jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoyo Hinuma | Shinya Mine | Takashi Toyao | Takashi Kamachi | Ken‐ichi Shimizu
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (<i>E</i><sub>Ovac</sub>) is a critical quantity for catalyst performance because the surface of metal oxide catalysts often acts as a reaction site, for example, in the Mars-van Krevelen mechanism. However, experimental evaluation of <i>E</i><sub>Ovac</sub> is very challenging. We obtained the <i>E</i><sub>Ovac</sub> for (100), (110), and (111) surfaces of normal zinc-based spinel oxides ZnAl<sub>2</sub>O<sub>4</sub>, ZnGa<sub>2</sub>O<sub>4</sub>, ZnIn<sub>2</sub>O<sub>4</sub>, ZnV<sub>2</sub>O<sub>4</sub>, ZnCr<sub>2</sub>O<sub>4</sub>, ZnMn<sub>2</sub>O<sub>4</sub>, ZnFe<sub>2</sub>O<sub>4</sub>, and ZnCo<sub>2</sub>O<sub>4</sub>. The most stable surface is (100) for all compounds. The smallest <i>E</i><sub>Ovac</sub> for a surface is the largest in the (100) surface except for ZnCo<sub>2</sub>O<sub>4</sub>. For (100) and (110) surfaces, there is a good correlation, over all spinels, between the smallest <i>E</i><sub>Ovac</sub> for the surface and bulk formation energy, while the ionization potential correlates well in (111) surfaces. Machine learning over <i>E</i><sub>Ovac</sub> of all surface sites in all orientations and for all compounds to find the important factors, or descriptors, that decide the <i>E</i><sub>Ovac</sub> revealed that bulk and surface-dependent descriptors are the most important, namely the bulk formation energy, a Boolean descriptor of whether the surface is (111) or not, and the ionization potential, followed by geometrical descriptors that are different in each O site. Spinel ; Catalysis ; Vacancy defect ; Oxide ; Ternary operation ; Electrocatalyst ; Photocatalysis ; Materials science ; Zinc ; Inorganic chemistry ; Chemical physics ; Oxygen ; Chemistry ; Chemical engineering ; Physical chemistry ; Crystallography ; Electrochemistry ; Metallurgy ; Electrode Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Ye Sang | Wei-Zhi Huang | Min Li | Xi‐Qiao Feng
Source: Extreme Mechanics Letters (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Deep learning method for determining the surface elastic moduli of microstructured solids Materials science ; Surface (topology) ; Elastic modulus ; Microstructure ; Finite element method ; Constitutive equation ; Composite material ; Artificial neural network ; Computer science ; Structural engineering ; Geometry ; Artificial intelligence ; Mathematics ; Engineering Machine Learning in Materials Science ; Thermal properties of materials ; Adhesion, Friction, and Surface Interactions
Authors: Shusen Chen | Taylor Nielson | Elayna Zalit | Bastian Bjerkem Skjelstad | Braden Borough | William J. Hirschi | Spencer Yu | David Balcells | Daniel H. Ess
Source: Topics in Catalysis (article, 2021-09-12)
Field: Materials Science — Materials Chemistry

Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States Workflow ; Transition metal ; Catalysis ; Ligand (biochemistry) ; Methane ; Computer science ; Chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Lihao Chen | Chuwen Lan | Ben Xu | Ke Bi
Source: Advanced Composites and Hybrid Materials (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

Progress on material characterization methods under big data environment Characterization (materials science) ; Computer science ; Digitization ; Big data ; Throughput ; Material properties ; Feature (linguistics) ; Field (mathematics) ; Quality (philosophy) ; Materials science ; Nanotechnology ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Deringer, VL | William Kirkpatrick | Volker L. Deringer
Source: Oxford University Research Archive (ORA) (University of Oxford) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Cluster fragments in amorphous phosphorus and their evolution under pressure Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how this structure changes under pressure. The structural model so obtained contains abundant five-membered rings, as well as more complex seven- and eight-atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials. Materials science ; Cluster (spacecraft) ; Chemical physics ; Amorphous solid ; Molecular dynamics ; Atomic units ; Hysteresis ; Diffraction ; Atomic radius ; Nanotechnology ; Crystallography ; Computational chemistry ; Condensed matter physics ; Physics ; Chemistry ; Computer science ; Optics Machine Learning in Materials Science ; 2D Materials and Applications ; Inorganic Chemistry and Materials
Authors: Kei Terayama | Kwangsik Han | Ryoji Katsube | Ikuo Ohnuma | Taichi Abe | Yoshitaro Nose | Ryo Tamura
Source: Scripta Materialia (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Acceleration of phase diagram construction by machine learning incorporating Gibbs' phase rule To efficiently construct phase diagrams of alloy systems, a machine learning-based method advanced by thermodynamics on phase equilibria is proposed. With the use of uncertainty sampling in active learning, the next point to be synthesized or measured can be recommended to efficiently draw the phase diagram. For appropriate recommendations, two ingenuities are introduced in the machine learning method: training data preparation when the multiphase coexisting region is detected and search space reduction based on the Gibbs’ phase rule. We demonstrate the construction of ternary phase diagrams using our machine learning method by incorporating these ingenuities. The complicated phase diagram of alloy systems could be effectively plotted even when knowing only the information of single-component systems in the initial step. The recommendation made by our machine learning method can help reduce the number of experiments required to construct a phase diagram to approximately 1/8 compared with random sampling. Phase diagram ; Machine learning ; Construct (python library) ; Phase rule ; Computer science ; Artificial intelligence ; Phase (matter) ; Ternary operation ; Sampling (signal processing) ; Component (thermodynamics) ; Materials science ; Thermodynamics ; Algorithm ; Statistical physics ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Yueming Guo | Sergei V. Kalinin | Hui Cai | Kai Xiao | Sergiy Krylyuk | Albert V. Davydov | Qianying Guo | Andrew R. Lupini
Source: npj Computational Materials (article, 2021-11-09)
Field: Materials Science — Materials Chemistry

Defect detection in atomic-resolution images via unsupervised learning with translational invariance Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals. Artificial intelligence ; Preprocessor ; Computer science ; Pattern recognition (psychology) ; Segmentation ; Point cloud ; Unsupervised learning ; Support vector machine ; Computer vision ; Supervised learning ; Object detection ; Artificial neural network Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Hongfang Lü | Saleh Behbahani | Xin Ma | Tom Iseley
Source: Construction and Building Materials (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

A multi-objective optimizer-based model for predicting composite material properties Property (philosophy) ; Composite number ; Computer science ; Support vector machine ; Set (abstract data type) ; Material properties ; Predictive modelling ; Performance prediction ; Mean squared prediction error ; Machine learning ; Training set ; Artificial intelligence ; Algorithm ; Materials science ; Simulation Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Qiuping Yang | Jian Lv | Qunchao Tong | Xin Du | Yanchao Wang | Shoutao Zhang | Guochun Yang | Aitor Bergara | Yanming Ma
Source: Physical review. B./Physical review. B (article, 2021-01-08)
Field: Materials Science — Materials Chemistry

Hard and superconducting cubic boron phase via swarm-intelligence structural prediction driven by a machine-learning potential Boron is an intriguing element due to its electron deficiency and the ability to form multicenter bonds in allotropes and borides, exhibiting diversified structures, unique chemical bonds, and interesting properties. Using swarm-intelligence structural prediction driven by a machine learning potential, we identified a boron phase with a 24-atom cubic unit cell, called $c\text{\ensuremath{-}}{\mathrm{B}}_{24}$, consisting of a ${\mathrm{B}}_{6}$ octahedron in addition to well-known ${\mathrm{B}}_{2}$ pairs and ${\mathrm{B}}_{12}$ icosahedra at ambient pressure. There appear unusual four-center-two-electron (4c-2e) bonds in the ${\mathrm{B}}_{12}$ icosahedron, originating from the peculiar bonding pattern between the ${\mathrm{B}}_{2}$ pair and ${\mathrm{B}}_{12}$ icosahedron, which is in sharp contrast with the 3c-2e and 2c-2e bonds in $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathrm{B}}_{12}$. More interestingly, $c\text{\ensuremath{-}}{\mathrm{B}}_{24}$ is a metal with a superconducting critical temperature of 13.8 K at ambient pressure. The predicted Vickers hardness (23.1 GPa) indicates that $c\text{\ensuremath{-}}{\mathrm{B}}_{24}$ is a potential hard material. Notably, it also has a good shear/tensile resistance (48.9/29.3 GPa). Our work not only enriches the understanding of the chemical properties of boron, but also sparks efforts on trying to synthesize this particular compound, $c\text{\ensuremath{-}}{\mathrm{B}}_{24}$. Octahedron ; Superconductivity ; Boron ; Crystallography ; Materials science ; Vickers hardness test ; Chemical bond ; Phase (matter) ; Atom (system on chip) ; Condensed matter physics ; Physics ; Crystal structure ; Chemistry ; Quantum mechanics ; Microstructure ; Nuclear physics Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Nguyễn Minh Tâm | Duc-Hung Pham | Đinh Minh Hiệp | Phuong‐Thao Tran | Dương Tuấn Quang | Sơn Tùng Ngô
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations The spread of severe acute respiratory syndrome coronavirus 2 novel coronavirus (SARS-CoV-2) worldwide has caused the coronavirus disease 2019 (COVID-19) pandemic. A hundred million people were infected, resulting in several millions of death worldwide. In order to prevent viral replication, scientists have been aiming to prevent the biological activity of the SARS-CoV-2 main protease (3CL pro or Mpro). In this work, we demonstrate that using a reasonable combination of deep-learning calculations and atomistic simulations could lead to a new approach for developing SARS-CoV-2 main protease (Mpro) inhibitors. Initially, the binding affinities of the natural compounds to SARS-CoV-2 Mpro were estimated <i>via</i> atomistic simulations. The compound tomatine, thevetine, and tribuloside could bind to SARS-CoV-2 Mpro with nanomolar/high-nanomolar affinities. Secondly, the deep-learning (DL) calculations were performed to chemically alter the top-lead natural compounds to improve ligand-binding affinity. The obtained results were then validated by free energy calculations using atomistic simulations. The outcome of the research will probably boost COVID-19 therapy. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Natural (archaeology) ; 2019-20 coronavirus outbreak ; Computer science ; Chemistry ; Computational biology ; Virology ; Biology ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shuang He | Werner Ecker | Oleg E. Peil | Reinhard Pıppan | Vsevolod I. Razumovskiy
Source: Materialia (article, 2021-12-01)
Field: Materials Science — Metals and Alloys

The effect of solute atoms on the bulk and grain boundary cohesion in Ni: Implications for hydrogen embrittlement Materials science ; Grain boundary ; Embrittlement ; Hydrogen embrittlement ; Hydrogen ; Work (physics) ; Impurity ; Metallurgy ; Thermodynamics ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; High Temperature Alloys and Creep
Authors: Dongsun Yoo | Jisu Jung | Wonseok Jeong | Seungwu Han
Source: npj Computational Materials (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials Abstract The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but conventional approaches are prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. To overcome this hurdle, methods are being proposed that automatically generate training data. Herein, we suggest a sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout these examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications. Metadynamics ; Sampling (signal processing) ; Training set ; Space (punctuation) ; Set (abstract data type) ; Training (meteorology) ; Atom (system on chip) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Jingjing Ma | Jin Cheng | Jinghua Wang | Ruoqian Pan | Fang He | Lei Yan | Jiang Xiao
Source: Information Processing in Agriculture (article, 2021-06-23)
Field: Chemistry — Analytical Chemistry

Rapid detection of total nitrogen content in soil based on hyperspectral technology Soil total nitrogen content (TN) is a crucial factor in boosting the growth of crops. Its surplus or scarcity will alter the quality and yield of crops to a certain extent. Traditional methods such as chemical analysis is complicated, laborious and time-consuming. A faster and more efficient method to detect TN should be explored to address this problem. The hyperspectral technology integrates conventional energy and spectroscopy which aids in the simultaneous collection of spatial and spectral information from an object. It has gradually proved its significance and gained popularity in the analysis of soil composition. This study discussed the possibility of using hyperspectral technology to detect TN, analyzed six spectral data preprocessing methods and five modeling methods: partial least squares (PLS), back-propagation (BP) neural network, radial basis function (RBF) neural network, extreme learning machine (ELM) and support vector regression (SVR) with evaluation index R2 and RMSE. Setting the content of chemical analysis as the control and comparing the errors from spectral analysis. According to the results, all five models can be used for TN detection, and the SVR model with R2 0.912 1 and RMSE 0.758 1 turned to the best method. The study showed that the spectral model can detect TN quickly, providing a reference for the detection of elements in soil with favorable research significance. Hyperspectral imaging ; Partial least squares regression ; Support vector machine ; Artificial neural network ; Preprocessor ; Mean squared error ; Artificial intelligence ; Least squares support vector machine ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Geochemistry and Geologic Mapping
Authors: Tina Olfatbakhsh | Abbas S. Milani
Source: Composites Science and Technology (article, 2021-10-04)
Field: Materials Science — Materials Chemistry

A highly interpretable materials informatics approach for predicting microstructure-property relationship in fabric composites Waviness ; Materials science ; Composite material ; Microstructure ; Aerospace ; Materials informatics ; Woven fabric ; Polypropylene ; Health informatics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Thermography and Photoacoustic Techniques
Authors: Ryosuke Jinnouchi | Ferenc Karsai | Carla Verdi | Georg Kresse
Source: The Journal of Chemical Physics (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

First-principles hydration free energies of oxygenated species at water–platinum interfaces The hydration free energy of atoms and molecules adsorbed at liquid-solid interfaces strongly influences the stability and reactivity of solid surfaces. However, its evaluation is challenging in both experiments and theories. In this work, a machine learning aided molecular dynamics method is proposed and applied to oxygen atoms and hydroxyl groups adsorbed on Pt(111) and Pt(100) surfaces in water. The proposed method adopts thermodynamic integration with respect to a coupling parameter specifying a path from well-defined non-interacting species to the fully interacting ones. The atomistic interactions are described by a machine-learned inter-atomic potential trained on first-principles data. The free energy calculated by the machine-learned potential is further corrected by using thermodynamic perturbation theory to provide the first-principles free energy. The calculated hydration free energies indicate that only the hydroxyl group adsorbed on the Pt(111) surface attains a hydration stabilization. The observed trend is attributed to differences in the adsorption site and surface morphology. Adsorption ; Molecule ; Chemistry ; Chemical physics ; Platinum ; Thermodynamic integration ; Molecular dynamics ; Work (physics) ; Potential energy surface ; Perturbation theory (quantum mechanics) ; Free energy perturbation ; Surface energy ; Thermodynamics ; Physical chemistry ; Computational chemistry ; Physics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Hasan Özdoğan | Yiğit Ali Üncü | Onur Karaman | Mert Şekerci | Abdullah Kaplan
Source: Applied Radiation and Isotopes (article, 2021-01-09)
Field: Chemistry — Spectroscopy

Estimations of giant dipole resonance parameters using artificial neural network Artificial neural network ; Resonance (particle physics) ; Dipole ; Convergence (economics) ; Algorithm ; Levenberg–Marquardt algorithm ; Computer science ; Code (set theory) ; Physics ; Artificial intelligence ; Atomic physics ; Quantum mechanics Advanced NMR Techniques and Applications ; Nuclear physics research studies ; Nuclear Physics and Applications