) ; Computer science ; Artificial intelligence ; Mathematics ; Epistemology ; Philosophy High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Zhang-Zhang Yang | Li Liu | Zhitao Wan | Jia Fu | Qunchao Fan | Feng Xie | Yi Zhang | Jie Ma
Source: Acta Physica Sinica (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Combining machine learning algorithm to improve prediction performance of ab initio method for vibrational energy spectra of HF/HBr/H&lt;sup&gt;35&lt;/sup&gt;Cl/Na&lt;sup&gt;35&lt;/sup&gt;Cl Halides play an important role in atmospheric chemistry, corrosion of steel, and also in controlling the abundance of O&lt;sub&gt;3&lt;/sub&gt;. Moreover high-precision vibrational energy spectra contain a large amount of quantum information of molecular system and are basic data for people to understand and manipulate molecules. At present, ab-initio methods have achieved many calculation results of the potential energy surfaces and corresponding vibrational energy of molecules, but they still face challenges in terms of accuracy and computational cost. Recently, data-driven machine learning methods have demonstrated very strong capability of extracting high-dimensional functional relationships from massive data and have been widely used in spectrum studies. ​Therefore, a theoretical approach to combining ab-initio method and machine learning algorithm is presented here to predict the vibrational energy of diatomic systems, which improves the accuracy and simultaneously reduces the computational cost. Firstly, the vibrational energy levels of 42 diatomic molecules are obtained by using different CCSD(T) methods to calculate the configurations from simple to complex and the corresponding experimental results are also collected. ​A machine learning algorithm is then used to learn the difference between the CCSD(T) method calculated vibrational results and the experimental vibrational results, and a high-dimensional error function is finally constructed to improve the original CCSD(T) computational accuracy. The results for HF, HBr, H&lt;sup&gt;35&lt;/sup&gt;Cl and Na&lt;sup&gt;35&lt;/sup&gt;Cl (they did not appear in the training set) and other halogen molecules show that compared with the CCSD(T)/cc-pV5Z calculation method alone, the present method reduces the prediction error by more than 50% and the computational cost by nearly one order of magnitude. It is worth noting that the method proposed in this paper is not only limited to the energy level prediction of diatomic systems, but also applicable in other fields where data can be obtained by ab initio methods and experimental methods simultaneously, such as the energy spectrum properties of macromolecular systems. Diatomic molecule ; Ab initio ; Energy (signal processing) ; Ab initio quantum chemistry methods ; Physics ; Spectral line ; Molecule ; Atomic physics ; Algorithm ; Computational chemistry ; Chemistry ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Inorganic Fluorides and Related Compounds
Authors: Shuaizhen Li | Tianwen Xie | Lin Ma | Daheng Liu | Nan Huang | Wei Liu | Song Ma | Zhidong Zhang
Source: Research Square (preprint, 2023-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchical bionic neural network-like architecture towards high- performance microwave absorption and corrosion protection Abstract It remains challenging to develop multifunctional electromagnetic wave (EMW) absorbers to enhance the environmental adaptability and survivability of target devices in harsh environment. Herein, this study innovatively constructs a three-dimensional (3D) hierarchical bionic neural network-like Ni 3 Fe@N-doped carbon nanotube@Carbon foam (Ni 3 Fe@NCNT@CF) composite via dip-coating and carbonization operation, realizing the integration of microwave absorption and corrosion protection. The optimal Ni 3 Fe@NCNT@CF composite demonstrates competitive EMW absorption with a minimum reflection loss of −51.5 dB and a maximum effective absorption bandwidth (EAB) of 6.4 GHz at 10 wt%, due to superior interlinked conductive channels (with flexible and adjustable conduction loss) and magnetic-dielectric matching of the 3D hierarchical bionic neural network, including 3D CF skeleton (matrical neural network), short CNTs network (enrich “dendrites”), and ultra-long CNTs network (enhance “axons”). Besides, Ni 3 Fe@NCNT@CF composite exhibits a reliable anti-corrosion performance with a low corrosion current density ~10 -8 A·cm -2 after 30 days of immersion in corrosive medium, owing to the physical shield provided by the impermeable, inert CNT shells and carbon foam. Our findings may provide design inspiration of versatile EMW materials for applications in complicated environments. Carbon nanotube ; Materials science ; Reflection loss ; Corrosion ; Microwave ; Composite number ; Absorption (acoustics) ; Composite material ; Nanotechnology ; Carbonization ; Scanning electron microscope ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Sonia Saxena | Jan-Hendrik Bastek | Miguel Spínola | Prateek Gupta | Dennis M. Kochmann
Source: arXiv (Cornell University) (preprint, 2023-03-20)
Field: Materials Science — Materials Chemistry

GNN-Assisted Phase Space Integration with Application to Atomistics Phase space ; Numerical integration ; Gaussian quadrature ; Computer science ; Statistical physics ; Gaussian ; Computation ; Quadrature (astronomy) ; Monte Carlo method ; Representation (politics) ; Thermodynamic integration ; Theoretical computer science ; Applied mathematics ; Mathematical optimization ; Algorithm ; Mathematics ; Nyström method ; Physics ; Energy (signal processing) ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Sam Shepherd | Gareth A. Tribello | David M. Wilkins
Source: arXiv (Cornell University) (preprint, 2023-01-11)
Field: Materials Science — Biomaterials

A Fully Quantum-Mechanical Treatment for Kaolinite Neural network potentials for kaolinite minerals have been fitted to data extracted from density functional theory calculation that were performed using the revPBE + D3 and revPBE + vdW functionals. These potentials have then been used to calculate static and dynamic properties of the mineral. We show that revPBE + vdW is better at reproducing the static properties. However, revPBE + D3 does a better job of reproducing the experimental IR spectrum. We also consider what happens to these properties when a fully-quantum treatment of the nuclei is employed. We find that nuclear quantum effects (NQEs) do not make a substantial difference to the static properties. However, when NQEs are included the dynamic properties of the material change substantially. Kaolinite ; Quantum ; Density functional theory ; Materials science ; Physics ; Quantum mechanics ; Metallurgy Clay minerals and soil interactions ; Geophysical and Geoelectrical Methods ; Soil and Unsaturated Flow
Authors: Lei Zhang | Markus Stricker
Source: arXiv (Cornell University) (preprint, 2023-11-07)
Field: Materials Science — Materials Chemistry

MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials Discover MatNexus is a specialized software for the automated collection, processing, and analysis of text from scientific articles. Through an integrated suite of modules, the MatNexus facilitates the retrieval of scientific articles, processes textual data for insights, generates vector representations suitable for machine learning, and offers visualization capabilities for word embeddings. With the vast volume of scientific publications, MatNexus stands out as an end-to-end tool for researchers aiming to gain insights from scientific literature in material science, making the exploration of materials, such as the electrocatalyst examples we show here, efficient and insightful. Suite ; Computer science ; Data science ; Visualization ; Information retrieval ; Software ; Scientific literature ; Software suite ; World Wide Web ; Data mining ; Programming language ; Archaeology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Shusuke Kasamatsu | Yuichi Motoyama | Kazuyoshi Yoshimi | Tatsumi Aoyama
Source: arXiv (Cornell University) (preprint, 2023-09-09)
Field: Materials Science — Materials Chemistry

Configuration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS) Simulation of the intermediate levels of disorder found in multi-component multi-sublattice systems in various functional materials is a challenging issue, even for state-of-the-art methodologies based on first-principles calculation. Here, we introduce our open-source package ab Initio Configuration Sampling Toolkit (abICS), which combines high-throughput first-principles calculations, machine learning, and parallel extended ensemble sampling in an active learning setting to enable such simulations. The theoretical background is reviewed in some detail followed by brief notes on usage of the software. In addition, our recent applications of abICS to multi-component ionic systems and their interfaces for energy applications are reviewed as demonstration of the power of this approach. Component (thermodynamics) ; Computer science ; Ab initio ; Sampling (signal processing) ; Computational science ; Software ; Chemistry ; Physics ; Programming language ; Thermodynamics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ionic liquids properties and applications
Authors: Ao Wu | Li Liu | Zifeng Wang | Shurong Pan | Jiangxue Huang | Qijun Huang | Jin He | Hao Wang | Sheng Chang
Source: Machine Learning Science and Technology (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

Graph machine learning framework for depicting wavefunction on interface Abstract The wavefunction, as the basic hypothesis of quantum mechanics, describes the motion of particles and plays a pivotal role in determining physical properties at the atomic scale. However, its conventional acquisition method, such as density functional theory, requires a considerable amount of calculation, which brings numerous problems to wide application. Here, we propose an algorithmic framework based on graph neural network to machine-learn the wavefunction of electrons. This framework primarily generates atomic features containing information about chemical environment and geometric structure and subsequently constructs a scalable distribution map. For the first time, the visualization of wavefunction of interface is realized by machine learning methods, bypassing complex calculation and obscure comprehension. In this way, we vividly illustrate quantum mechanics, which can inspire theoretical exploration. As an intriguing case to verify the ability of our method, a novel quantum confinement phenomenon on interfaces based on graphene nanoribbon is uncovered. We believe that the versatility of this framework paves the way for swiftly linking quantum physics and atom-level structures. Wave function ; Computer science ; Interface (matter) ; Quantum ; Graph ; Scalability ; Comprehension ; Visualization ; Theoretical computer science ; Artificial intelligence ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Graphene research and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Ling Liu | Shaofang Zhang | Xinzhu Chen | Li Guo | Yonghui Li | Xiaodong Zhang
Source: arXiv (Cornell University) (preprint, 2023-05-13)
Field: Materials Science — Materials Chemistry

Spin Cooperated Catalytic Activities in Mn-N4 based Single-atom Nanozyme: Mechanisms and a Brief Charge-spin Model Although developing artificial enzymes has made great progress, there is still a gap between artificial enzymes and natural enzymes in catalytic performance. Designing and constructing efficient artificial biocatalysts is extremely desirable because of their high stability, low cost and easy storage. Here, we report a synthesized amino-functionalized graphene quantum dots-based manganese single atom catalyst (SAC) Mn-N4, which exhibits POD-, CAT, SOD-like activities, especially the superior SOD-like activity. Recent studies have reported Mn-based SAzymes, however, the multi-enzyme mimicking catalytic mechanisms for Mn-N4 are not comprehensive and in-depth enough. Therefore, we combine density functional theory (DFT) calculations and machine learning (ML) to validate the performance of the multi-enzyme mimicking activities. The DFT simulations show that Mn-N4 owns a highly effective SOD in the "one-side adsorption" with a very low energy barrier of 0.077 eV, which can be attributed to variation of the preferred spin states of Mn-O2.- system and its "spin flip-collection lock" in the SOD-like catalytic procedure. Furthermore, spin related charge distributions on Mn-N4 configurations by machine learning (ML) analysis suggest that the pattern of spin and natural charge/valence electron distribution will exhibit similarity in the structures of multiple intermediate steps of multi-enzyme mimicking activities. This work not only puts forward the catalytic mechanisms of Mn-N4 SAzymes, but also provides essential guidance for future design of highly performance artificial enzymes. Manganese ; Valence (chemistry) ; Catalysis ; Graphene ; Density functional theory ; Spin (aerodynamics) ; Artificial enzyme ; Atom (system on chip) ; Enzyme ; Chemistry ; Materials science ; Chemical physics ; Nanotechnology ; Computational chemistry ; Physics ; Computer science ; Organic chemistry Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: Xinshui Lei | Jiangen Wang | Teng Wang | Xinjie Wang | Xiaowen Xie | Haocheng Huang | Didi Li | Zhimin Ao
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Toluene Decomposition by Non-Thermal Plasma Assisted 2d Coox/Al2o3: The Relative Contributions of Specific Energy Density of Plasma, Co 3+ and Oxygen Vacancy Plasma ; Decomposition ; Toluene ; Oxygen ; Vacancy defect ; Materials science ; Thermal ; Nonthermal plasma ; Thermal decomposition ; Energy (signal processing) ; Atomic physics ; Chemistry ; Thermodynamics ; Physics ; Condensed matter physics ; Nuclear physics ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science
Authors: Yerin Jang | Choong H. Kim | Ara Go
Source: Research Square (preprint, 2023-03-15)
Field: Materials Science — Materials Chemistry

Classification of magnetic order from electronic structure by using machine learning Abstract Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO 3 . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning. Excitation ; Antiferromagnetism ; Artificial intelligence ; Machine learning ; Hamiltonian (control theory) ; Physics ; Magnetism ; Computer science ; Statistical physics ; Condensed matter physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Tuan Anh Pham | Hyuna Kwon | Tim Hsu | Wenyu Sun | Wonseok Jeong | Fikret Aydin | James Chapman | Xiao Chen | Matthew R. Carbone | Deyu Lu | Fei Zhou
Source: Research Square (preprint, 2023-12-28)
Field: Materials Science — Materials Chemistry

Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models <title>Abstract</title> The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of amorphous carbons (a-C) as a representative material system from the target X-ray absorption near edge structure (XANES) spectra--a common experimental technique to probe atomic structures of materials. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e., with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted. XANES ; Generative grammar ; Materials science ; Characterization (materials science) ; Atomic units ; Amorphous solid ; Computer science ; Spectroscopy ; Nanotechnology ; Physics ; Artificial intelligence ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Alessandro Stagni | Ludovico Musenich | Flavia Libonati | Lucio Derin
Source:  (preprint, 2023-06-14)
Field: Materials Science — Biomaterials

Tunable energy absorption in 3D-printed data-driven diatom-inspired architected materials Boosted by additive manufacturing, architected materials have opened new opportunities to extend the performance of engineering materials. Yet, their development is held back by the intense efforts required to understand their complex property-structure-process-performance relationship. Therefore, data-driven biomimetic approaches are becoming increasingly popular to unveil such relationship. Here we mimic the functionally graded structures found in Coscinodiscus sp. diatom to understand the role of their shapes and define new guidelines for the design of novel architected honeycombs with tunable mechanical properties. Finite element simulations, validated on the outcome of a testing campaign performed on 3D-printed elastomeric samples, are used to build a dataset for machine learning algorithm training. Different machine learning techniques are used to link the geometric features of the designed biomimetic structures to their energy absorption properties and, in particular, to the specific absorbed energy divided by the peak force, here used as the performance index. The proposed approach leads to a novel design, which features a performance increase of 250% w.r.t. conventional honeycombs. Absorption (acoustics) ; Finite element method ; Process (computing) ; Materials science ; Computer science ; Diatom ; Property (philosophy) ; Mechanical engineering ; Energy (signal processing) ; Elastomer ; 3d printed ; Engineering design process ; Artificial intelligence ; Nanotechnology ; Composite material ; Structural engineering ; Engineering ; Manufacturing engineering ; Geology ; Mathematics Diatoms and Algae Research ; Cephalopods and Marine Biology ; Paleontology and Stratigraphy of Fossils
Authors: Flaviu Cipcigan | Jonathan Booth | Rodrigo Neumann Barros Ferreira | Carine Ribeiro dos Santos | M. Steiner
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Chemistry — Inorganic Chemistry

Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. By using GFlowNets, we generate porous reticular materials, such as metal organic frameworks and covalent organic frameworks, for applications in carbon dioxide capture. We introduce a new Python package (matgfn) to train and sample GFlowNets. We use matgfn to generate the matgfn-rm dataset of novel and diverse reticular materials with gravimetric surface area above 5000 m$^2$/g. We calculate single- and two-component gas adsorption isotherms for the top-100 candidates in matgfn-rm. These candidates are novel compared to the state-of-art ARC-MOF dataset and rank in the 90th percentile in terms of working capacity compared to the CoRE2019 dataset. We discover 15 materials outperforming all materials in CoRE2019. Python (programming language) ; Computer science ; Reticular connective tissue ; Carbon dioxide ; Gravimetric analysis ; Metal-organic framework ; Materials science ; Adsorption ; Nanotechnology ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalytic Processes in Materials Science
Authors: Xinyu Zhang | Ao Wang | Cheng Shao | Hua Bao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Understanding Thermal Transport in Magnesium Solid Solutions Through First-Principles Approaches and Machine Learning Feature Screening Feature (linguistics) ; Magnesium ; Thermal ; Computer science ; Materials science ; Machine learning ; Artificial intelligence ; Physics ; Metallurgy ; Thermodynamics Machine Learning in Materials Science ; Thermodynamic and Structural Properties of Metals and Alloys ; Nuclear Materials and Properties