References
[1]
Hattrick-Simpers
J R,
Gregoire
J M,
Kusne
A G.
Perspective: Composition-structure-property mapping in high-throughput experiments: Turning data into knowledge.
APL Mater,
2016, 4: 053211
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Perspective: Composition-structure-property mapping in high-throughput experiments: Turning data into knowledge&author=Hattrick-Simpers J R&author=Gregoire J M&author=Kusne A G&publication_year=2016&journal=APL Mater&volume=4&pages=053211
[2]
Butler
K T,
Davies
D W,
Cartwright
H, et al.
Machine learning for molecular and materials science.
Nature,
2018, 559: 547-555
CrossRef
PubMed
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Machine learning for molecular and materials science&author=Butler K T&author=Davies D W&author=Cartwright H&publication_year=2018&journal=Nature&volume=559&pages=547-555
[3]
Agrawal
A,
Choudhary
A.
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science.
APL Mater,
2016, 4: 053208
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science&author=Agrawal A&author=Choudhary A&publication_year=2016&journal=APL Mater&volume=4&pages=053208
[4]
Hill
J,
Mulholland
G,
Persson
K, et al.
Materials science with large-scale data and informatics: Unlocking new opportunities.
MRS Bull,
2016, 41: 399-409
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Materials science with large-scale data and informatics: Unlocking new opportunities&author=Hill J&author=Mulholland G&author=Persson K&publication_year=2016&journal=MRS Bull&volume=41&pages=399-409
[5]
Schmidt
J,
Marques
M R G,
Botti
S, et al.
Recent advances and applications of machine learning in solid-state materials science.
npj Comput Mater,
2019, 5: 83
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Recent advances and applications of machine learning in solid-state materials science&author=Schmidt J&author=Marques M R G&author=Botti S&publication_year=2019&journal=npj Comput Mater&volume=5&pages=83
[6]
Ramprasad
R,
Batra
R,
Pilania
G, et al.
Machine learning in materials informatics: Recent applications and prospects.
npj Comput Mater,
2017, 3: 54
CrossRef
ADS
arXiv
Google Scholar
http://scholar.google.com/scholar_lookup?title=Machine learning in materials informatics: Recent applications and prospects&author=Ramprasad R&author=Batra R&author=Pilania G&publication_year=2017&journal=npj Comput Mater&volume=3&pages=54
[7]
Tetko
I V,
Maran
U,
Tropsha
A.
Public (Q)SAR services, integrated modeling environments, and model repositories on the web: State of the art and perspectives for future development.
Mol Inf,
2017, 36: 1600082
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Public (Q)SAR services, integrated modeling environments, and model repositories on the web: State of the art and perspectives for future development&author=Tetko I V&author=Maran U&author=Tropsha A&publication_year=2017&journal=Mol Inf&volume=36&pages=1600082
[8]
Kalidindi
S R,
Brough
D B,
Li
S, et al.
Role of materials data science and informatics in accelerated materials innovation.
MRS Bull,
2016, 41: 596-602
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Role of materials data science and informatics in accelerated materials innovation&author=Kalidindi S R&author=Brough D B&author=Li S&publication_year=2016&journal=MRS Bull&volume=41&pages=596-602
[9]
Correa-Baena
J P,
Hippalgaonkar
K,
van Duren
J, et al.
Accelerating materials development via automation, machine learning, and high-performance computing.
Joule,
2018, 2: 1410-1420
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Accelerating materials development via automation, machine learning, and high-performance computing&author=Correa-Baena J P&author=Hippalgaonkar K&author=van Duren J&publication_year=2018&journal=Joule&volume=2&pages=1410-1420
[10]
Brunton
S L,
Kutz
J N.
Methods for data-driven multiscale model discovery for materials.
J Phys Mater,
2019, 2: 044002
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Methods for data-driven multiscale model discovery for materials&author=Brunton S L&author=Kutz J N&publication_year=2019&journal=J Phys Mater&volume=2&pages=044002
[11]
Schleder G R, Padilha A C M, Acosta C M, et al. From DFT to machine learning: Recent approaches to materials science–A review. J Phys Mater, 2019, 2: 032001.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Schleder G R, Padilha A C M, Acosta C M, et al. From DFT to machine learning: Recent approaches to materials science–A review. J Phys Mater, 2019, 2: 032001&
[12]
Raccuglia
P,
Elbert
K C,
Adler
P D F, et al.
Machine-learning-assisted materials discovery using failed experiments.
Nature,
2016, 533: 73-76
CrossRef
PubMed
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Machine-learning-assisted materials discovery using failed experiments&author=Raccuglia P&author=Elbert K C&author=Adler P D F&publication_year=2016&journal=Nature&volume=533&pages=73-76
[13]
Yu
Y,
Tan
X,
Ning
S, et al.
Machine learning for understanding compatibility of organic-inorganic hybrid perovskites with post-treatment amines.
ACS Energy Lett,
2019, 4: 397-404
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Machine learning for understanding compatibility of organic-inorganic hybrid perovskites with post-treatment amines&author=Yu Y&author=Tan X&author=Ning S&publication_year=2019&journal=ACS Energy Lett&volume=4&pages=397-404
[14]
Ward
L,
Wolverton
C.
Atomistic calculations and materials informatics: A review.
Curr Opin Solid State Mater Sci,
2017, 21: 167-176
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Atomistic calculations and materials informatics: A review&author=Ward L&author=Wolverton C&publication_year=2017&journal=Curr Opin Solid State Mater Sci&volume=21&pages=167-176
[15]
Belsky
A,
Hellenbrandt
M,
Karen
V L, et al.
New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design.
Acta Cryst Sect A Found Cryst,
2002, 58: 364-369
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design&author=Belsky A&author=Hellenbrandt M&author=Karen V L&publication_year=2002&journal=Acta Cryst Sect A Found Cryst&volume=58&pages=364-369
[16]
Allen F H. The cambridge structural database: A quarter of a million crystal structures and rising. Acta Cryst Sect B Struct Sci Cryst Eng Mater, 2002, 58: 380−388.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Allen F H. The cambridge structural database: A quarter of a million crystal structures and rising. Acta Cryst Sect B Struct Sci Cryst Eng Mater, 2002, 58: 380−388&
[17]
Gražulis
S,
Chateigner
D,
Downs
R T, et al.
Crystallography Open Database – An open-access collection of crystal structures.
J Appl Crystlogr,
2009, 42: 726-729
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Crystallography Open Database – An open-access collection of crystal structures&author=Gražulis S&author=Chateigner D&author=Downs R T&publication_year=2009&journal=J Appl Crystlogr&volume=42&pages=726-729
[18]
Villars
P,
Berndt
M,
Brandenburg
K, et al.
The pauling file, binaries edition.
J Alloys Compd,
2004, 367: 293-297
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=The pauling file, binaries edition&author=Villars P&author=Berndt M&author=Brandenburg K&publication_year=2004&journal=J Alloys Compd&volume=367&pages=293-297
[19]
Xu
Y,
Yamazaki
M,
Villars
P.
Inorganic materials database for exploring the nature of material.
Jpn J Appl Phys,
2011, 50: 11RH02
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Inorganic materials database for exploring the nature of material&author=Xu Y&author=Yamazaki M&author=Villars P&publication_year=2011&journal=Jpn J Appl Phys&volume=50&pages=11RH02
[20]
Kirklin
S,
Saal
J E,
Meredig
B, et al.
The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies.
npj Comput Mater,
2015, 1: 15010
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies&author=Kirklin S&author=Saal J E&author=Meredig B&publication_year=2015&journal=npj Comput Mater&volume=1&pages=15010
[21]
Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, et al. The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett, 2011, 2: 2241−2251.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, et al. The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett, 2011, 2: 2241−2251&
[22]
Curtarolo
S,
Setyawan
W,
Hart
G L W, et al.
AFLOW: An automatic framework for high-throughput materials discovery.
Comput Mater Sci,
2012, 58: 218-226
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=AFLOW: An automatic framework for high-throughput materials discovery&author=Curtarolo S&author=Setyawan W&author=Hart G L W&publication_year=2012&journal=Comput Mater Sci&volume=58&pages=218-226
[23]
Liu
Y,
Zhao
T,
Ju
W, et al.
Materials discovery and design using machine learning.
J Materiom,
2017, 3: 159-177
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Materials discovery and design using machine learning&author=Liu Y&author=Zhao T&author=Ju W&publication_year=2017&journal=J Materiom&volume=3&pages=159-177
[24]
Ward
L,
Agrawal
A,
Choudhary
A, et al.
A general-purpose machine learning framework for predicting properties of inorganic materials.
npj Comput Mater,
2016, 2: 16028
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=A general-purpose machine learning framework for predicting properties of inorganic materials&author=Ward L&author=Agrawal A&author=Choudhary A&publication_year=2016&journal=npj Comput Mater&volume=2&pages=16028
[25]
Peña
M A,
Fierro
J L G.
Chemical structures and performance of perovskite oxides.
Chem Rev,
2001, 101: 1981-2018
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Chemical structures and performance of perovskite oxides&author=Peña M A&author=Fierro J L G&publication_year=2001&journal=Chem Rev&volume=101&pages=1981-2018
[26]
Yin
W J,
Weng
B,
Ge
J, et al.
Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics.
Energy Environ Sci,
2019, 12: 442-462
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics&author=Yin W J&author=Weng B&author=Ge J&publication_year=2019&journal=Energy Environ Sci&volume=12&pages=442-462
[27]
Roth R S. Classification of perovskite and other ABO3-type compounds. J Res Nat Bur Stand, 1957, 58: 75−88.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Roth R S. Classification of perovskite and other ABO3-type compounds. J Res Nat Bur Stand, 1957, 58: 75−88&
[28]
Zhang
H,
Li
N,
Li
K, et al.
Structural stability and formability of ABO3-type perovskite compounds.
Acta Cryst Sect A Found Cryst,
2007, 63: 812-818
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Structural stability and formability of ABO3-type perovskite compounds&author=Zhang H&author=Li N&author=Li K&publication_year=2007&journal=Acta Cryst Sect A Found Cryst&volume=63&pages=812-818
[29]
Li
C,
Lu
X,
Ding
W, et al.
Formability of ABX3 (X = F, Cl, Br, I) halide perovskites.
Acta Crystlogr B Struct Sci,
2008, 64: 702-707
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Formability of ABX3 (X = F, Cl, Br, I) halide perovskites&author=Li C&author=Lu X&author=Ding W&publication_year=2008&journal=Acta Crystlogr B Struct Sci&volume=64&pages=702-707
[30]
Balachandran
P V,
Emery
A A,
Gubernatis
J E, et al.
Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory.
Phys Rev Mater,
2018, 2: 043802
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory&author=Balachandran P V&author=Emery A A&author=Gubernatis J E&publication_year=2018&journal=Phys Rev Mater&volume=2&pages=043802
[31]
Li
W,
Jacobs
R,
Morgan
D.
Predicting the thermodynamic stability of perovskite oxides using machine learning models.
Comput Mater Sci,
2018, 150: 454-463
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Predicting the thermodynamic stability of perovskite oxides using machine learning models&author=Li W&author=Jacobs R&author=Morgan D&publication_year=2018&journal=Comput Mater Sci&volume=150&pages=454-463
[32]
Xu
Q,
Li
Z,
Liu
M, et al.
Rationalizing perovskite data for machine learning and materials design.
J Phys Chem Lett,
2018, 9: 6948-6954
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Rationalizing perovskite data for machine learning and materials design&author=Xu Q&author=Li Z&author=Liu M&publication_year=2018&journal=J Phys Chem Lett&volume=9&pages=6948-6954
[33]
Lu
S,
Zhou
Q,
Ouyang
Y, et al.
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.
Nat Commun,
2018, 9: 3405
CrossRef
PubMed
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning&author=Lu S&author=Zhou Q&author=Ouyang Y&publication_year=2018&journal=Nat Commun&volume=9&pages=3405
[34]
Li
Z,
Xu
Q,
Sun
Q, et al.
Thermodynamic stability landscape of halide double perovskites via high-throughput computing and machine learning.
Adv Funct Mater,
2019, 29: 1807280
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Thermodynamic stability landscape of halide double perovskites via high-throughput computing and machine learning&author=Li Z&author=Xu Q&author=Sun Q&publication_year=2019&journal=Adv Funct Mater&volume=29&pages=1807280
[35]
Sun
Q,
Yin
W J.
Thermodynamic stability trend of cubic perovskites.
J Am Chem Soc,
2017, 139: 14905-14908
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Thermodynamic stability trend of cubic perovskites&author=Sun Q&author=Yin W J&publication_year=2017&journal=J Am Chem Soc&volume=139&pages=14905-14908
[36]
Tran
K,
Ulissi
Z W.
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.
Nat Catal,
2018, 1: 696-703
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution&author=Tran K&author=Ulissi Z W&publication_year=2018&journal=Nat Catal&volume=1&pages=696-703
[37]
Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel T D, et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater, 2016, 15: 1120−1127.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel T D, et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater, 2016, 15: 1120−1127&
[38]
Warmuth
M K,
Liao
J,
Rätsch
G, et al.
Active learning with support vector machines in the drug discovery process.
J Chem Inf Comput Sci,
2003, 43: 667-673
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Active learning with support vector machines in the drug discovery process&author=Warmuth M K&author=Liao J&author=Rätsch G&publication_year=2003&journal=J Chem Inf Comput Sci&volume=43&pages=667-673
[39]
Gubaev K, Podryabinkin E V, Shapeev A V. Machine learning of molecular properties: Locality and active learning. J Chem Phys, 2018, 148: 1−9.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Gubaev K, Podryabinkin E V, Shapeev A V. Machine learning of molecular properties: Locality and active learning. J Chem Phys, 2018, 148: 1−9&
[40]
Weng B, Song Z, Zhu R, et al. Symbolic regression discovery of new perovskite catalysts with high oxygen evolution reaction activity. 2019, arXiv:1908.06778.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Weng B, Song Z, Zhu R, et al. Symbolic regression discovery of new perovskite catalysts with high oxygen evolution reaction activity. 2019, arXiv:1908.06778&
[41]
Waag
W,
Fleischer
C,
Sauer
D U.
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles.
J Power Sources,
2014, 258: 321-339
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles&author=Waag W&author=Fleischer C&author=Sauer D U&publication_year=2014&journal=J Power Sources&volume=258&pages=321-339
[42]
Wu
L,
Fu
X,
Guan
Y.
Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies.
Appl Sci,
2016, 6: 166
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies&author=Wu L&author=Fu X&author=Guan Y&publication_year=2016&journal=Appl Sci&volume=6&pages=166
[43]
Severson
K A,
Attia
P M,
Jin
N, et al.
Data-driven prediction of battery cycle life before capacity degradation.
Nat Energy,
2019, 4: 383-391
CrossRef
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Data-driven prediction of battery cycle life before capacity degradation&author=Severson K A&author=Attia P M&author=Jin N&publication_year=2019&journal=Nat Energy&volume=4&pages=383-391
[44]
Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Trans Evol Comput, 1996, 1: 67.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Trans Evol Comput, 1996, 1: 67&
[45]
Wang
Y,
Wagner
N,
Rondinelli
J M.
Symbolic regression in materials science.
MRC Commun,
2019, 9: 793-805
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Symbolic regression in materials science&author=Wang Y&author=Wagner N&author=Rondinelli J M&publication_year=2019&journal=MRC Commun&volume=9&pages=793-805
[46]
Bartel
C J,
Sutton
C,
Goldsmith
B R, et al.
New tolerance factor to predict the stability of perovskite oxides and halides.
Sci Adv,
2019, 5: eaav0693
CrossRef
PubMed
ADS
arXiv
Google Scholar
http://scholar.google.com/scholar_lookup?title=New tolerance factor to predict the stability of perovskite oxides and halides&author=Bartel C J&author=Sutton C&author=Goldsmith B R&publication_year=2019&journal=Sci Adv&volume=5&pages=eaav0693
[47]
Bartel
C J,
Millican
S L,
Deml
A M, et al.
Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry.
Nat Commun,
2018, 9: 4168
CrossRef
PubMed
ADS
arXiv
Google Scholar
http://scholar.google.com/scholar_lookup?title=Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry&author=Bartel C J&author=Millican S L&author=Deml A M&publication_year=2018&journal=Nat Commun&volume=9&pages=4168
[48]
Jankowski N, Duch W, Grąbczewski K. Meta-learning in Computational Intelligence. Berlin: Springer, 2011.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Jankowski N, Duch W, Grąbczewski K. Meta-learning in Computational Intelligence. Berlin: Springer, 2011&
[49]
Graves A, Wayne G, Danihelka I. Neural turing machines. 2014, arXiv:1410.5401.
arXiv
Google Scholar
http://scholar.google.com/scholar_lookup?title=Graves A, Wayne G, Danihelka I. Neural turing machines. 2014, arXiv:1410.5401&
[50]
Duan Y, Andrychowicz M, Stadie B, et al. One-shot imitation learning. In: Guyon I, Luxburg U V, Bengio S, et al, eds. Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook: NIPS, 2017.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Duan Y, Andrychowicz M, Stadie B, et al. One-shot imitation learning. In: Guyon I, Luxburg U V, Bengio S, et al, eds. Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook: NIPS, 2017&
[51]
Lake
B M,
Salakhutdinov
R,
Tenenbaum
J B.
Human-level concept learning through probabilistic program induction.
Science,
2015, 350: 1332-1338
CrossRef
PubMed
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Human-level concept learning through probabilistic program induction&author=Lake B M&author=Salakhutdinov R&author=Tenenbaum J B&publication_year=2015&journal=Science&volume=350&pages=1332-1338
[52]
Sanchez-Lengeling
B,
Aspuru-Guzik
A.
Inverse molecular design using machine learning: Generative models for matter engineering.
Science,
2018, 361: 360-365
CrossRef
PubMed
ADS
Google Scholar
http://scholar.google.com/scholar_lookup?title=Inverse molecular design using machine learning: Generative models for matter engineering&author=Sanchez-Lengeling B&author=Aspuru-Guzik A&publication_year=2018&journal=Science&volume=361&pages=360-365
[53]
Zubatyuk
R,
Smith
J S,
Leszczynski
J, et al.
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.
Sci Adv,
2019, 5: eaav6490
CrossRef
PubMed
Google Scholar
http://scholar.google.com/scholar_lookup?title=Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network&author=Zubatyuk R&author=Smith J S&author=Leszczynski J&publication_year=2019&journal=Sci Adv&volume=5&pages=eaav6490