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The screening of novel materials is an important topic in the field of materials science.
Bayesian layers: A module for neural network uncertainty
Tran, D., Dusenberry, M. W., van der Wilk, A. M. & Hafner, D · 1911
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Cluster analysis of multivariate data: efficiency versus interpretability of classifications
Forgy, E. W · 1965
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Dynamics of solid lubrication as observed by optical microscopy
Sliney, H. E · 1978
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A practical bayesian framework for backprop networks
MacKay, D. J. C · 1992
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Norm-conserving and ultrasoft pseudopotentials for first-row and transition elements
Kresse, G. & Hafner, J · 1994
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Probable networks and plausible predictions - a review of practical bayesian methods for supervised neural networks
MacKay, D. J. C · 1995
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Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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Microscopic determination of the interlayer binding energy in graphite
Benedict, L. X. et al · 1998
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Linear scaling electronic structure methods
Goedecker, S · 1999
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From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G. & Joubert, D · 1999
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Robust qsar models using bayesian regularized artificial neural networks
Burden, F. R. & Winkler, D. A · 1999
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Van der Waals density functional for layered structures
Rydberg, H. et al · 2003
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http://link.springer.de (2003)
Landolt-Börnstein · 2003
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van der Waals layered materials: opportunities and challenges
Zacharia, R., Ulbricht, H. & Hertel, T · 2004
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Torque and twist against superlubricity
Filippov, A. E., Dienwiebel, M., Frenken, J. W. M., Kla, J. & Urbakh, M · 2008
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Comparison of bayesian networks and artificial neural networks for quality detection in a machining process
Correa, M., Bielza, C. & Pamies-Teixeirac, J · 2009
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Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
Tkatchenko, A. & Scheffler, M · 2009
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Cohesive properties and asymptotics of the dispersion interaction in graphite by the random phase approximation
Lebégue, S. et al · 2010
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Nature and strength of interlayer binding in graphite
Spanu, L., Sorella, S. & Galli, G · 2010
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Tunable metal-insulator transition in double-layer graphene heterostructures
Ponomarenko, L. A. et al · 2011
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Stretching and Breaking of Ultrathin MoS 2
Bertolazzi, S., Brivio, J. & Kis, A · 2011
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Interlayer binding energy of graphite - a direct experimental determination
Liu, Z., Liu, J. Z. & et al., Y. C · 2011
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Field-effect tunneling transistor based on vertical graphene heterostructures
Britnell, L. et al · 2012
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Cross-sectional imaging of individual layers and buried interfaces of graphene-based heterostructures and superlattices
Haigh, S. J. et al · 2012
Beware of R 2 : correct statistical usage in qsar and qspr studies
Alexander, D., Tropsha, A. & Winkler, D. A · 2015
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Measurement of the cleavage energy of graphite
Wang, W. et al · 2015
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2d materials and van der Waals heterostructures
Novoselov, K. S., Mishchenko, A., Carvalho, A. & Neto, A. H. C · 2016
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Mechanical properties of two-dimensional materials and heterostructures
Liu, K. & Wu, J · 2016
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The thermodynamic scale of inorganic crystalline metastability
Sun, W. et al · 2016
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Computational discovery of stable M 2 AX phases
Ashton, M., Hennig, R. G., Broderick, S. R., Rajan, K. & Sinnott, S. B · 2016
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Cited alongside, same era.
O(n) methods in electronic structure calculations
Bowler, D. & Miyazaki, T · 2012
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van der Waals bonding in layered compounds from advanced density-functional first-principles calculations
Björkman, T., Gulans, A., Krasheninnikov, A. V. & Nieminen, R. M · 2012
Cited alongside, same era.
Are we van der Waals ready?
Björkman, T., Gulans, A., Krasheninnikov, A. V. & Nieminen, R. M · 2012
Cited alongside, same era.
Improved description of soft layered materials with van der Waals density functional theory
Graziano, G., Klimeš, J., Fernandez-Alonso, F. & Michaelides, A · 2012
Cited alongside, same era.
Van der Waals heterostructures
Geim, A. K. & Grigorieva, I. V · 2013
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The materials project: A materials genome approach to accelerating materials innovation
Jain, A. et al · 2013
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. & Ghahramani, Z · 2016
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Twisted MX 2 /MoS 2 heterobilayers: effect of van der Waals interaction on the electronic structure
Lu, N. et al · 2017
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Superlubricity of a graphene/MoS 2 heterostructure: a combined experimental and dft study
Wang, L. et al · 2017
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Universal fragment descriptors for predicting properties of inorganic crystals
Isayev, O · 2017
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Topology-scaling algorithm for bonded networks
Ashton, M., Paul, J., Sinnott, S. B. & Hennig, R. G · 2017
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Ultralow interlayer friction of layered electride Ca 2 N: A potential two-dimensional solid lubricant material
Wang, J. et al · 2018
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A · 2018
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Feature selection in machine learning:a new perspective
Cai, J., Luo, J., Wang, S. & Yang, S · 2018
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Stress and strain effects on the electronic structure and optical properties of scn monolayer
Tamleh, S., Rezaei, G. & Jalilian, J. · 2018
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2dmatpedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches
Zhou, J. et al · 2019
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Efficient prediction of structural and electronic properties of hybrid 2d materials using complementary DFT and machine learning approaches
Tawfik, S. A., Isayev, O., Stampfl, C., Shapter, J., Winkler, D. A., & Ford, M. J · 2019
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