Built on

Y. Song and S. Ermon, “Generative Modeling by Estimating Gradients of the Data Distribution,” (2020), arxiv:1907.05600 [cs, stat]
Original
1907
Earlier work this paper cites.
P. M. Morse, Physical Review 34
1929
Earlier work this paper cites.
J. Kennedy and R. Eberhart, in Proceedings of ICNN’95 - International Conference on Neural Networks , Vol. 4 (1995) pp. 1942–1948 vol.4
1948
Earlier work this paper cites.
W. Kohn and L. J. Sham, Physical Review 140
1965
Earlier work this paper cites.
G. Kresse and J. Hafner, Physical Review B 47
1993
Earlier work this paper cites.
W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, Journal of the American Chemical Society 118
1996
Earlier work this paper cites.
J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman, and D. A. Case, Journal of Computational Chemistry 25
2004
Earlier work this paper cites.
A. Hyvärinen, Journal of Machine Learning Research 6
2005
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S. J. Clark, M. D. Segall, C. J. Pickard, P. J. Hasnip, M. I. J. Probert, K. Refson, and M. C. Payne, Zeitschrift für Kristallographie - Crystalline Materials 220
2005
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J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” (2020), arxiv:2006.11239 [cs, stat]
Original
2006
Earlier work this paper cites.
C. J. Pickard and R. J. Needs, Physical Review Letters 97
2006
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C. Zang and F. Wang, in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020) pp. 617–626, arxiv:2006.10137 [physics, stat]
Original
2006
Earlier work this paper cites.
J. Behler and M. Parrinello, Physical Review Letters 98
2007
Earlier work this paper cites.
J. J. P. Stewart, Journal of Molecular Modeling 13
2007
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K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, and A. D. Mackerell Jr., Journal of Computational Chemistry 31
2010
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A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Physical Review Letters 104
2010
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising Diffusion Implicit Models,” (2022), arxiv:2010.02502 [cs]
Original
2010
Earlier work this paper cites.
P. Vincent, Neural Computation 23
2011
Earlier work this paper cites.
C. J. Pickard and R. J. Needs, Journal of Physics: Condensed Matter 23
2011
Earlier work this paper cites.
R. Gao, Y. Song, B. Poole, Y. N. Wu, and D. P. Kingma, “Learning Energy-Based Models by Diffusion Recovery Likelihood,” (2021), arxiv:2012.08125 [cs, stat]
Original
2012
Earlier work this paper cites.
A. P. Bartók, R. Kondor, and G. Csányi, Physical Review B 87
2013
Earlier work this paper cites.

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Then

T. Xie, X. Fu, O.-E. Ganea, R. Barzilay, and T. Jaakkola, “Crystal Diffusion Variational Autoencoder for Periodic Material Generation,” (2022), arxiv:2110.06197 [cond-mat, physics:physics]
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Later among the works it cites.
S. Zaidi, M. Schaarschmidt, J. Martens, H. Kim, Y. W. Teh, A. Sanchez-Gonzalez, P. Battaglia, R. Pascanu, and J. Godwin, “Pre-training via Denoising for Molecular Property Prediction,” (2022), arxiv:2206.00133 [cs, q-bio, stat]
Original
2022
Later among the works it cites.
L. Wu, C. Gong, X. Liu, M. Ye, and Q. Liu, “Diffusion-based molecule generation with informative prior bridges,” (2022), arXiv:2209.00865 [cs.LG]
Original
2022
Later among the works it cites.
M. Xu, L. Yu, Y. Song, C. Shi, S. Ermon, and J. Tang, “GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation,” (2022), arxiv:2203.02923 [cs, q-bio]
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Later among the works it cites.

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2024

Zero Shot Molecular Generation via Similarity Kernels

Elijošius, Rokas, Zills, Fabian, Batatia, Ilyes et al.

Understand

Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties.

Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches.
Key to their success is the close relationship between the score and physical force, allowing the use of powerful equivariant neural networks.
However, the behaviour of the learnt score is not yet well understood.

Built on

Y. Song and S. Ermon, “Generative Modeling by Estimating Gradients of the Data Distribution,” (2020), arxiv:1907.05600 [cs, stat]
Original
1907
Earlier work this paper cites.
P. M. Morse, Physical Review 34
1929
Earlier work this paper cites.
J. Kennedy and R. Eberhart, in Proceedings of ICNN’95 - International Conference on Neural Networks , Vol. 4 (1995) pp. 1942–1948 vol.4
1948
Earlier work this paper cites.
W. Kohn and L. J. Sham, Physical Review 140
1965
Earlier work this paper cites.
G. Kresse and J. Hafner, Physical Review B 47
1993
Earlier work this paper cites.
W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, Journal of the American Chemical Society 118
1996
Earlier work this paper cites.
J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman, and D. A. Case, Journal of Computational Chemistry 25
2004
Earlier work this paper cites.
A. Hyvärinen, Journal of Machine Learning Research 6
2005
Earlier work this paper cites.
S. J. Clark, M. D. Segall, C. J. Pickard, P. J. Hasnip, M. I. J. Probert, K. Refson, and M. C. Payne, Zeitschrift für Kristallographie - Crystalline Materials 220
2005
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” (2020), arxiv:2006.11239 [cs, stat]
Original
2006
Earlier work this paper cites.
C. J. Pickard and R. J. Needs, Physical Review Letters 97
2006
Earlier work this paper cites.
C. Zang and F. Wang, in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020) pp. 617–626, arxiv:2006.10137 [physics, stat]
Original
2006
Earlier work this paper cites.
J. Behler and M. Parrinello, Physical Review Letters 98
2007
Earlier work this paper cites.
J. J. P. Stewart, Journal of Molecular Modeling 13
2007
Earlier work this paper cites.
K. Vanommeslaeghe, E. Hatcher, C. Acharya, S. Kundu, S. Zhong, J. Shim, E. Darian, O. Guvench, P. Lopes, I. Vorobyov, and A. D. Mackerell Jr., Journal of Computational Chemistry 31
2010
Earlier work this paper cites.
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Physical Review Letters 104
2010
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising Diffusion Implicit Models,” (2022), arxiv:2010.02502 [cs]
Original
2010
Earlier work this paper cites.
P. Vincent, Neural Computation 23
2011
Earlier work this paper cites.
C. J. Pickard and R. J. Needs, Journal of Physics: Condensed Matter 23
2011
Earlier work this paper cites.
R. Gao, Y. Song, B. Poole, Y. N. Wu, and D. P. Kingma, “Learning Energy-Based Models by Diffusion Recovery Likelihood,” (2021), arxiv:2012.08125 [cs, stat]
Original
2012
Earlier work this paper cites.
A. P. Bartók, R. Kondor, and G. Csányi, Physical Review B 87
2013
Earlier work this paper cites.

Similar

R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld, Scientific Data 1
2014
Cited alongside, same era.
P. de Buyl, P. H. Colberg, and F. Höfling, Computer Physics Communications 185
2014
Cited alongside, same era.
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep Unsupervised Learning using Nonequilibrium Thermodynamics,” (2015), arxiv:1503.03585 [cond-mat, q-bio, stat]
Original
2015
Cited alongside, same era.
J. J. P. Stewart, “Mopac2016,” Stewart Computational Chemistry, Colorado Springs, CO, USA, http://OpenMOPAC.net (2016)
2016
Cited alongside, same era.
J. S. Smith, O. Isayev, and A. E. Roitberg, Chemical Science 8
2017
Cited alongside, same era.
F. Peng, Y. Sun, C. J. Pickard, R. J. Needs, Q. Wu, and Y. Ma, Physical Review Letters 119

Then

T. Xie, X. Fu, O.-E. Ganea, R. Barzilay, and T. Jaakkola, “Crystal Diffusion Variational Autoencoder for Periodic Material Generation,” (2022), arxiv:2110.06197 [cond-mat, physics:physics]
Original
2022
Later among the works it cites.
S. Zaidi, M. Schaarschmidt, J. Martens, H. Kim, Y. W. Teh, A. Sanchez-Gonzalez, P. Battaglia, R. Pascanu, and J. Godwin, “Pre-training via Denoising for Molecular Property Prediction,” (2022), arxiv:2206.00133 [cs, q-bio, stat]
Original
2022
Later among the works it cites.
L. Wu, C. Gong, X. Liu, M. Ye, and Q. Liu, “Diffusion-based molecule generation with informative prior bridges,” (2022), arXiv:2209.00865 [cs.LG]
Original
2022
Later among the works it cites.
M. Xu, L. Yu, Y. Song, C. Shi, S. Ermon, and J. Tang, “GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation,” (2022), arxiv:2203.02923 [cs, q-bio]
Original
2022
Later among the works it cites.

Beyond the bibliography

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Open on alphaXiv

alphaXiv is searching for related work…

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, arXiv:1706.03762 [cs] (2017), arxiv:1706.03762 [cs]

Original

2017

Cited alongside, same era.

A. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dułak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. B. Jensen, J. Kermode, J. R. Kitchin, E. L. Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K. S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K. W. Jacobsen, Journal of Physics: Condensed Matter 29

2017

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L. N. Smith and N. Topin, “Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates,” (2018), arxiv:1708.07120 [cs, stat]

Original

2018

Cited alongside, same era.

R. Drautz, Physical Review B 99

2019

Cited alongside, same era.

B. Anderson, T. S. Hy, and R. Kondor, in Advances in Neural Information Processing Systems , Vol. 32 (Curran Associates, Inc., 2019)

2019

Cited alongside, same era.

Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, in International Conference on Learning Representations (2020)

2020

Cited alongside, same era.

E. Hoogeboom, V. G. Satorras, C. Vignac, and M. Welling, “Equivariant Diffusion for Molecule Generation in 3D,” (2022), arxiv:2203.17003 [cs, q-bio, stat]

Original

2022