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Materials Informatics: A Journey Towards Material Design And Synthesis.

Keisuke Takahashi, Yuzuru Tanaka
Published 2016 · Medicine, Materials Science
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Materials informatics has been gaining popularity with the rapid development of computational materials science. However, collaborations between information science and materials science have not yet reached the success. There are several issues which need to be overcome in order to establish the field of materials informatics. Construction of material big data, implementation of machine learning, and platform design for materials discovery are discussed with potential solutions.
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This paper is referenced by
10.1016/J.COMMATSCI.2019.03.005
MatCALO: Knowledge-enabled machine learning in materials science
Mareike Picklum (2019)
10.3390/app10020569
Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO 2 Capture from Air
Xiaomei Deng (2020)
10.1016/J.COMMATSCI.2019.04.030
Application of materials informatics on crystalline materials for two-body terms approximation
Van-Doan Nguyen (2019)
10.1088/1361-648X/aaa471
Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations.
Itsuki Miyazato (2018)
10.1039/c7cp07542a
Atomic adsorption on graphene with a single vacancy: systematic DFT study through the periodic table of elements.
Igor A Pašti (2018)
10.1016/J.COMMATSCI.2018.10.036
Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications
Abigail Hunter (2019)
10.1007/s12613-019-1724-x
A novel approach to predict green density by high-velocity compaction based on the materials informatics method
Kai-qi Zhang (2019)
10.1088/2515-7639/AB077B
Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO
Runhai Ouyang (2019)
10.1016/j.cpc.2019.106949
DScribe: Library of Descriptors for Machine Learning in Materials Science
Lauri Himanen (2020)
10.1021/ACS.CHEMMATER.8B00679
Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives.
Anna M Hiszpanski (2018)
10.1002/CCTC.201801956
The Rise of Catalyst Informatics: Towards Catalyst Genomics
Keisuke Takahashi (2019)
10.1063/1.5037098
A new approach for the prediction of partition functions using machine learning techniques.
Caroline Desgranges (2018)
10.1146/annurev-matsci-070218-010015
Opportunities and Challenges for Machine Learning in Materials Science
Dane Morgan (2020)
10.1021/acs.jpca.9b11712
The Rising Sun Envelope Method: An Automatic and Accurate Peak Location Technique for XANES Measurements.
Rafael Monteiro (2020)
10.1016/J.COMMATSCI.2018.09.026
An automated algorithm for reliable equation of state fitting of magnetic systems
H. Levamaki (2019)
10.1038/s43246-020-00052-8
Integrating multiple materials science projects in a single neural network
Kan Hatakeyama-Sato (2020)
10.1002/advs.201900808
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Lauri Himanen (2019)
Exploration of novel two dimensional materials by using first principle calculation and materials informatics approach
Itsuki Miyazato (2019)
10.1016/j.pmatsci.2020.100664
Deformation behavior and amorphization in icosahedral boron-rich ceramics
Amnaya P. Awasthi (2020)
10.1002/CCTC.201800310
Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data
Keisuke Takahashi (2018)
10.1103/physrevapplied.12.054049
Decoding Phases of Matter by Machine-Learning Raman Spectroscopy
Anyang Cui (2019)
10.7567/1347-4065/ab4f39
A high throughput molecular screening for organic electronics via machine learning: present status and perspective
Akinori Saeki (2020)
10.1103/PhysRevB.100.174506
Materials informatics based on evolutionary algorithms: Application to search for superconducting hydrogen compounds
Takahiro Ishikawa (2019)
10.2200/s00981ed1v01y202001mop001
Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods
Ghanshyam Pilania (2020)
10.1007/s40192-018-0117-8
Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
Dennis M. Dimiduk (2018)
10.1103/PHYSREVMATERIALS.2.120301
Machine learning in materials design and discovery: Examples from the present and suggestions for the future
James E. Gubernatis (2018)
10.1103/PHYSREVB.95.054110
Unveiling descriptors for predicting the bulk modulus of amorphous carbon
Keisuke Takahashi (2017)
10.1016/j.jmat.2020.02.011
Metaheuristic-based inverse design of materials – A survey
T. Warren Liao (2020)
10.1002/adfm.201907259
Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells
Nahdia Majeed (2020)
10.3390/CRYST9040191
Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors
Zhuo Cao (2019)
10.1063/1.4984047
Descriptors for predicting the lattice constant of body centered cubic crystal.
Katsuji Takahashi (2017)
10.1177/1473871620925821
GoCrystal: A gamified visual analytics tool for analysis and visualization of atomic configurations and thermodynamic energy models
Haeyong Chung (2020)
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