jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel Gallegos | José Manuel Guevara‐Vela | Ángel Martín Pendás
Source: The Journal of Chemical Physics (article, 2021-12-13)
Field: Materials Science — Materials Chemistry

NNAIMQ: A neural network model for predicting QTAIM charges Atomic charges provide crucial information about the electronic structure of a molecular system. Among the different definitions of these descriptors, the one proposed by the Quantum Theory of Atoms in Molecules (QTAIM) is particularly attractive given its invariance against orbital transformations although the computational cost associated with their calculation limits its applicability. Given that Machine Learning (ML) techniques have been shown to accelerate orders of magnitude the computation of a number of quantum mechanical observables, in this work, we take advantage of ML knowledge to develop an intuitive and fast neural network model (NNAIMQ) for the computation of QTAIM charges for C, H, O, and N atoms with high accuracy. Our model has been trained and tested using data from quantum chemical calculations in more than 45 000 molecular environments of the near-equilibrium CHON chemical space. The reliability and performance of NNAIMQ have been analyzed in a variety of scenarios, from equilibrium geometries to molecular dynamics simulations. Altogether, NNAIMQ yields remarkably small prediction errors, well below the 0.03 electron limit in the general case, while accelerating the calculation of QTAIM charges by several orders of magnitude. Observable ; Computation ; Quantum ; Atoms in molecules ; Artificial neural network ; Computer science ; Statistical physics ; Electron ; Quantum mechanics ; Physics ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Chaolun Wang | Qiran Zou | Zhiheng Cheng | Jietao Chen | Chen Luo | Liang Fang | Chunhua Cai | Hengchang Bi | Xiaocong Lian | Xiangyang Ji | Qiubo Zhang | Litao Sun | Xing Wu
Source: Nanotechnology (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Tailoring atomic 1T phase CrTe <sub>2</sub> for <i>in situ</i> fabrication Controllable tailoring and understanding the phase-structure relationship of the 1T phase two-dimensional (2D) materials are critical for their applications in nanodevices. The<i>in situ</i>transmission electron microscope (TEM) could regulate and monitor the evolution process of the nanostructure of 2D material with atomic resolution. In this work, a controllably tailoring 1T-CrTe<sub>2</sub>nanopore is carried out by the<i>in situ</i>TEM. A preferred formation of the 1T-CrTe<sub>2</sub>border structure and nanopore healing process are studied at the atomic scale. The controllable tailoring of the 1T phase nanopore could be achieved by regulating the transformation of two types of low indices of crystal faces {101¯0} and {112¯0} at the nanopore border. Machine learning is applied to automatically process the TEM images with high efficiency. By adopting the deep-learning-based image segmentation method and augmenting the TEM images specifically, the nanopore of the TEM image could be automatically identified and the evaluation result of DICE metric reaches 93.17% on test set. This work presents the unique structure evolution of 1T phase 2D material and the computer aided high efficiency TEM data analysis based on deep learning. The techniques applied in this work could be generalized to other materials for controlled nanostructure regulation and automatic TEM image analyzation. Nanopore ; Materials science ; Nanostructure ; Nanotechnology ; Fabrication ; Atomic units ; Phase (matter) ; Nanoscopic scale ; Process (computing) ; Transmission electron microscopy ; Artificial intelligence ; Computer science 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Huan Zhang | Pei‐Song Yang | Duli Yu | Kunfeng Wang | Qingyuan Yang
Source: Chinese Journal of Chemical Engineering (article, 2021-03-18)
Field: Chemistry — Inorganic Chemistry

Prediction of methane storage in covalent organic frameworks using big-data-mining approach Deliverable ; Support vector machine ; Random forest ; Computer science ; Decision tree ; Artificial neural network ; Methane ; Machine learning ; Data mining ; Artificial intelligence ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Rui Wang | Chunlan Liu | Yong Wei | Ping Wu | Yudong Su | Zhi Zhang
Source: Results in Optics (article, 2021-08-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of metal nanoparticles based on deep learning We proposed and demonstrated a novel inverse design method of metal nanoparticles based on deep learning. By selecting the position of the SPR resonance peak, we designed the size and storage status of the metal nanoparticles. Taking gold nanospheres as an example, we contrasted the inverse design method of gold nanoparticles based on least square method and neural network for error back propagation (BP) training. The calculation results indicated that the inverse design method based on deep learning is more adaptable and stable, and its minimum error reached 2.23 × 10−12. The proposed inverse design method based on deep learning can change the mathematical model of metal nanoparticles according to the actual demands, which is suitable for the inverse design of more materials, morphology and structure of nanoparticles. It provides a new idea for the design of nanoparticles. Inverse ; Nanoparticle ; Artificial neural network ; Computer science ; Deep learning ; Colloidal gold ; Backpropagation ; Mean squared error ; Nanotechnology ; Position (finance) ; Materials science ; Artificial intelligence ; Mathematics ; Geometry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Photoacoustic and Ultrasonic Imaging
Authors: Pedro Baptista de Castro | Kensei Terashima | Miren Garbiñe Esparza Echevarría | Hiroyuki Takeya | Yoshihiko Takano
Source: arXiv (Cornell University) (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

XERUS: An open-source tool for quick XRD phase identification and refinement automation Analysis of XRD diffraction patterns is one of the keystones of materials science and materials research. With the advancement of data-driven methods for materials design, candidate materials can be quickly screened for the study of a desired physical property. Efficient methods to automatically analyze and identify phases present in a given pattern, are paramount for the success of this new paradigm. To aid this process, the open source python package Xray Estimation and Refinement Using Similarity (XERUS) for semi-automatic/automatic phase identification is presented. XERUS takes advantages of open crystal structure databases, not relying on proprietary databases, to obtain crystal structures on the fly, being then chemical space agnostic. By wrapping around GSASII, it can automatically simulate patterns and calculate similarity measures used for phase identification. Our approach is simple and quick but also applicable to multiphase identification, by coupling the similarity calculations with quick refinements followed by an iterative peak removal process. XERUS is shown in action in four different experimental datasets and also it is benchmarked against a recently proposed deep learning method for a mixture dataset covering the Li-Mn-O-F chemical space. XERUS will be freely available on https://www.github.com/pedrobcst/Xerus/ Python (programming language) ; Computer science ; On the fly ; Identification (biology) ; Similarity (geometry) ; Process (computing) ; Automation ; Data mining ; Open source ; Iterative refinement ; Algorithm ; Software ; Artificial intelligence ; Programming language ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Yifan Wu | Natalie Prezhdo | Weibin Chu
Source: The Journal of Physical Chemistry A (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

Increasing Efficiency of Nonadiabatic Molecular Dynamics by Hamiltonian Interpolation with Kernel Ridge Regression Nonadiabatic (NA) molecular dynamics (MD) goes beyond the adiabatic Born–Oppenheimer approximation to account for transitions between electronic states. Such processes are common in molecules and materials used in solar energy, optoelectronics, sensing, and many other fields. NA-MD simulations are much more expensive compared to adiabatic MD due to the need to compute excited state properties and NA couplings (NACs). Similarly, application of machine learning (ML) to NA-MD is more challenging compared with adiabatic MD. We develop an NA-MD simulation strategy in which an adiabatic MD trajectory, which can be generated with a ML force field, is used to sample excitation energies and NACs for a small fraction of geometries, while the properties for the remaining geometries are interpolated with kernel ridge regression (KRR). This ML strategy allows for one to perform NA-MD under the classical path approximation, increasing the computational efficiency by over an order of magnitude. Compared to neural networks, KRR requires little parameter tuning, saving efforts on model building. The developed strategy is demonstrated with two metal halide perovskites that exhibit complicated MD and are actively studied for various applications. Adiabatic process ; Molecular dynamics ; Hamiltonian (control theory) ; Excited state ; Statistical physics ; Solvation ; Kernel (algebra) ; Physics ; Chemistry ; Computational physics ; Quantum mechanics ; Molecule ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Perovskite Materials and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Tejas Sukumar | G. Balaji | B. Vigneshwaran | Abdullah Al Prince | S.Praveen Kumar
Source:  (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Recognition of Single and Multiple Partial Discharge patterns using Deep Learning Algorithm Recent years, checking the status of electrical apparatus is gaining importance for assessing the quality and life of insulating material. Recognition and analysis of Partial Discharge (PD) activity both in single and multiple patterns are generally used as an indicator of insulation degradation over time. Furthermore, in real-world circumstances, PD can be generated either from single or multiple sources. However, multiple PD signals gets partially overlapped, which is very hard to identify them. The proposed work illustrates a new approach to predict partial discharge patterns using Deep Learning Algorithm (DLA). To demonstrate, three different simulated discharge conditions are investigated in this work: void, corona discharge, surface discharge and their combination. The deep learning algorithm with Convolution Neural Network (CNN) have the capability to predict the patterns using pre-trained networks. The Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradients (HOG) feature extraction techniques compared with the proposed method. Partial discharge ; Computer science ; Artificial intelligence ; Feature extraction ; Pattern recognition (psychology) ; Histogram of oriented gradients ; Convolutional neural network ; Algorithm ; Histogram ; Convolution (computer science) ; Corona discharge ; Deep learning ; Feature (linguistics) ; Artificial neural network ; Image (mathematics) ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Embedded Systems and FPGA Design ; Elevator Systems and Control
Authors: Huijie Zhen | Liang Liu | Zezhou Lin | Siyan Gao | Xiaolin Li | Xi Zhang
Source: The Journal of Physical Chemistry Letters (article, 2021-02-04)
Field: Materials Science — Materials Chemistry

Physically Compatible Machine Learning Study on the Pt–Ni Nanoclusters Pt-Ni alloy nanoclusters are essential for high-performance catalysis, and the full description for the finite temperature properties is highly desired. Here we developed an efficient machine learning method to evaluate the accurate structure-stability correspondence in a Pt<sub>(85-<i>x</i>)</sub>-Ni<sub><i>x</i></sub> nanocluster over the structural space with a dimension of 3.84 × 10<sup>25</sup>. On the basis of the physical model and big-data analysis, for the first time, we demonstrated that the segregation-extent bond order parameter (BOP) and the shell-resolved undercoordination ratio play the key roles in the structural stability. This a priori knowledge extremely reduced the computational costs and enhanced the accuracies. With the 500-sample train data set generated by density functional theory (DFT)-level geometry optimizations, we fit the machine-learning excess energy potential and verified the mean-square-error is <0.13. Our physically niche genetic-machine learning program (PNG-ML) searched 2.5 × 10<sup>5</sup> structures and predicted precisely the most stable Pt<sub>43</sub>-Ni<sub>42</sub> (<i>x</i> = 42). The structural space dimension was reduced by 10<sup>20</sup> fold using our PNG-ML method. The Pt/Ni ratio of the most stable nanocluster is 1.02, which is highly consistent with the experimental observation of 1.0. The above results provide reliable theoretical references for the realistic applications of Pt-Ni nanoclusters and suggest feature engineering for future studies on binary alloys nanostructures. Nanoclusters ; Stability (learning theory) ; Density functional theory ; A priori and a posteriori ; Binary number ; Machine learning ; Materials science ; Computer science ; Artificial intelligence ; Algorithm ; Nanotechnology ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanocluster Synthesis and Applications
Authors: Vittorio Michelassi | Julia Ling
Source: Journal of the Global Power and Propulsion Society (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective Turbomachines are still required for the efficient conversion of renewable, chemical or potential energy into propulsion, mechanical power, or electricity. The increasing demand for efficiency, availability, reduced footprint and cost of ownership poses fundamental challenges to design methods the accuracy of which needs to be constantly upgraded to keep the pace with the availability of new materials, type of fluids and fuels, manufacturing methods and technologies. This paper discusses recent trends in design methods that take advantage of both artificial intelligence and high-fidelity simulations techniques that guide the design process by harvesting design data from multiple sources and improve the accuracy of design verification respectively. Such approach has been successfully used in aero and thermal design as well as in the critical area of materials and fluids engineering. In the future the concerted use of machine learning and high-fidelity methods may allow researchers to conduct reliable virtual tests in the framework of design loops to cut down design time and risk. Computer science ; Systems engineering ; Fidelity ; Engineering design process ; Process (computing) ; Renewable energy ; Efficient energy use ; Turbomachinery ; Pace ; Risk analysis (engineering) ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Heat Transfer and Boiling Studies ; Nuclear Engineering Thermal-Hydraulics
Authors: Michael C. Lun | Wenxiang Cong | Md. Arifuzzaman | Meenakshi Ranasinghe | Sriparna Bhattacharya | Jeffrey N. Anker | Ge Wang | Changqing Li
Source: Journal of Biomedical Optics (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Focused x-ray luminescence imaging system for small animals based on a rotary gantry The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals. Molecular imaging ; Materials science ; Scanner ; Image resolution ; Iterative reconstruction ; Tomography ; Luminescence ; Positron emission tomography ; Preclinical imaging ; Biomedical engineering ; Computer science ; Optics ; Medical physics ; Artificial intelligence ; Nuclear medicine ; Physics ; Optoelectronics ; Medicine ; In vivo Luminescence Properties of Advanced Materials ; Optical Imaging and Spectroscopy Techniques ; Medical Imaging Techniques and Applications
Authors: Mingrui Jiang | Ting Shu | Chao Ye | Jing Ren | Shengjie Ling
Source: The Analyst (article, 2021-01-01)
Field: Materials Science — Biomaterials

Predicting the conformations of the silk protein through deep learning As with other proteins, the conformation of the silk protein is critical for determining the mechanical, optical and biological performance of materials. However, an efficient, accurate and time-efficient method for evaluating the protein conformation from Fourier transform infrared (FTIR) spectra is still desired. A set of convolutional neural network (CNN)-based deep learning models was developed in this study to identify the silk proteins and evaluate their relative content of each conformation from FTIR spectra. Compared with the conventional deconvolution algorithm, our CNN models are highly accurate and time-efficient, showing promise in processing massive FTIR data sets, such as data from FTIR imaging, and in quick analysis feedback, such as on-line and time-resolved FTIR measurements. We compiled an open-source and user-friendly graphical Python program that allows users to analyze their own FTIR data set, which can be from the silk protein or other proteins, for the encouragement and convenience of interested researchers to use the CNN models. SILK ; Fourier transform infrared spectroscopy ; Deep learning ; Artificial intelligence ; Chemistry ; Biological system ; Polymer science ; Computer science ; Chemical engineering ; Materials science ; Engineering ; Composite material ; Biology Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Insect and Arachnid Ecology and Behavior
Authors: Ashu Panwar | Saeed Shirazian | Mehakpreet Singh | Gavin Walker
Source: Journal of Molecular Liquids (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Comprehensive modelling of pharmaceutical solvation energy in different solvents Solvation ; Artificial neural network ; Crystallization ; Computer science ; Artificial intelligence ; Biological system ; Process engineering ; Materials science ; Machine learning ; Solvent ; Chemistry ; Organic chemistry ; Engineering Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Chaoyi Chang | Andrew J. Medford
Source: The Journal of Physical Chemistry C (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Application of Density Functional Tight Binding and Machine Learning to Evaluate the Stability of Biomass Intermediates on the Rh(111) Surface Biomass compounds adsorbed on surfaces are challenging to study due to the large number of possible species and adsorption geometries. In this work, possible intermediates of erythrose, glyceraldehyde, glycerol, and propionic acid are studied on the Rh(111) surface. The intermediates and elementary reactions are generated from first two recursions of a recursive bond-breaking algorithm. These structures are used as the input of an unsupervised Mol2Vec algorithm to generate vector descriptors. A data-driven scheme to classify the reactions is developed, and adsorption energies are predicted. The lowest mean absolute error of our prediction on adsorption energies is 0.39 eV, and the relative ordering of different surface adsorption geometries is relatively accurate. We show that combining geometries from density functional tight-binding calculations with energies from machine-learning predictions provides a novel workflow for rapidly assessing the stability of various molecular geometries on the Rh(111) surface. Adsorption ; Stability (learning theory) ; Density functional theory ; Work (physics) ; Surface (topology) ; Chemistry ; Binding energy ; Tight binding ; Computational chemistry ; Chemical physics ; Materials science ; Physics ; Computer science ; Physical chemistry ; Mathematics ; Atomic physics ; Thermodynamics ; Machine learning ; Electronic structure ; Geometry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Ryan J. Richards | Arindam Paul
Source: Solar Energy (article, 2021-06-05)
Field: Materials Science — Materials Chemistry

An attention-driven long short-term memory network for high throughput virtual screening of organic photovoltaic candidate molecules Photovoltaic system ; Computer science ; Renewable energy ; Virtual screening ; HOMO/LUMO ; Organic solar cell ; Acceptor ; Materials science ; Molecule ; Biological system ; Drug discovery ; Physics ; Chemistry ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maxim Ziatdinov | Ayana Ghosh | Tommy Wong | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2021-05-16)
Field: Materials Science — Surfaces, Coatings and Films

AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders (VAEs). The latter consists of VAEs with rotational and (optionally) translational invariance for unsupervised and class-conditioned disentanglement of categorical and continuous data representations. In addition, AtomAI provides utilities for mapping structure-property relationships via im2spec and spec2im type of encoder-decoder models. Finally, AtomAI allows seamless connection to the first principles modeling with a Python interface, including molecular dynamics and density functional theory calculations on the inferred atomic position. While the majority of applications to date were based on atomically resolved electron microscopy, the flexibility of AtomAI allows straightforward extension towards the analysis of mesoscopic imaging data once the labels and feature identification workflows are established/available. The source code and example notebooks are available at https://github.com/pycroscopy/atomai. Python (programming language) ; Computer science ; Artificial intelligence ; Encoder ; Mesoscopic physics ; Pattern recognition (psychology) ; Software ; Convolutional neural network ; Theoretical computer science ; Physics ; Programming language Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications