Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

@article{Bartk2010GaussianAP,
  title={Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.},
  author={Albert P. Bart{\'o}k and Mike C. Payne and Risi Kondor and G{\'a}bor Cs{\'a}nyi},
  journal={Physical review letters},
  year={2010},
  volume={104 13},
  pages={
          136403
        }
}
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations. The models do not have a fixed functional form and hence are capable of modeling complex potential energy landscapes. They are systematically improvable with more data. We apply the method to bulk crystals, and test it by calculating properties at high temperatures. Using the interatomic… 

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References

SHOWING 1-10 OF 43 REFERENCES
Quantum Theory of Angular Momentum
Containing basic definitions and theorems as well as relations, tables of formulas and numerical tables which are essential for applications to many physical problems, the book is useful for
Interatomic Forces in Condensed Matter
I: THE FRAMEWORK 1. Essential quantum mechanics 2. Essential density functional theory 3. Exploiting the variational principle 4. Linear response theory II: MODELLING ATOMS WITHIN SOLIDS 5. Testing
Modern quantum chemistry : introduction to advanced electronic structure theory
Dover Publications Inc., United States, 1996. Paperback. Book Condition: New. New edition. 212 x 138 mm. Language: English . Brand New Book. The aim of this graduate-level textbook is to present and
Gaussian Processes for Machine Learning
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
"J."
however (for it was the literal soul of the life of the Redeemer, John xv. io), is the peculiar token of fellowship with the Redeemer. That love to God (what is meant here is not God’s love to men)
Information Theory, Inference, and Learning Algorithms
  • D. Mackay
  • Computer Science
    IEEE Transactions on Information Theory
  • 2004
Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
Information Theory
Information TheoryPapers read at a Symposium on Information Theory held at the Royal Institution, London, August 29th to September 2nd, 1960. Edited by Colin Cherry. Pp. xi + 476. (London:
Learn
This article describes the process and findings of a community study that was part of a task force to improve educational experiences for new English learners, particularly the large number of
Phys
  • Rev. B 39, 5566
  • 1989
Rep
  • Prog. Phys. 72, 026501
  • 2009
...
1
2
3
4
5
...