Filters








6,530 Hits in 4.2 sec

Machine Learning-Assisted High-Throughput Semi-empirical Search of OFET Molecular Materials [article]

Zhenyu Chen, Jiahao Li, Yuzhi Xu
2021 arXiv   pre-print
Machine learning has been widely verified and applied in chemoinformatics, and have achieved outstanding results in the prediction, modification, and optimization of luminescence, magnetism, and electrode  ...  Here, we propose a deepth first search traversal (DFST) approach combined with lightGBM machine learning model to search the classic Organic field-effect transistor (OFET) functional molecules chemical  ...  based on DFT. Ⅱ.  ... 
arXiv:2107.02613v1 fatcat:qevy46lojvbenjql3idft3k4vu

Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides

Masanori Kaneko, Mikiya Fujii, Takashi Hisatomi, Koichi Yamashita, Kazunari Domen
2019 Journal of Energy Chemistry  
This work shows that anion ordering in large-scale supercells within perovskite-type oxynitrides can be rapidly predicted based on machine learning, using BaNbO 2 N (capable of oxidizing water under irradiation  ...  However, the numerous possible orderings complicate systematic analyses based on density functional theory (DFT) calculations using defined elemental arrangements.  ...  The DFT calculations in this study were performed using the facilities at the Supercomputer Center in the Institute for Solid State Physics (ISSP) at the University of Tokyo and at the Research Center  ... 
doi:10.1016/j.jechem.2019.01.012 fatcat:iazil77sejciraor27r6546fbq

Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

Omar Allam, Byung Woo Cho, Ki Chul Kim, Seung Soon Jang
2018 RSC Advances  
In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials.  ...  It has two components: one is quantum mechanical density functional theory (DFT) modeling and another is the machine learning method.  ...  Machine learning (ML), a precursor to articial intelligence, offers an algorithm that can achieve its predictive capability by learning from an input dataset. 27 8] [29] For instance, once a machine  ... 
doi:10.1039/c8ra07112h pmid:35558035 pmcid:PMC9090775 fatcat:xmk4jyt7tzdn5faqgd527b4lpi

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang (+16 others)
2020 npj Computational Materials  
force-fields (FF), and machine learning (ML) techniques.  ...  AbstractThe Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical  ...  The database component of JARVIS consists of JARVIS-DFT for DFT calculations and JARVIS-FF for molecular dynamics simulations. JARVIS-ML hosts several machine learning models based on our datasets.  ... 
doi:10.1038/s41524-020-00440-1 fatcat:zr7amkyjuvh57aurrei23k6v54

A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis

Xuhao Wan, Zhaofu Zhang, Wei Yu, Yuzheng Guo
2021 Materials Reports: Energy  
Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated (DFT-ML) scheme, and the common algorithms useable for machine learning, we presented in this paper our  ...  DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.  ...  The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.  ... 
doi:10.1016/j.matre.2021.100046 fatcat:27acb4t3ejhf5amseb7pptiu64

Identification of pesticide residues on black tea by fluorescence hyperspectral technology combined with machine learning

Jie SUN, Yan HU, Yulin ZOU, Jinping GENG, Youli WU, Rongsheng FAN, Zhiliang KANG
2022 Food Science and Technology  
on black tea, and the 1D CNN-RF model was compared with three other machine learning models (support vector machine, RF, and 1D CNN).  ...  This study proposes a machine learning model composed of a one-dimensional convolutional neural network backbone (1D CNN backbone) and a random forest classifier (RF classifier) to identify pesticide residues  ...  This study proposes a new method for the identification of pesticide residues on the surface of Ya'an black tea based on fluorescence hyperspectral technology and machine learning.  ... 
doi:10.1590/fst.55822 fatcat:tgjggvzzhvfyfeldr47pfjea6e

Journal of Materials Chemistry A and Materials Advances Editor's choice web collection: "Machine learning for materials innovation"

Zhen Zhou
2021 Materials Advances  
Zhen Zhou introduces a Journal of Materials Chemistry/Materials Advances Editor's choice web collection on machine learning for materials innovation (https://rsc.li/MachineLearning).  ...  Machine learning technology could develop flexible models, which are trained by the input data to predict the desired information based on various algorithms.  ...  Yang et al. built a machine learning model based on the intrinsic properties of substrates and adsorbates to rapidly screen alloy catalysts for the CO 2 reduction reaction with the usual DFT accuracy (  ... 
doi:10.1039/d0ma90054k fatcat:5344vzoidbhhjmx7aycfzupb24

Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization

Steven Maley, Doo-Hyun Kwon, Nick Rollins, Johnathan C Stanely, Orson L Sydora, Steven Michael Bischof, Daniel H Ess
2020 Chemical Science  
The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general...  ...  Fig. 5 5 Top: Structures for previous (P,N) ligand generations and the new proposed ligands (generation 3) based on machine-learning identified features.  ...  This work showed that machine learning Scheme 3 Outline of 105 unique (P,N) ligands in our transition-state training data set. These ligands were used to calculate selectivity based on TS1 and TS2.  ... 
doi:10.1039/d0sc03552a pmid:34094231 pmcid:PMC8161675 fatcat:p3rpjxoravduxfvdy7bzgieg24

Machine Learning in Materials Modeling – Fundamentals and the Opportunities in 2D Materials [article]

Shreeja Das, Hansraj Pegu, Kisor Sahu, Ameeya Kumar Nayak, Seeram Ramakrishna, Dibakar Datta, Soumya Swayamjyoti
2020 arXiv   pre-print
The challenges faced in designing batteries and how machine learning tools can help in screening and narrowing down on the best composition, as well as the synthesis of air-stable 2D materials, are also  ...  The application of machine learning in materials presents a unique challenge of dealing with scarce and varied materials data - both experimental and theoretical.  ...  ML is also integrated with DFT (DFT-based ML) for developing molecular electrode materials in LIBs [34] . All these studies on ML for energies are not on 2D materials based systems.  ... 
arXiv:2001.04605v1 fatcat:uhpv22vmejcoxf77a4cnokglji

Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms [article]

Tomoyuki Tamura, Masayuki Karasuyama
2020 arXiv   pre-print
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning.  ...  The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy.  ...  Figure 1 shows a schematic of our proposed procedure, in which a machine-learning model is built based on an atomic descriptor space.  ... 
arXiv:1912.04596v3 fatcat:knbwht4tijh3tkzqoftiephsqe

Predicting the thermodynamic stability of perovskite oxides using machine learning models

Wei Li, Ryan Jacobs, Dane Morgan
2018 Computational materials science  
We generated a set of 791 features based on elemental property data to correlate with the Ehull value of each perovskite compound.  ...  In this work, we developed machine learning models to predict the thermodynamic phase stability of perovskite oxides using a dataset of more than 1900 DFT-calculated perovskite oxide energies.  ...  Scikit-learn is an open source machine learning package distributed under BSD license.  ... 
doi:10.1016/j.commatsci.2018.04.033 fatcat:px6huubrlrdctih5sxswgxvfra

JARVIS: An Integrated Infrastructure for Data-driven Materials Design [article]

Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang (+16 others)
2020 arXiv   pre-print
force-fields (FF), and machine learning (ML) techniques.  ...  The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical  ...  The database component of JARVIS consists of JARVIS-DFT for DFT calculations and JARVIS-FF for molecular dynamics simulations. JARVIS-ML hosts several machine learning models based on our datasets.  ... 
arXiv:2007.01831v1 fatcat:frgfqvwp4neo7h6y63d5n6bgkm

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

Randy Jalem, Masanobu Nakayama, Yusuke Noda, Tam Le, Ichiro Takeuchi, Yoshitaka Tateyama, Hisatsugu Yamazaki
2018 Science and Technology of Advanced Materials  
real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal.  ...  We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation  ...  DFT Computations were mainly carried out at the Information Technology Center of Nagoya University (CX400).  ... 
doi:10.1080/14686996.2018.1439253 pmid:29707064 pmcid:PMC5917445 fatcat:jshp37qu6zbmzhz26xdgj3l7bi

Computational discovery of energy materials in the era of big data and machine learning: a critical review

Ziheng Lu
2021 Materials Reports: Energy  
on machine learning.  ...  Three paradigms based on empiricism-driven experiments, database-driven high-throughput screening, and data informatics-driven machine learning are discussed critically.  ...  The third paradigm of materials discovery is based on data informatics and machine learning.  ... 
doi:10.1016/j.matre.2021.100047 fatcat:qookvmaqfze7zogocfqrbu7awi

Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering

Carlos León, Roderick Melnik
2022 Bioengineering  
with machine learning interatomic potentials.  ...  In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects  ...  We use a physics-based ML model designed for materials, coded in the MLIP (Machine Learning Interatomic Potential) package [39] that we use to build an interatomic potential for the GO system.  ... 
doi:10.3390/bioengineering9030090 pmid:35324779 pmcid:PMC8945856 fatcat:thjsynos7rdc3mvya7dibbio5m
« Previous Showing results 1 — 15 out of 6,530 results