jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Manoj Dhivakar | R. Sarathi | Stefan Kornhuber
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Investigation on Electrical, Thermal, and Mechanical Properties of Silicone Rubber ATH Nanocomposites In the present study, investigations have been carried out by choosing the nano-sized ATH (30 – 50 nm) particles as filler content in silicone rubber to acquire a deep insight to understand the use of nano ATH fillers in silicone rubber on its performance, to use it as insulation structure as an outdoor insulator. Variation in the surface condition of silicone rubber nanocomposites due to corona aging and the recovery characteristics analysis carried out through contact angle measurement. Nano ATH added in silicone rubber, up to 3 wt% of filler, shows increased dielectric constant with less loss factor. Water droplet-initiated discharges on silicone rubber nano ATH composites under DC voltage measured using UHF technique and the damage caused zone analyzed through AFM studies and by surface potential measurement, which indicates higher surface roughness and increased charge trap depth. It is observed that negative DC voltage has higher CIV followed by positive DC and AC voltage. Laser Induced breakdown studies (LIBS) show a direct correlation between plasma temperature and material surface hardness. Also, on laser shining on the surface of the specimen, its temperature was measured on the backside of the specimen through thermal imaging confirming increased diffusivity of temperature. Laser flash analysis shows a direct correlation between filler content in base polymer and thermal conductivity and diffusivity. Higher storage modulus and activation energies measured from the dynamic mechanical analysis indicate the significance of prepared composites as an excellent mechanical support structure for the power system network. Materials science ; Silicone rubber ; Composite material ; Surface roughness ; Nanocomposite ; Dynamic mechanical analysis ; Thermal diffusivity ; Dielectric ; Natural rubber ; Dielectric loss ; Polymer High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Fiber Optic Sensors
Authors: Chun-Te Wu | Po‐Hsun Lin | Sih-Ying Huang | Yu-Jen Tseng | Hsiao-Tzu Chang | Sheng-Yen Li | Hung‐Wei Yen
Source: Materialia (article, 2022-01-02)
Field: Materials Science — Materials Chemistry

Revisiting alloy design of low-modulus biomedical β-Ti alloys using an artificial neural network An ensemble neural networks model is developed to detangle the complex effects of alloying elements on phase stability and Young's modulus, enabling design guidelines for low-modulus Ti alloy. This computational framework, which is named βLow 2.0, is validated and examined to understand the sensitivity of the modulus to the alloy composition for material selections. The exercise provides design guidelines for future material designs considering metastability of β phase and effects of Nb, Zr, Mo, Sn, and Ta. To evaluate this data-driven model, a basic uncertainty quantification function is applied to understand the model in the present data space. The results of the model validation are also presented with experimental data different from that of the calibration dataset. This work enables comprehensive metallurgical principles for alloy design by using neural network-based machine learning. Materials science ; Artificial neural network ; Alloy ; Modulus ; Experimental data ; Work (physics) ; Metastability ; Sensitivity (control systems) ; Stability (learning theory) ; Calibration ; Function (biology) ; Elastic modulus ; Mechanical engineering ; Machine learning ; Computer science ; Metallurgy ; Composite material ; Engineering ; Statistics Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Haobo Li | Yan Jiao | Kenneth Davey | Shi‐Zhang Qiao
Source: Angewandte Chemie (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts Abstract The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active‐site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in‐situ reactions. We propose therefore data‐driven machine‐learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine‐learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro‐environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts. Adsorption ; Catalysis ; Computer science ; Nanotechnology ; Surface (topology) ; Norm (philosophy) ; Heterogeneous catalysis ; Biochemical engineering ; Materials science ; Chemistry ; Engineering ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ferroelectric and Negative Capacitance Devices
Authors: Zixuan Huang | Wang Quan-jie | Xinyu Liu | Xiangjun Liu
Source: Physical Chemistry Chemical Physics (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

First-principles based deep neural network force field for molecular dynamics simulation of N–Ga–Al semiconductors Accurate interatomic force fields are of paramount importance for molecular dynamics simulations to explore the thermal transport at the GaN/AlN heterogenous interface, which is a key factor hindering heat dissipation and limiting the performance of GaN power electronic devices. In this work, an interatomic potential (force field) based on a deep neural network technique and first-principles calculations is developed for N-Ga-Al semiconductors to predict the elastic and thermodynamic properties. Using our deep neural network potential (NNP), the precise structural features, elastic constants, and thermal conductivities of GaN, AlN, and their alloy are obtained, which are well consistent with those from experiments and first-principles calculations. The interfacial thermal conductance of GaN/AlN heterostructures with different interfacial morphologies are further studied using molecular dynamics simulations with the NNP. It is found that atomic interdiffusion and disorder at the interfaces dramatically reduces the interfacial thermal conductance. The developed NNP exhibits a larger effective dimension with respect to classical empirical potentials and reaches competitive performances, thus pointing towards attractive advantages in the study of GaN heterostructures and devices with the NNP. Molecular dynamics ; Heterojunction ; Materials science ; Thermal conductivity ; Semiconductor ; Work (physics) ; Condensed matter physics ; Interatomic potential ; Thermal ; Field (mathematics) ; Chemical physics ; Optoelectronics ; Thermodynamics ; Chemistry ; Physics ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; GaN-based semiconductor devices and materials
Authors: Shinya Mine | Yuan Jing | Takumi Mukaiyama | Motoshi Takao | Zen Maeno | Ken‐ichi Shimizu | Ichigaku Takigawa | Takashi Toyao
Source: Chemistry Letters (article, 2022-01-20)
Field: Materials Science — Materials Chemistry

Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts Abstract Literature data based on the water gas shift (WGS) reaction have been analyzed using statistical methods based on machine learning (ML). Our ML approach, which considers elemental features as input representations rather than the catalyst compositions, was successfully applied, and new promising catalyst candidates for future research were proposed. Water-gas shift reaction ; Chemistry ; Catalysis ; Statistical learning ; Statistical analysis ; Artificial intelligence ; Computer science ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Tiankai Chen | Jiali Li | Pengfei Cai | Qiaofeng Yao | Zekun Ren | Yixin Zhu | Saif A. Khan | Jianping Xie | Xiaonan Wang
Source: Nano Research (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations Rapid and accurate chemical composition identification is critically important in chemistry. While it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple, such application is limited in more complicated scenarios especially in nano-scale research. This is due to the difficulties in identifying optical absorption peaks (i.e., from “featureless” spectra) arose from the complexity. In this work, using the ultraviolet—visible (UV—Vis) absorption spectra of metal nanoclusters (NCs) as a demonstration, we develop a machine-learning-based method to unravel the compositions of metal NCs behind the “featureless” spectra. By implementing a one-dimensional convolutional neural network, good matches between prediction results and experimental results and low mean absolute error values are achieved on these optical absorption spectra that human cannot interpret. This work opens a door for the identification of nanomaterials at molecular precision from their optical properties, paving the way to rapid and high-throughput characterizations. Nanoclusters ; Absorption (acoustics) ; Absorption spectroscopy ; Spectral line ; Convolutional neural network ; Identification (biology) ; Ultraviolet ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Computer science ; Optics ; Machine learning ; Optoelectronics ; Physics ; Chromatography Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Bojun Liu | Yunrui Qiu | Eshani C. Goonetilleke | Xuhui Huang
Source: MRS Bulletin (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Kinetic network models to study molecular self-assembly in the wake of machine learning Computer science ; Self-assembly ; Metastability ; Path (computing) ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies
Authors: Tao Chen | Yuan Yuan | Xiaoxi Mi | Jiajia Wu | Aitao Tang | Jingfeng Wang | Nele Moelans | Fusheng Pan
Source: Journal of Materials Research and Technology (article, 2022-11-01)
Field: Materials Science — Biomaterials

Interaction of elements in dilute Mg alloys: a DFT and machine learning study The solution behavior of alloying elements is crucial for the design of Mg alloys. The plasticity, strength and corrosion resistance of Mg alloys can be tailored by alloying elements in solid solution in the primary Mg phase. In this work, the interaction and solution behavior of 24 alloying elements in Mg alloys, including 24 binary systems and 88 ternary systems, was studied. Features characterizing the interactions of solute elements in the primary Mg phase, including atomic substitutional sites, volume difference, c/a ratio and formation energy, of these alloys were calculated by first principles. Relations between the interaction features and the alloying elements’ characteristics were constructed based on machine learning methods, in which strong correlations were observed. Additionally, the electronic and atomic origin of the interaction features in solid solution ternary Mg alloys were analyzed. This work provides new insights into the design of high-performance multicomponent Mg alloys, as well as the construction of atomic models for other alloys. Ternary operation ; Materials science ; Atomic radius ; Phase (matter) ; Solid solution ; Work (physics) ; Thermodynamics ; Binary number ; Interaction energy ; Metallurgy ; Chemical physics ; Computer science Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Metal and Thin Film Mechanics
Authors: Jongmin Han | Hyungu Kang | Seokho Kang | Youngchun Kwon | Dongseon Lee | Youn-Suk Choi
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Scalable graph neural network for NMR chemical shift prediction Graph neural networks (GNNs) have been proven effective in the fast and accurate prediction of nuclear magnetic resonance (NMR) chemical shifts of a molecule. Existing methods, despite their effectiveness, suffer from high space complexity and are therefore limited to relatively small molecules. In this work, we propose a scalable GNN for NMR chemical shift prediction. To reduce the space complexity, we sparsify the graph representation of a molecule by regarding only heavy atoms as nodes and their chemical bonds as edges. To better learn from the sparsified graph representation, we improve the message passing and readout functions in the GNN. For the message passing function, we adapt the attention mechanism and residual connection to better capture local information around each node. For the readout function, we use both node-level and graph-level embeddings as the local and global information to better predict node-level chemical shifts. Through the experimental investigation using <sup>13</sup>C and <sup>1</sup>H NMR datasets, we demonstrate that the proposed method yields higher prediction accuracy and is more scalable to large molecules having many heavy atoms. Scalability ; Chemical shift ; Chemical space ; Computer science ; Graph ; Node (physics) ; Message passing ; Representation (politics) ; Molecular graph ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Drug discovery ; Physics ; Distributed computing Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Alexander E. Mayer | Vasiliy S. Krasnikov | Victor V. Pogorelko
Source: Computational Materials Science (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Homogeneous nucleation of dislocations in copper: Theory and approximate description based on molecular dynamics and artificial neural networks Nucleation ; Materials science ; Dislocation ; Extrapolation ; Statistical physics ; Molecular dynamics ; Artificial neural network ; Condensed matter physics ; Thermodynamics ; Mathematics ; Computer science ; Mathematical analysis ; Physics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Composite material Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yongdi Huang | Qionghai Chen | Zhiyu Zhang | Ke Gao | Anwen Hu | Yining Dong | Jun Liu | Lihong Cui
Source: Polymers (article, 2022-05-06)
Field: Materials Science — Materials Chemistry

A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with MD data is developed to predict not only the tensile stress of NR but also other mechanical behaviors. We propose a novel idea based on feature processing by combining our previous experience in performing predictions of small samples. The proposed ML method consists of (i) an extreme gradient boosting (XGB) model to predict the tensile stress of NR, and (ii) a data augmentation algorithm based on nearest-neighbor interpolation (NNI) and the synthetic minority oversampling technique (SMOTE) to maximize the use of limited training data. Among the data enhancement algorithms that we design, the NNI algorithm finally achieves the effect of approaching the original data sample distribution by interpolating at the neighborhood of the original sample, and the SMOTE algorithm is used to solve the problem of sample imbalance by interpolating at the clustering boundaries of minority samples. The augmented samples are used to establish the XGB prediction model. Finally, the robustness of the proposed models and their predictive ability are guaranteed by high performance values, which indicate that the obtained regression models have good internal and external predictive capacities. Cluster analysis ; Computer science ; Robustness (evolution) ; Oversampling ; Artificial intelligence ; Interpolation (computer graphics) ; Machine learning ; Algorithm ; Support vector machine ; Molecular dynamics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Software Engineering Research
Authors: Stefan Heinen | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2022-10-24)
Field: Materials Science — Materials Chemistry

Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers but without the need for subsequent re-optimization with quantum chemistry methods. For randomly sampled initial coordinates of small organic query molecules, we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample S<sub>N</sub>2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithms with an root-mean-square deviation (RMSD) of 0.16 and 0.4 Å-after training on up to 200 reactant complex relaxations and transition state search trajectories from the QMrxn20 dataset, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'595 constitutional isomers with sum formula C<sub>7</sub>H<sub>10</sub>O<sub>2</sub> from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14 Å, only using ∼6000 training points obtained from normal mode sampling along the optimization paths of the training compounds without the need for active learning. For converged equilibrium and transition state geometries, subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26 cm<sup>-1</sup>, respectively. While the numerical cost for OQML predictions is negligible in comparison to density functional theory or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability. Relaxation (psychology) ; Geometry ; Density functional theory ; Quantum ; Mathematics ; Statistical physics ; Algorithm ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Igor I. Baskin | Yair Ein‐Eli
Source: Advanced Energy Materials (article, 2022-11-07)
Field: Materials Science — Materials Chemistry

Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology Abstract Electrochemical processes underlie the functioning of electrochemical devices for energy storage and conversion. In this paper, electrochemoinformatics is defined as a scientific discipline, a part of computational electrochemistry, dealing with the application of information technologies, specifically data science, machine learning (ML), and artificial intelligence, to solve problems related to electrochemical processes, including the development of materials and electrochemical devices. Electrochemoinformatics is closely related to chemoinformatics and materials informatics and relies on the technologies they use. The definitions and the basics of chemoinformatics and materials informatics are reviewed with an emphasis on the role of data science, data representation, the use of ML methods, and molecular and material descriptors. The third constituent part of electrochemoinformatics is full‐cell informatics, which deals with the performance and design of whole electrochemical devices. The article focuses on the use of electrochemoinformatics in battery science and technology (battery informatics), including the development of electrolyte and electrode materials, battery performance monitoring, modeling, and design. The scope and the main challenges facing electrochemoinformatics are discussed. Cheminformatics ; Materials informatics ; Scope (computer science) ; Electrochemical energy storage ; Computer science ; Battery (electricity) ; Informatics ; Electrochemical energy conversion ; Data science ; Engineering informatics ; Field (mathematics) ; Nanotechnology ; Systems engineering ; Health informatics ; Electrochemistry ; Materials science ; Electrical engineering ; Engineering ; Electrode ; Chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Lian Li | Zhi Min Li | Yuanzhong Wang
Source: Plant Methods (article, 2022-08-13)
Field: Chemistry — Analytical Chemistry

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus. Eucommia ulmoides ; Chemometrics ; Mathematics ; Computer science ; Pattern recognition (psychology) ; Residual ; Artificial intelligence ; Biological system ; Environmental science ; Machine learning ; Biology ; Algorithm Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemistry and Biological Activities