标题
Recent machine learning guided material research - A review
作者
关键词
ML, Material science, Design, Characterization, Advancements, Challenges
出版物
Computational Condensed Matter
Volume 29, Issue -, Pages e00597
出版商
Elsevier BV
发表日期
2021-09-20
DOI
10.1016/j.cocom.2021.e00597
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Descriptor selection for predicting interfacial thermal resistance by machine learning methods
- (2021) Xiaojuan Tian et al. Scientific Reports
- Machine learning in materials science: From explainable predictions to autonomous design
- (2021) Ghanshyam Pilania COMPUTATIONAL MATERIALS SCIENCE
- Screening stable and metastable ABO3 perovskites using machine learning and the materials project
- (2020) Haiying Liu et al. COMPUTATIONAL MATERIALS SCIENCE
- Machine learning in experimental materials chemistry
- (2020) Balaranjan Selvaratnam et al. CATALYSIS TODAY
- Two-dimensional Janus material MoS2(1-x)Se2x (0
- (2020) Guanhua Zhang et al. COMPUTATIONAL MATERIALS SCIENCE
- Machine learning-guided synthesis of advanced inorganic materials
- (2020) Bijun Tang et al. Materials Today
- Capturing chemical intuition in synthesis of metal-organic frameworks
- (2019) Seyed Mohamad Moosavi et al. Nature Communications
- Challenges and opportunities of polymer design with machine learning and high throughput experimentation
- (2019) Jatin N. Kumar et al. MRS Communications
- Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
- (2019) Stephen Wu et al. npj Computational Materials
- Optimal machine learning models for robust materials classification using ToF-SIMS data
- (2019) Robert M.T. Madiona et al. APPLIED SURFACE SCIENCE
- Data‐Driven Materials Science: Status, Challenges, and Perspectives
- (2019) Lauri Himanen et al. Advanced Science
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- High-performance fiber reinforced concrete as a repairing material to normal concrete structures: Experiments, numerical simulations and a machine learning-based prediction model
- (2019) Pengcheng Jiao et al. CONSTRUCTION AND BUILDING MATERIALS
- Linking synthesis and structure descriptors from a large collection of synthetic records of zeolite materials
- (2019) Koki Muraoka et al. Nature Communications
- Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
- (2019) Yan Zhang et al. ACTA MATERIALIA
- Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene
- (2018) Arunkumar Chitteth Rajan et al. CHEMISTRY OF MATERIALS
- Predicting the thermodynamic stability of perovskite oxides using machine learning models
- (2018) Wei Li et al. COMPUTATIONAL MATERIALS SCIENCE
- Liquid electrolyte informatics using an exhaustive search with linear regression
- (2018) Keitaro Sodeyama et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- MoleculeNet: a benchmark for molecular machine learning
- (2018) Zhenqin Wu et al. Chemical Science
- Predicting phase behavior of grain boundaries with evolutionary search and machine learning
- (2018) Qiang Zhu et al. Nature Communications
- Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study
- (2018) Won-Mi Choi et al. npj Computational Materials
- Crystal structure prediction accelerated by Bayesian optimization
- (2018) Tomoki Yamashita et al. PHYSICAL REVIEW MATERIALS
- Machine leaning aided study of sintered density in Cu-Al alloy
- (2018) Zhenghua Deng et al. COMPUTATIONAL MATERIALS SCIENCE
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Inverse molecular design using machine learning: Generative models for matter engineering
- (2018) Benjamin Sanchez-Lengeling et al. SCIENCE
- Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
- (2018) Shuaihua Lu et al. Nature Communications
- Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
- (2018) Kamal Choudhary et al. PHYSICAL REVIEW MATERIALS
- Combinatorial Material Science and Strain Engineering Enabled by Pulsed Laser Deposition Using Radially Segmented Targets
- (2018) Max Kneiß et al. ACS Combinatorial Science
- Deep-learning-based inverse design model for intelligent discovery of organic molecules
- (2018) Kyungdoc Kim et al. npj Computational Materials
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods
- (2017) Fleur Legrain et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches
- (2017) Natalia Kireeva et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Machine-learned and codified synthesis parameters of oxide materials
- (2017) Edward Kim et al. Scientific Data
- A hybrid organic-inorganic perovskite dataset
- (2017) Chiho Kim et al. Scientific Data
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- A universal strategy for the creation of machine learning-based atomistic force fields
- (2017) Tran Doan Huan et al. npj Computational Materials
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
- (2016) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Report on the sixth blind test of organic crystal structure prediction methods
- (2016) Anthony M. Reilly et al. Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials
- Machine learning bandgaps of double perovskites
- (2016) G. Pilania et al. Scientific Reports
- Materials Informatics: The Materials “Gene” and Big Data
- (2015) Krishna Rajan Annual Review of Materials Research
- Machine learning for quantum mechanics in a nutshell
- (2015) Matthias Rupp INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Informatics-Aided Density Functional Theory Study on the Li Ion Transport of Tavorite-Type LiMTO4F (M3+–T5+, M2+–T6+)
- (2015) Randy Jalem et al. Journal of Chemical Information and Modeling
- Learning scheme to predict atomic forces and accelerate materials simulations
- (2015) V. Botu et al. PHYSICAL REVIEW B
- Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
- (2015) Zhenwei Li et al. PHYSICAL REVIEW LETTERS
- Big Data of Materials Science: Critical Role of the Descriptor
- (2015) Luca M. Ghiringhelli et al. PHYSICAL REVIEW LETTERS
- Properties of Schottky Barrier Diodes on (InxGa1–x)2O3 for 0.01 ≤ x ≤ 0.85 Determined by a Combinatorial Approach
- (2015) H. von Wenckstern et al. ACS Combinatorial Science
- Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
- (2014) Wladislaw Waag et al. JOURNAL OF POWER SOURCES
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information
- (2014) I Amelio et al. Cell Death & Disease
- CMAP: Complement Map Database
- (2013) Kun Yang et al. BIOINFORMATICS
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis
- (2012) V. Muralidharan et al. APPLIED SOFT COMPUTING
- e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design
- (2012) Emilie Pihan et al. BIOINFORMATICS
- Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
- (2012) W. G. Touw et al. BRIEFINGS IN BIOINFORMATICS
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Finding Density Functionals with Machine Learning
- (2012) John C. Snyder et al. PHYSICAL REVIEW LETTERS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Designing a BIM-based serious game for fire safety evacuation simulations
- (2011) Uwe Rüppel et al. ADVANCED ENGINEERING INFORMATICS
- Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
- (2011) Thomas F. Willems et al. MICROPOROUS AND MESOPOROUS MATERIALS
- Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery
- (2011) F. Zhu et al. NUCLEIC ACIDS RESEARCH
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
- (2010) Geoffroy Hautier et al. CHEMISTRY OF MATERIALS
- Advances in computational methods to predict the biological activity of compounds
- (2010) Chanin Nantasenamat et al. Expert Opinion on Drug Discovery
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Determination of the glass transition temperature
- (2009) John M. Hutchinson JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
- Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential
- (2008) Jörg Behler et al. PHYSICAL REVIEW LETTERS
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