Normalizing flows for atomic solids

@article{Wirnsberger2022NormalizingFF,
  title={Normalizing flows for atomic solids},
  author={Peter Wirnsberger and George Papamakarios and Borja Ibarz and S{\'e}bastien Racani{\`e}re and Andy Ballard and Alexander Pritzel and Charles Blundell},
  journal={Machine Learning: Science and Technology},
  year={2022},
  volume={3}
}
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Helmholtz free energy estimates for cubic and hexagonal ice modelled as monatomic water as well as for a truncated and shifted Lennard-Jones system, and find them to be in excellent agreement with literature values and with estimates from established… 

A deep variational free energy approach to dense hydrogen

We present a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Boltzmann distribution

Flow Annealed Importance Sampling Bootstrap

TLDR
This work is the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density and without access to samples generated via Molecular Dynamics simulations, and produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations.

Atomic structure generation from reconstructing structural fingerprints

TLDR
This work implements this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model and is able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept.

References

SHOWING 1-10 OF 35 REFERENCES

Phys

  • 153, 144112
  • 2020

Phys

  • Rev. E 65, 046122
  • 2002

Phys

  • 129, 124105
  • 2008

Understanding Molecular Simulation

Phys

  • 22, 245
  • 1976

Phys

  • Rev. Lett. 79, 3002
  • 1997

Phys

  • 127, 154113
  • 2007

Equivariant Flows: exact likelihood generative learning for symmetric densities

TLDR
This work provides a theoretical sufficient criterion showing that the distribution generated by equivariant normalizing flows is invariant with respect to these symmetries by design, and proposes building blocks for flows which preserve symmetry which are usually found in physical/chemical many-body particle systems.

Graph

  • C. Calì
  • Computer Science
    Data Structure and Algorithms Using C++
  • 2020
TLDR
Experimental results show that the Predictive Random Graph Ranking framework can improve the accuracy of the ranking algorithms such as PageRank, Common Neighbor, and Jaccard’s Coefficient.

Normalizing Flows on Tori and Spheres

TLDR
This paper proposes and compares expressive and numerically stable flows on spaces with more complex geometries, such as tori or spheres, and builds recursively on the dimension of the space, starting from flows on circles, closed intervals or spheres.