jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yantao Xia | Philippe Sautet
Source: ACS Nano (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Plasma Oxidation of Copper: Molecular Dynamics Study with Neural Network Potentials The formation of thin oxide films is of significant scientific and practical interest. In particular, the semiconductor industry is interested in developing a plasma atomic layer etching process to pattern copper, replacing the dual Damascene process. Using a nonthermal oxygen plasma to convert the metallic copper into copper oxide, followed by a formic acid organometallic reaction to etch the copper oxide, this process has shown great promise. However, the current process is not optimal because the plasma oxidation step is not self-limiting, hampering the degree of thickness control. In the present study, a neural network potential for the binary interaction between copper and oxygen is developed and validated against first-principles calculations. This potential covers the entire range of potential energy surfaces of metallic copper, copper oxides, atomic oxygen, and molecular oxygen. The usable kinetic energy ranges from 0 to 20 eV. Using this potential, the plasma oxidation of copper surfaces was studied with large-scale molecular dynamics at atomic resolution, with an accuracy approaching that of the first principle calculations. An amorphous layer of CuO is formed on Cu, with thicknesses reaching 2.5 nm. Plasma is found to create an intense local heating effect that rapidly dissipates across the thickness of the film. The range of this heating effect depends on the kinetic energy of the ions. A higher ion energy leads to a longer range, which sustains faster-than-thermal rates for longer periods of time for the oxide growth. Beyond the range of this agitation, the growth is expected to be limited to the thermally activated rate. High-frequency, repeated ion impacts result in a microannealing effect that leads to a quasicrystalline oxide beneath the amorphized layer. The crystalline layer slows down oxide growth. Growth rate is fitted to the temperature gradient due to ion-induced thermal agitations, to obtain an apparent activation energy of 1.0 eV. A strategy of lowering the substrate temperature and increasing plasma power is proposed as being favorable for more self-limited oxidation. Copper ; Oxide ; Materials science ; Chemical physics ; Copper oxide ; Plasma ; Molecular dynamics ; Amorphous solid ; Plasma etching ; Analytical Chemistry (journal) ; Nanotechnology ; Etching (microfabrication) ; Chemistry ; Layer (electronics) ; Metallurgy ; Computational chemistry ; Crystallography ZnO doping and properties ; Semiconductor materials and devices ; Copper-based nanomaterials and applications
Authors: Ingo Dierking | Jason Dominguez | James Harbon | Joshua Heaton
Source: Liquid Crystals (article, 2022-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Classification of liquid crystal textures using convolutional neural networks We investigate the application of convolutional neural networks (CNNs) to the classification of liquid crystal phases from images of their experimental textures. Three CNN classifier model types (Sequential, Inception and ResNet50) are tuned and trained on five individual phase group datasets. The complete dataset includes images of the cholesteric phase, chiral fluid smectic A and C phases and hexatic smectic I and F phases, all extracted from polarised microscopy videos of various liquid crystalline compounds. Three binary classification tasks, each including two liquid crystal phases, provide the foundational demonstration of CNN model viability. The average test set accuracies obtained are approximately (95 ± 2)%. More complex multi-phase datasets are also created and investigated, with a three-phase cholesteric, fluid smectic, and hexatic smectic set, in addition to a set containing all five individual phases. The average test set accuracies for these classification tasks are (85 ± 2)%. Liquid crystal ; Convolutional neural network ; Materials science ; Binary number ; Artificial intelligence ; Pattern recognition (psychology) ; Classifier (UML) ; Phase (matter) ; Set (abstract data type) ; Test set ; Computer science ; Mathematics ; Physics ; Optoelectronics Liquid Crystal Research Advancements ; Visual perception and processing mechanisms ; Optical Polarization and Ellipsometry
Authors: Soumya Chatterjee | Sayanjit Singha Roy | Arpan Chatterjee | Biswarup Ganguly | Subho Paul
Source: IEEE Transactions on Instrumentation and Measurement (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Recognition of Hydrophobicity Class of Polymeric Insulators Employing Residual Morphological Neural Network and Granulometry-Based Image Analysis Hydrophobicity is an important property of polymeric insulators that allow formation of discrete water droplets on the insulator surface. However, with aging or under extreme humidification, the insulator surface loses its hydrophobic property, resulting in continuous water channel formation, which may lead to dry band arcing and subsequently flashover, thereby affecting the long-term performance of the insulator. Therefore, accurate recognition of hydrophobic class (HC) is important as it provides information regarding the surface condition of the insulator. Considering the above-said facts, this article investigates the feasibility of employing residual morphological neural network (Res-Morph-NN) to accurately recognize different HCs of polymeric insulators. In addition, for better understanding of the distribution of water droplets on the insulator surface, a novel approach based on multiscale mathematical morphology, i.e., granulometry, is used in this article. Our investigations revealed significant alterations in the granulometric patterns with the change in HC of polymeric insulators. The efficacy of the proposed framework employing Res-Morph-NN has been validated on HC images obtained from the experiment as well as on large set of images obtained from an online database, which returned satisfactory results. The proposed method can be efficiently used to discriminate different hydrophobicity classes, which can be implemented for monitoring insulator surface condition in real time. Insulator (electricity) ; Materials science ; Residual ; Biological system ; Granulometry ; Artificial neural network ; Arc flash ; Composite material ; Computer science ; Artificial intelligence ; Algorithm High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Surface Modification and Superhydrophobicity
Authors: Qi Zhang | Hanwei Liang | Yangtianze Tao | Jianxin Yang | Bin Tang | Rui Li | Yun Ma | Linhong Ji | Xuan Jiang | Shuangshou Li
Source: Small Methods (article, 2022-04-22)
Field: Chemistry — Inorganic Chemistry

Size‐Controllable Eu‐MOFs through Machine Learning Technology: Application for High Sensitive Ions and Small‐Molecular Identification Metal-organic frameworks (MOFs) with the aggregation-induced emission (AIE) activities exhibit potential applications in the fields of energy and biomedical technology. However, the controllable synthesis of MOFs in the varied particle sizes not only affects their AIE activities, but also restricts their application scenarios. In this work, the varied particle sizes of Eu-MOFs are synthesized by adjusting the synthesis process parameters, and their variation rules combining the single factor analysis method with machine learning technology are studied. Based on the R<sup>2</sup> score, the gradient boosting decision tree (GBDT) regression model (0.9535) is employed to calculate the weight and correlation between different synthesis process parameters and it is shown that all these parameters have synergic effects on the particle sizes of Eu-MOFs, and the Eu-precursors concentration dominates in their synthesis process. Furthermore, it is indicated that the large size of Eu-MOFs and strong structural stability contribute to their high AIE activities. Finally, a screen-printed pattern is fabricated using the sample of "120-0.3-6," and this pattern exhibits a bright red fluorescence under the UV light. More importantly, this kind of Eu-MOFs can also be used to identify varied ions (Fe<sup>3+</sup> , F<sup>-</sup> , I<sup>-</sup> , SO<sub>4</sub><sup>2-</sup> , CO<sub>3</sub><sup>2-</sup> , and PO<sub>4</sub><sup>3-</sup> ) and citric acid. Particle size ; Ion ; Materials science ; Fluorescence ; Citric acid ; Particle (ecology) ; Gradient boosting ; Nanotechnology ; Computer science ; Biological system ; Chemistry ; Machine learning ; Physical chemistry ; Physics ; Random forest Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials
Authors: Timothy Q. Hartnett | Vaibhav Sharma | Sunidhi Garg | Radhika Barua | Prasanna V. Balachandran
Source: Acta Materialia (article, 2022-03-26)
Field: Materials Science — Materials Chemistry

Accelerated design of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:mi>M</mml:mi> <mml:mi>T</mml:mi> <mml:mi>X</mml:mi> </mml:mrow> </mml:math> alloys with targeted magnetostructural properties through interpretable machine learning Materials science ; Hysteresis ; Interpretability ; Machine learning ; Artificial intelligence ; Algorithm ; Alloy ; Computer science ; Thermodynamics ; Condensed matter physics ; Physics ; Metallurgy Machine Learning in Materials Science ; Magnetic Properties and Applications ; Magnetic and transport properties of perovskites and related materials
Authors: Junmeng Li | Jie Ren | Ruiyan Cui | Keqiang Yu | Yanru Zhao
Source: Frontiers in Plant Science (review, 2022-10-24)
Field: Chemistry — Analytical Chemistry

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health <i>via</i> the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its <i>in-situ</i>, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments. Software portability ; Spectroscopy ; Computer science ; Wilting ; Artificial intelligence ; Machine learning ; Biological system ; Nanotechnology ; Materials science ; Physics ; Botany Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rebecca Marshall | Katherine Define | R.S. Rosner | Adam Goff | Charles Sprinkle | Prasanna V. Balachandran | Robert G. Kelly
Source: CORROSION (article, 2022-12-13)
Field: Materials Science — Materials Chemistry

Galvanic Corrosion Between Coated Al Alloy Plate and Stainless Steel Fasteners, Part 2: Application of Finite Element Method and Machine Learning to Study Galvanic Current Distributions Aluminum alloy panels joined with stainless steel fasteners have been known to occur in aerospace structures, due to their respective optimized mechanical properties. When connected via a conductive solution, a high-driving force for galvanic corrosion is present. The combination of the dissimilar materials, indicating galvanic corrosion, and complex geometry of the occluded fastener hole, indicating crevice corrosion, leads to the detrimental combined effect of galvanic-induced crevice corrosion, as investigated previously in Part I. The present work extends the validated finite element method (FEM) model to predict the current distribution and magnitude in a variety of geometric and environmental conditions, with the goal of preventing corrosion damage within the highly susceptible fastener hole. Specifically, water layer thicknesses ranging from bulk full-immersion (800 μm) to atmospheric (89 μm) conditions were investigated, as well as the impact of external scribe dimensions. Two avenues for mitigation were determined: (1) to force the majority of current away from the fastener hole and onto the bulk surface of the panel and (2) to lower the overall galvanic coupling current. A random forest machine learning algorithm was developed to generalize the FEM predictions and create an open-source applicable prediction tool. Fastener ; Crevice corrosion ; Galvanic corrosion ; Materials science ; Corrosion ; Galvanic cell ; Finite element method ; Metallurgy ; Alloy ; Current (fluid) ; Coupling (piping) ; Composite material ; Structural engineering ; Engineering ; Electrical engineering Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Non-Destructive Testing Techniques
Authors: Sherif Abdulkader Tawfik | Salvy P. Russo
Source: Journal of Cheminformatics (article, 2022-11-08)
Field: Materials Science — Materials Chemistry

Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal-organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions. Ab initio ; Computer science ; Pipeline (software) ; Class (philosophy) ; Simple (philosophy) ; Range (aeronautics) ; Shot (pellet) ; Artificial intelligence ; Machine learning ; Data mining ; Biological system ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jeong Min Choi | Kyeongpung Lee | Sangtae Kim | Minseok Moon | Wonseok Jeong | Seungwu Han
Source: Computational Materials Science (article, 2022-05-13)
Field: Materials Science — Materials Chemistry

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials Thermal conductivity ; Density functional theory ; Lattice (music) ; Statistical physics ; Computation ; Artificial neural network ; Test set ; Lattice Boltzmann methods ; Thermal ; Interatomic potential ; Computer science ; Materials science ; Algorithm ; Molecular dynamics ; Machine learning ; Condensed matter physics ; Computational physics ; Physics ; Thermodynamics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Jacopo Baima | Alexandra M. Goryaeva | Thomas D. Swinburne | Jean‐Bernard Maillet | Maylise Nastar | Mihai‐Cosmin Marinica
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science Free energy calculations in materials science are routinely hindered by the need to provide reaction coordinates that can meaningfully partition atomic configuration space, a prerequisite for most enhanced sampling approaches. Recent studies on molecular systems have highlighted the possibility of constructing appropriate collective variables directly from atomic motions through deep learning techniques. Here we extend this class of approaches to condensed matter problems, for which we encode the finite temperature collective variable by an iterative procedure starting from 0 K features of the energy landscape <i>i.e.</i> activation events or migration mechanisms given by a minimum - saddle point - minimum sequence. We employ the autoencoder neural networks in order to build a scalar collective variable for use with the adaptive biasing force method. Particular attention is given to design choices required for application to crystalline systems with defects, including the filtering of thermal motions which otherwise dominate the autoencoder input. The machine-learning workflow is tested on body-centered cubic iron and its common defects, such as small vacancy or self-interstitial clusters and screw dislocations. For localized defects, excellent collective variables as well as derivatives, necessary for free energy sampling, are systematically obtained. However, the approach has a limited accuracy when dealing with reaction coordinates that include atomic displacements of a magnitude comparable to thermal motions, <i>e.g.</i> the ones produced by the long-range elastic field of dislocations. We then combine the extraction of collective variables by autoencoders with an adaptive biasing force free energy method based on Bayesian inference. Using a vacancy migration as an example, we demonstrate the performance of coupling these two approaches for simultaneous discovery of reaction coordinates and free energy sampling in systems with localized defects. Autoencoder ; Statistical physics ; Computer science ; Vacancy defect ; Artificial intelligence ; Artificial neural network ; Algorithm ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties
Authors: Franco Allegrini | Alejandro C. Olivieri
Source: Analytica Chimica Acta (article, 2022-08-11)
Field: Chemistry — Analytical Chemistry

Linear or non-linear multivariate calibration models? That is the question Chemometrics ; Linear model ; Multivariate statistics ; Set (abstract data type) ; Linear regression ; Calibration ; Data set ; Artificial neural network ; Chemistry ; Sample (material) ; Artificial intelligence ; Machine learning ; Statistics ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Raimón Fabregat | Alberto Fabrizio | Edgar A. Engel | Benjamin Meyer | Veronika Jurásková | Michele Ceriotti | Clémence Corminbœuf
Source: Journal of Chemical Theory and Computation (article, 2022-02-18)
Field: Materials Science — Materials Chemistry

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem: the sampling of the conformational landscape of polypeptides at finite temperature. We develop a local kernel regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type neural networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost. Metadynamics ; Leverage (statistics) ; Computer science ; Energy landscape ; Artificial neural network ; Machine learning ; Artificial intelligence ; Sampling (signal processing) ; Algorithm ; Molecular dynamics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Miran Ha | Amir Hajibabaei | Saeed Pourasad | Kwang S. Kim
Source: ACS Physical Chemistry Au (article, 2022-02-17)
Field: Materials Science — Materials Chemistry

Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons Universal machine learning (ML) interatomic potentials (IAPs) for saturated, olefinic, and aromatic hydrocarbons are generated by using the Sparse Gaussian process regression algorithm. The universal potentials are obtained by combining the potentials for the previously trained alkane/polyene systems and the potentials generated with the presently trained cyclic/aromatic hydrocarbon systems, along with the newly trained cross-terms between the two systems. The ML-IAPs have been trained using the PBE + D3 level of density functional theory for the on-the-fly adaptive sampling of various hydrocarbon molecules and these clusters composed of small molecules. We tested the ML-IAPs and found that they correctly predicted the structures and energies of the β-carotene monomer and dimer. Also, the simulations of liquid ethylene reproduced the molecular volume and the simulations of toluene crystals reproduced higher stability of the α-phase over the β-phase. These ab initio-level force-fields could eventually evolve toward universal organic/polymeric/biomolecular systems. Toluene ; Gaussian ; Alkane ; Chemistry ; Ab initio ; Computational chemistry ; Molecule ; Hydrocarbon ; Polyene ; Chemical physics ; Density functional theory ; Statistical physics ; Biological system ; Thermodynamics ; Molecular physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics