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We study property prediction for crystal materials.
Über elektrostatische. gitterpotentiale
Born, M · 1921
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Die berechnung optischer und elektrostatischer gitterpotentiale
Ewald, P. P · 1921
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Cohesion at a crystal surface
Lennard-Jones, J. and Dent, B. M · 1928
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The normal state of helium
Slater, J. C · 1928
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Diatomic molecules according to the wave mechanics. ii. vibrational levels
Morse, P. M · 1929
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The classical equation of state of gaseous helium, neon and argon
Buckingham, R. A · 1938
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Tables of integral transforms [volumes I & II] , volume 1
Bateman, H · 1954
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On epstein’s zeta-function
Selberg, A. and Chowla, S · 1967
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Bessel series expansions of the epstein zeta function and the functional equation
Terras, A. A · 1973
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Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
Daw, M. S. and Baskes, M. I · 1984
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Convergence of lattice sums and madelung’s constant
Borwein, D., Borwein, J. M., and Taylor, K. F · 1985
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Application of the embedded-atom method to covalent materials: a semiempirical potential for silicon
Baskes, M · 1987
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Elementary function expansions for madelung constants
Crandall, R. E. and Buhler, J. P · 1987
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Basic Solid State Chemistry
West, A. R · 1988
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Introductory nuclear physics
Krane, K. S · 1991
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The embedded-atom method: a review of theory and applications
Daw, M. S., Foiles, S. M., and Baskes, M. I · 1993
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Generalized multidimensional epstein zeta functions
Kirsten, K · 1994
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Fast evaluation of epstein zeta functions
Crandall, R. E · 1998
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GNU scientific library
Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Alken, P., Booth, M., Rossi, F., and Ulerich, R · 2002
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 2003
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Complex analysis
Kung, J. P. and Yang, C.-C · 2003
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Computing riemann theta functions
Deconinck, B., Heil, M., Bobenko, A., Van Hoeij, M., and Schmies, M · 2004
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. and Parrinello, M · 2007
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Incomplete bessel functions. i
Jones, D · 2007
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Incomplete bessel, generalized incomplete gamma, or leaky aquifer functions
Harris, F. E · 2008
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Uniform bounds for the incomplete complementary gamma function
Borwein, J. and Chan, O.-Y · 2009
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Ewald summation for coulomb interactions in a periodic supercell
Lee, H. and Cai, W · 2009
Cited alongside, same era.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J., Giri, S., Margraf, J. T., and Günnemann, S · 2011
Cited alongside, same era.
Computation of tail probabilities via extrapolation methods and connection with rational and padé approximants
Gaudreau, P., Slevinsky, R. M., and Safouhi, H · 2012
Cited alongside, same era.
Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R., and Von Lilienfeld, O. A · 2012
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Smith, L. N. and Topin, N · 2019
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The joint automated repository for various integrated simulations (jarvis) for data-driven materials design
Choudhary, K., Garrity, K. F., Reid, A. C., DeCost, B., Biacchi, A. J., Hight Walker, A. R., Trautt, Z., Hattrick-Simpers, J., Kusne, A. G., Centrone, A., et al · 2020
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Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Goodall, R. E. and Lee, A. A · 2020
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Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., and Dror, R · 2020
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Deep learning of high-order interactions for protein interface prediction
Liu, Y., Yuan, H., Cai, L., and Ji, S · 2020
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Probability and information theory, with applications to radar: international series of monographs on electronics and instrumentation , volume 3
Woodward, P. M · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Cited alongside, same era.
Systematic analysis and modification of embedded-atom potentials: case study of copper
Jalkanen, J. and Müser, M. H · 2015
Cited alongside, same era.
Fast ewald summation based on nfft with mixed periodicity
Nestler, F., Pippig, M., and Potts, D · 2015
Cited alongside, same era.
London dispersion in molecular chemistry—reconsidering steric effects
Wagner, J. P. and Schreiner, P. R · 2015
Cited alongside, same era.
The chemical bond in inorganic chemistry: the bond valence model , volume 27
Brown, I. D · 2016
Cited alongside, same era.
A modified embedded-atom method interatomic potential for ionic systems: 2 nnmeam+ qeq
Lee, E., Lee, K.-R., Baskes, M., and Lee, B.-J · 2016
Cited alongside, same era.
Graph convolutional neural networks with global attention for improved materials property prediction
Louis, S.-Y., Zhao, Y., Nasiri, A., Wang, X., Song, Y., Liu, F., and Hu, J · 2020
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A deep learning approach to antibiotic discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al · 2020
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A variational principle for gaussian lattice sums
Bétermin, L., Faulhuber, M., and Steinerberger, S · 2021
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Atomistic line graph neural network for improved materials property predictions
Choudhary, K. and DeCost, B · 2021
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Topology-aware graph pooling networks
Gao, H., Liu, Y., and Ji, S · 2021
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Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F., and Günnemann, S · 2021
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OGB-LSC: A large-scale challenge for machine learning on graphs
Hu, W., Fey, M., Ren, H., Nakata, M., Dong, Y., and Leskovec, J · 2021
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The influence of coulomb interaction screening on the excitons in disordered two-dimensional insulators
Kirichenko, E. and Stephanovich, V · 2021
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GraphDF: A discrete flow model for molecular graph generation
Luo, Y., Yan, K., and Ji, S · 2021
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Crystal graph attention networks for the prediction of stable materials
Schmidt, J., Pettersson, L., Verdozzi, C., Botti, S., and Marques, M. A. L · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O., and Gastegger, M · 2021
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Compositionally restricted attention-based network for materials property predictions
Wang, A. Y.-T., Kauwe, S. K., Murdock, R. J., and Sparks, T. D · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
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Equivariant diffusion for molecule generation in 3D
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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An autoregressive flow model for 3D molecular geometry generation from scratch
Luo, Y. and Ji, S · 2022
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Scalable deeper graph neural networks for high-performance materials property prediction
Omee, S. S., Louis, S.-Y., Fu, N., Wei, L., Dey, S., Dong, R., Li, Q., and Hu, J · 2022
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A recursive algorithm for an efficient and accurate computation of incomplete bessel functions
Slevinsky, R. M. and Safouhi, H · 2022
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Periodic graph transformers for crystal material property prediction
Yan, K., Liu, Y., Lin, Y., and Ji, S · 2022
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Ewald-based long-range message passing for molecular graphs
Kosmala, A., Gasteiger, J., Gao, N., and Günnemann, S · 2023
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Learning hierarchical protein representations via complete 3d graph networks
Wang, L., Liu, H., Liu, Y., Kurtin, J., and Ji, S · 2023
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