USPEX - Evolutionary crystal structure prediction

By Colin W. Glass, Artem R. Oganov, Nikolaus Hansen. Computer Physics Communications, 2006

1,117 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0bbe0e05f428349b62194e432f2f0da6bc032bd1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Konstantin Gubaev
4. Octavian-Eugen Ganea
5. Ivan S. Novikov
6. Alexander V. Shapeev
7. Evgeny V. Podryabinkin
8. Tian Xie

In Materials Chemistry

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Evgeny V. Podryabinkin
4. Linfeng Zhang
5. Konstantin Gubaev
6. Han Wang
7. David J. Srolovitz
8. W. E

In Molecular Biology

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Han Wang
4. Linfeng Zhang
5. Octavian-Eugen Ganea
6. Tian Xie
7. Xiang Fu
8. W. E

Citations in the corpus

• The MLIP package: moment tensor potentials with MPI and active learning (2020, background): “, [22]) or are taken from some bank of structures (e.”
• Crystal Diffusion Variational Autoencoder for Periodic Material Generation (2021, methodology): “State-of-the-art methods include random sampling (Pickard & Needs, 2011), evolutionary algorithms (Wang et al., 2012; Glass et al., 2006), substituting elements in known materials (Hautier et al., 2011), etc., but they generally have low success rates and require extensive computation even on…”