Crossref journal-article
American Chemical Society (ACS)
ACS Catalysis (316)
Bibliography

Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., Palizhati, A., Sriram, A., Wood, B., Yoon, J., Parikh, D., Zitnick, C. L., & Ulissi, Z. (2021). Open Catalyst 2020 (OC20) Dataset and Community Challenges. ACS Catalysis, 11(10), 6059–6072.

Authors 17
  1. Lowik Chanussot (first)
  2. Abhishek Das (additional)
  3. Siddharth Goyal (additional)
  4. Thibaut Lavril (additional)
  5. Muhammed Shuaibi (additional)
  6. Morgane Riviere (additional)
  7. Kevin Tran (additional)
  8. Javier Heras-Domingo (additional)
  9. Caleb Ho (additional)
  10. Weihua Hu (additional)
  11. Aini Palizhati (additional)
  12. Anuroop Sriram (additional)
  13. Brandon Wood (additional)
  14. Junwoong Yoon (additional)
  15. Devi Parikh (additional)
  16. C. Lawrence Zitnick (additional)
  17. Zachary Ulissi (additional)
References 103 Referenced 423
  1. {'volume-title': 'Global Energy Outlook 2020: Energy Transition or Energy Addition? With Commentary on Implications of the COVID-19 Pandemic', 'year': '2020', 'author': 'Newell R. G.', 'key': 'ref1/cit1'} / Global Energy Outlook 2020: Energy Transition or Energy Addition? With Commentary on Implications of the COVID-19 Pandemic by Newell R. G. (2020)
  2. {'volume-title': 'Annual Energy Outlook 2020', 'year': '2020', 'key': 'ref2/cit2'} / Annual Energy Outlook 2020 (2020)
  3. Nørskov, J. K.; Studt, F.; Abild-Pedersen, F.; Bligaard, T. Fundamental Concepts in Heterogeneous Catalysis; John Wiley & Sons, 2014; pp 1–4. (10.1002/9781118892114)
  4. 10.1126/science.aad4998
  5. 10.1002/anie.201208487 / The Catalyst Genome by Nørskov J. K. (2013)
  6. Sholl, D. S.; Steckel, J. A. Density Functional Theory; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2009; pp 1–31. (10.1002/9780470447710)
  7. 10.1021/acscatal.9b01234
  8. 10.1021/acscatal.8b01708
  9. 10.1038/ncomms14621
  10. 10.1039/c9me00078j
  11. 10.1039/c7re00210f
  12. 10.1016/j.energy.2019.116091
  13. 10.1021/acs.jpclett.9b03657
  14. 10.1007/s40192-018-0108-9
  15. 10.1088/2632-2153/ab7e1a
  16. 10.1038/s41929-018-0142-1
  17. 10.1021/acs.jpclett.0c00634
  18. 10.1021/acs.jpclett.9b01428
  19. 10.1021/acs.chemmater.9b03686
  20. 10.1016/j.chempr.2020.09.001
  21. 10.1021/jacs.7b11239
  22. 10.1016/j.cad.2019.05.038
  23. 10.3389/fbuil.2020.00059
  24. 10.1038/s41524-019-0221-0
  25. 10.1007/978-3-030-29829-6_2 / Impact: Design with All Senses by Aksöz Z. (2020)
  26. 10.1080/08927022.2016.1274984
  27. 10.1016/j.cpc.2016.05.010
  28. 10.1063/1.4960708
  29. 10.1021/acs.jctc.9b00465
  30. 10.1021/acscatal.9b03599
  31. 10.1038/s41929-018-0056-y
  32. 10.1038/s41929-018-0150-1
  33. 10.1002/aic.16198
  34. 10.1002/cctc.201900595 / ChemCatChem by Schlexer Lamoureux P. (2019)
  35. 10.3390/catal7100306
  36. 10.1038/s41578-018-0005-z
  37. 10.1002/aenm.201903242
  38. 10.1088/2515-7655/ab2060
  39. 10.1039/c9ta02356a
  40. 10.1021/acscatal.9b04186
  41. 10.1002/adma.201907865
  42. 10.1002/cctc.201900595
  43. 10.1002/cctc.201900595
  44. 10.1016/j.commatsci.2012.02.002
  45. 10.1038/npjcompumats.2015.10 / npj Comput. Mater. by Kirklin S. (2015)
  46. 10.1002/anie.201402958
  47. 10.1103/physrevlett.99.016105
  48. 10.1103/physrevlett.118.036101
  49. 10.1039/c7sc03422a
  50. 10.1021/acscatal.8b04478
  51. 10.1016/j.susc.2018.11.019
  52. 10.1039/c7ta01812f
  53. 10.1016/j.joule.2018.12.015
  54. 10.1038/s41597-019-0080-z
  55. 10.1038/s41597-019-0081-y / Sci. Data by Winther K. T. (2019)
  56. Deng, J.; Dong, W.; Socher, R.; Li, L.J.; Li, K.; Fei-Fei, L. Imagenet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition; IEEE, 2009; pp 248–255. (10.1109/CVPR.2009.5206848)
  57. Panayotov, V.; Chen, G.; Povey, D.; Khudanpur, S. Librispeech: an asr corpus based on public domain audio books. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); IEEE, 2015; pp 5206–5210. (10.1109/ICASSP.2015.7178964)
  58. Antol, S.; Agrawal, A.; Lu, J.; Mitchell, M.; Batra, D.; Lawrence Zitnick, C.; Parikh, D. Vqa: Visual question answering. Proceedings of the IEEE International Conference on Computer Vision; IEEE, 2015; pp 2425–2433. (10.1109/ICCV.2015.279)
  59. 10.1063/1.4812323
  60. 10.1016/j.commatsci.2005.04.010
  61. Bader, R.; Bader, R. Atoms In Molecules: A Quantum Theory; International Series of Monographs on Chemistry; Clarendon Press, 1994; pp 13–52.
  62. 10.1038/s41570-020-0189-9 / Nat. Rev. Chem. by von Lilienfeld O. A. (2020)
  63. 10.1016/j.commatsci.2012.10.028
  64. 10.1021/acs.jpca.9b00311
  65. {'key': 'ref65/cit65', 'first-page': '273002', 'volume': '29', 'author': 'Larsen A. H.', 'year': '2017', 'journal-title': 'J. Phys.: Condens. Matter'} / J. Phys.: Condens. Matter by Larsen A. H. (2017)
  66. 10.1103/physrevb.49.14251
  67. 10.1016/0927-0256(96)00008-0
  68. 10.1103/physrevb.54.11169
  69. 10.1103/physrevb.59.1758
  70. 10.1103/physrevlett.77.3865
  71. 10.1088/0953-8984/21/8/084204
  72. 10.1002/jcc.20575
  73. 10.1002/jcc.26353
  74. 10.1021/jp202489s
  75. 10.1103/physrevb.100.184103
  76. 10.1038/s41524-020-0310-0
  77. 10.1038/s41524-019-0162-7
  78. 10.1038/s41524-020-00401-8 / npj Comput. Mater. by Kim Y. (2019)
  79. 10.1039/c8me00012c
  80. Fey, M.; Lenssen, J. E.; Fast graph representation learning with PyTorch Geometric. 2019, arXiv preprint arXiv:1903.02428.
  81. Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 2019, pp 8026–8037.
  82. {'volume-title': 'Representation Learning on Graphs: Methods and Applications', 'year': '2017', 'author': 'Hamilton W. L.', 'key': 'ref83/cit83'} / Representation Learning on Graphs: Methods and Applications by Hamilton W. L. (2017)
  83. 10.1063/1.4966192
  84. 10.1103/physrevlett.104.136403
  85. 10.1103/physrevlett.120.145301
  86. {'key': 'ref87/cit87', 'first-page': '991', 'author': 'Schütt K.', 'year': '2017', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. by Schütt K. (2017)
  87. Klicpera, J.; Giri, S.; Margraf, J. T.; Günnemann, S. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. 2020, arXiv preprint arXiv:2011.14115.
  88. Klicpera, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs. International Conference on Learning Representations (ICLR), 2020.
  89. 10.1038/sdata.2014.22
  90. Pracht, P.; Caldeweyher, E.; Ehlert, S.; Grimme, S.;A Robust Non-Self-Consistent Tight-Binding Quantum Chemistry Method for large Molecules. 2019, chemrxiv:8326202.v1. (10.26434/chemrxiv.8326202)
  91. 10.1002/wcms.1493
  92. 10.1063/1.3095491
  93. 10.1007/bf01589116
  94. Tang, Y.; Selvitopi, O.; Popovici, D. T.; Buluç, A. A High-Throughput Solver for Marginalized Graph Kernels on GPU. 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS); IEEE, 2020; pp 728–738. (10.1109/IPDPS47924.2020.00080)
  95. 10.1063/1.5078640
  96. Huang, B.; Symonds, N. O.; von Lilienfeld, O. A. The fundamentals of quantum machine learning. 2018, arXiv preprint arXiv:1807.04259.
  97. Miller, B. K.; Geiger, M.; Smidt, T. E.; Noé, F. Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. 2020, arXiv preprint arXiv:2008.08461.
  98. Bratholm, L. A.; Gerrard, W.; Anderson, B.; Bai, S.; Choi, S.; Dang, L.; Hanchar, P.; Howard, A.; Huard, G.; Kim, S.; A community-powered search of machine learning strategy space to find NMR property prediction models. 2020, arXiv preprint arXiv:2008.05994. (10.1371/journal.pone.0253612)
  99. {'key': 'ref100/cit100', 'first-page': '14537', 'author': 'Anderson B.', 'year': '2019', 'journal-title': 'Adv. Neural Inf. Process. Syst.'} / Adv. Neural Inf. Process. Syst. by Anderson B. (2019)
  100. 10.1063/5.0021116
  101. 10.1002/anie.201107947
  102. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016; pp 770–778. (10.1109/CVPR.2016.90)
  103. {'key': 'ref104/cit104', 'first-page': '9', 'volume': '1', 'author': 'Radford A.', 'year': '2019', 'journal-title': 'OpenAI Blog'} / OpenAI Blog by Radford A. (2019)
Dates
Type When
Created 4 years, 3 months ago (May 4, 2021, 12:41 p.m.)
Deposited 2 years, 4 months ago (April 15, 2023, 7:20 a.m.)
Indexed 1 minute ago (Aug. 21, 2025, 3:17 a.m.)
Issued 4 years, 3 months ago (May 4, 2021)
Published 4 years, 3 months ago (May 4, 2021)
Published Online 4 years, 3 months ago (May 4, 2021)
Published Print 4 years, 3 months ago (May 21, 2021)
Funders 0

None

@article{Chanussot_2021, title={Open Catalyst 2020 (OC20) Dataset and Community Challenges}, volume={11}, ISSN={2155-5435}, url={http://dx.doi.org/10.1021/acscatal.0c04525}, DOI={10.1021/acscatal.0c04525}, number={10}, journal={ACS Catalysis}, publisher={American Chemical Society (ACS)}, author={Chanussot, Lowik and Das, Abhishek and Goyal, Siddharth and Lavril, Thibaut and Shuaibi, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary}, year={2021}, month=may, pages={6059–6072} }