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An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts

https://doi.org/10.1021/acscatal.9b05248
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The 52 checked references that resolve
resolves10.1038/s41524-017-0056-5
Machine learning in materials informatics: recent applications and prospects
resolves10.1038/s41524-019-0221-0
Recent advances and applications of machine learning in solid-state materials science
resolves10.1002/cctc.201900595
Machine Learning for Computational Heterogeneous Catalysis
resolves10.1103/PhysRevLett.120.145301
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
resolves10.1103/PhysRevLett.114.105503
Big Data of Materials Science: Critical Role of the Descriptor
resolves10.1103/PhysRevB.93.085142
Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression
resolves10.1002/qua.24917
Crystal structure representations for machine learning models of formation energies
resolves10.1021/acs.chemmater.7b00156
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
resolves10.1021/acs.jpclett.5b01660
Machine-Learning-Augmented Chemisorption Model for CO<sub>2</sub> Electroreduction Catalyst Screening
resolves10.1016/j.cattod.2016.04.013
Feature engineering of machine-learning chemisorption models for catalyst design
resolves10.1039/C7TA01812F
High-throughput screening of bimetallic catalysts enabled by machine learning
resolves10.1038/s41929-018-0142-1
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
resolves10.1038/ncomms14621
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
resolves10.1103/PhysRevB.89.205118
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
resolves10.1038/s41524-019-0162-7
Solving the electronic structure problem with machine learning
resolves10.1038/srep19375
Machine learning bandgaps of double perovskites
resolves10.1021/acs.jpca.8b02842
Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations
resolves10.1038/s41467-018-05761-w
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
resolves10.1038/npjcompumats.2016.28
A general-purpose machine learning framework for predicting properties of inorganic materials
resolves10.1007/978-0-387-84858-7
The Elements of Statistical Learning
resolves10.1038/nature14541
Probabilistic machine learning and artificial intelligence
resolves10.1038/s41524-019-0153-8
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
resolves10.1073/pnas.1607412113
Accelerated search for BaTiO <sub>3</sub> -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
resolves10.1038/s41524-018-0129-0
Active learning for accelerated design of layered materials
resolves10.1038/ncomms11241
Accelerated search for materials with targeted properties by adaptive design
resolves10.1126/science.1212858
A Perovskite Oxide Optimized for Oxygen Evolution Catalysis from Molecular Orbital Principles
resolves10.1038/ncomms3439
Double perovskites as a family of highly active catalysts for oxygen evolution in alkaline solution
resolves10.1126/science.1215081
Optimizing Perovskites for the Water-Splitting Reaction
resolves10.1126/science.aaf5050
A highly active and stable IrO <i> <sub>x</sub> </i> /SrIrO <sub>3</sub> catalyst for the oxygen evolution reaction
resolves10.1039/C6CS00328A
Electrocatalysis for the oxygen evolution reaction: recent development and future perspectives
resolves10.1073/pnas.0603395103
Powering the planet: Chemical challenges in solar energy utilization
resolves10.1038/nmat4778
Materials for solar fuels and chemicals
resolves10.1088/1468-6996/16/3/036002
Perovskite-type catalytic materials for environmental applications
resolves10.1016/j.cattod.2017.11.007
Pd doped CaCo Zr1-O3 perovskites for automotive emissions control
resolves10.1002/cssc.201900644
CaCo<sub><i>x</i></sub>Zr<sub>1−<i>x</i></sub>O<sub>3−<i>δ</i></sub> Perovskites as Oxygen‐Selective Sorbents for Air Separation
resolves10.1126/science.aam7092
Perovskites in catalysis and electrocatalysis
resolves10.1038/nmat3568
The high-throughput highway to computational materials design
resolves10.1038/nchem.1069
Design principles for oxygen-reduction activity on perovskite oxide catalysts for fuel cells and metal–air batteries
resolves10.1002/cctc.201000397
Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces
resolves10.1016/j.jelechem.2006.11.008
Electrolysis of water on oxide surfaces
resolves10.1021/acs.jpcc.5b10071
Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation
resolves10.1021/jp511426q
A Linear Response DFT+<i>U</i> Study of Trends in the Oxygen Evolution Activity of Transition Metal Rutile Dioxides
resolves10.1103/PhysRevB.75.195128
Ground-state properties of multivalent manganese oxides: Density functional and hybrid density functional calculations
resolves10.1039/C5CP02834E
Ab initio GGA+U study of oxygen evolution and oxygen reduction electrocatalysis on the (001) surfaces of lanthanum transition metal perovskites LaBO <sub>3</sub> (B = Cr, Mn, Fe, Co and Ni)
resolves10.1038/ncomms15679
Universal fragment descriptors for predicting properties of inorganic crystals
resolves10.1038/srep34256
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
resolves10.1016/j.commatsci.2012.10.028
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
resolves10.3389/fmats.2016.00019
Finding New Perovskite Halides via Machine Learning
resolves10.1002/9781118892114
Fundamental Concepts in Heterogeneous Catalysis
resolves10.1201/b16018
Bayesian Data Analysis
resolves10.1002/9780470022184
Handbook of Magnetism and Advanced Magnetic Materials
resolves10.1214/aoms/1177729694
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