标题
Applied Machine Learning for Developing Next‐Generation Functional Materials
作者
关键词
-
出版物
ADVANCED FUNCTIONAL MATERIALS
Volume -, Issue -, Pages 2104195
出版商
Wiley
发表日期
2021-09-13
DOI
10.1002/adfm.202104195
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- 3D Deep Learning Enables Accurate Layer Mapping of 2D Materials
- (2021) Xingchen Dong et al. ACS Nano
- Amorphization mechanism of SrIrO3 electrocatalyst: How oxygen redox initiates ionic diffusion and structural reorganization
- (2021) Gang Wan et al. Science Advances
- Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning
- (2021) Xiaoyan Du et al. Joule
- Bayesian reaction optimization as a tool for chemical synthesis
- (2021) Benjamin J. Shields et al. NATURE
- Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
- (2021) Cigdem Altintas et al. Journal of Chemical Information and Modeling
- Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks
- (2021) Srinivasu Kancharlapalli et al. Journal of Chemical Theory and Computation
- Two-step machine learning enables optimized nanoparticle synthesis
- (2021) Flore Mekki-Berrada et al. npj Computational Materials
- Machine learning for materials discovery: Two-dimensional topological insulators
- (2021) Gabriel R. Schleder et al. Applied Physics Reviews
- Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning
- (2021) Peisong Yang et al. ACS Omega
- Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators
- (2021) Pengcheng Jiao Nano Energy
- Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
- (2020) Edward Kim et al. Journal of Chemical Information and Modeling
- FCHL revisited: Faster and more accurate quantum machine learning
- (2020) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- Closed-loop optimization of fast-charging protocols for batteries with machine learning
- (2020) Peter M. Attia et al. NATURE
- Designing solid-state electrolytes for safe, energy-dense batteries
- (2020) Qing Zhao et al. Nature Reviews Materials
- Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
- (2020) James E. Saal et al. Annual Review of Materials Research
- Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning
- (2020) Chuhong Wang et al. CHEMISTRY OF MATERIALS
- Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks
- (2020) Hong Wang et al. Journal of Chemical Information and Modeling
- Optimization of Heterogeneous Ternary Li3PO4-Li3BO3-Li2SO4 Mixture for Li-ion Conductivity by Machine Learning
- (2020) Kenji Homma et al. Journal of Physical Chemistry C
- Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning
- (2020) Hao Jin et al. Journal of Physical Chemistry Letters
- Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
- (2020) Zhisen Jiang et al. Nature Communications
- An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts
- (2020) Zheng Li et al. ACS Catalysis
- Heterogeneous Catalysts with Well‐Defined Active Metal Sites toward CO 2 Electrocatalytic Reduction
- (2020) Deren Yang et al. Advanced Energy Materials
- AI Feynman: A physics-inspired method for symbolic regression
- (2020) Silviu-Marian Udrescu et al. Science Advances
- Self-driving laboratory for accelerated discovery of thin-film materials
- (2020) B. P. MacLeod et al. Science Advances
- High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses
- (2020) Kamal Choudhary et al. npj Computational Materials
- Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery
- (2020) Cheol Woo Park et al. Physical Review Materials
- Discovery of Acid-Stable Oxygen Evolution Catalysts : High-throughput Computational Screening of Equimolar Bimetallic Oxides
- (2020) Seoin Back et al. ACS Applied Materials & Interfaces
- Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
- (2020) Kevin Maik Jablonka et al. CHEMICAL REVIEWS
- Robot-Accelerated Perovskite Investigation and Discovery
- (2020) Zhi Li et al. CHEMISTRY OF MATERIALS
- Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
- (2020) Raul A. Flores et al. CHEMISTRY OF MATERIALS
- Oxygen evolution electrocatalysis using mixed metal oxides under acidic conditions: Challenges and opportunities
- (2020) Xiang-Kui Gu et al. JOURNAL OF CATALYSIS
- A mobile robotic chemist
- (2020) Benjamin Burger et al. NATURE
- Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts
- (2020) Baicheng Weng et al. Nature Communications
- A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates
- (2020) Daniel S. Salley et al. ACS Central Science
- Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing
- (2020) Nathan C. Frey et al. ACS Nano
- Large family of two-dimensional ferroelectric metals discovered via machine learning
- (2020) Xing-Yu Ma et al. Science Bulletin
- Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set
- (2020) Sagi Eppel et al. ACS Central Science
- Acid-Stable Oxides for Oxygen Electrocatalysis
- (2020) Zhenbin Wang et al. ACS Energy Letters
- Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches
- (2020) Aaron Kirkey et al. ACS Applied Materials & Interfaces
- Enhancing the stability of organic photovoltaics through machine learning
- (2020) Tudur Wyn David et al. Nano Energy
- Engineering early prediction of supercapacitors’ cycle life using neural networks
- (2020) Jiahao Ren et al. Materials Today Energy
- Coupling machine learning with 3D bioprinting to fast track optimisation of extrusion printing
- (2020) Kalani Ruberu et al. Applied Materials Today
- Identifying Pb-free perovskites for solar cells by machine learning
- (2019) Jino Im et al. npj Computational Materials
- Data-driven prediction of battery cycle life before capacity degradation
- (2019) Kristen A. Severson et al. Nature Energy
- Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites
- (2019) Mikhail Askerka et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries
- (2019) Rajendra P. Joshi et al. ACS Applied Materials & Interfaces
- Molecular Dynamics Simulations of Ionic Liquids and Electrolytes Using Polarizable Force Fields
- (2019) Dmitry Bedrov et al. CHEMICAL REVIEWS
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- (2019) Chi Chen et al. CHEMISTRY OF MATERIALS
- Stable Two-Dimensional Materials for Oxygen Reduction and Oxygen Evolution Reactions
- (2019) Ankit Jain et al. ACS Energy Letters
- Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
- (2019) Felipe Oviedo et al. npj Computational Materials
- Identifying Active Sites for CO2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations
- (2019) Yalu Chen et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning
- (2019) Seoin Back et al. ACS Catalysis
- Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis
- (2019) Oleksandr Voznyy et al. ACS Nano
- Data-Driven Discovery of Full-Visible-Spectrum Phosphor
- (2019) Shuxing Li et al. CHEMISTRY OF MATERIALS
- Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing
- (2019) Young Hoon Jung et al. ADVANCED MATERIALS
- Unsupervised discovery of solid-state lithium ion conductors
- (2019) Ying Zhang et al. Nature Communications
- Crystal Structural Framework of Lithium Super‐Ionic Conductors
- (2019) Xingfeng He et al. Advanced Energy Materials
- Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
- (2019) Wenbo Sun et al. Science Advances
- Matminer: An open source toolkit for materials data mining
- (2018) Logan Ward et al. COMPUTATIONAL MATERIALS SCIENCE
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber et al. JOURNAL OF CHEMICAL PHYSICS
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- (2018) M. Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- A New Method for Determining the Concentration of Electrolyte Components in Lithium-Ion Cells, Using Fourier Transform Infrared Spectroscopy and Machine Learning
- (2018) L. D. Ellis et al. JOURNAL OF THE ELECTROCHEMICAL SOCIETY
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning
- (2018) Prasanna V. Balachandran et al. Nature Communications
- Recent Progress on Multimetal Oxide Catalysts for the Oxygen Evolution Reaction
- (2018) Ju Seong Kim et al. Advanced Energy Materials
- A convolutional neural network-based screening tool for X-ray serial crystallography
- (2018) Tsung-Wei Ke et al. JOURNAL OF SYNCHROTRON RADIATION
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Machine learning modeling of superconducting critical temperature
- (2018) Valentin Stanev et al. npj Computational Materials
- Artificial neural network enabled capacitance prediction for carbon-based supercapacitors
- (2018) Shan Zhu et al. MATERIALS LETTERS
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
- (2018) M.A. Janai et al. ORGANIC ELECTRONICS
- 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
- Phoenics: A Bayesian Optimizer for Chemistry
- (2018) Florian Häse et al. ACS Central Science
- Predictive modeling and design rules for solid electrolytes
- (2018) Gerbrand Ceder et al. MRS BULLETIN
- Identifying an efficient, thermally robust inorganic phosphor host via machine learning
- (2018) Ya Zhuo et al. Nature Communications
- High-Throughput Screening of Lead-Free Perovskite-like Materials for Optoelectronic Applications
- (2017) Ankit Jain et al. Journal of Physical Chemistry C
- Universal fragment descriptors for predicting properties of inorganic crystals
- (2017) Olexandr Isayev et al. Nature Communications
- Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)
- (2017) Maryam Pardakhti et al. ACS Combinatorial Science
- Strain-controlled electrocatalysis on multimetallic nanomaterials
- (2017) Mingchuan Luo et al. Nature Reviews Materials
- Electronic Structure Descriptor for the Discovery of Narrow-Band Red-Emitting Phosphors
- (2016) Zhenbin Wang et al. CHEMISTRY OF MATERIALS
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
- (2016) Dezhen Xue et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- The ReaxFF reactive force-field: development, applications and future directions
- (2016) Thomas P Senftle et al. npj Computational Materials
- Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning
- (2016) Bharat Medasani et al. npj Computational Materials
- Machine learning bandgaps of double perovskites
- (2016) G. Pilania et al. Scientific Reports
- The Chemical Space Project
- (2015) Jean-Louis Reymond ACCOUNTS OF CHEMICAL RESEARCH
- Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
- (2015) Olexandr Isayev et al. CHEMISTRY OF MATERIALS
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Big Data of Materials Science: Critical Role of the Descriptor
- (2015) Luca M. Ghiringhelli et al. PHYSICAL REVIEW LETTERS
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- (2013) James E. Saal et al. JOM
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Discovering crystals using shape matching and machine learning
- (2013) Carolyn L. Phillips et al. Soft Matter
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- AFLOW: An automatic framework for high-throughput materials discovery
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Gas Chromatography/Mass Spectrometry As a Suitable Tool for the Li-Ion Battery Electrolyte Degradation Mechanisms Study
- (2010) Grégory Gachot et al. ANALYTICAL CHEMISTRY
- Key challenges in future Li-battery research
- (2010) J.- M. Tarascon PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
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