Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries

By Roy L. Johnston. 2003

591 citations recorded by the source corpus.

Subfields: Atmospheric Science, Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/bc7550fb9c512b7a0bf5b3a4db4e7edaf9cbf9d2

How researchers see this paper

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

In Atmospheric Science

1. R. Ferrando
2. F. Baletto
3. Scott M. Woodley
4. R. Catlow

In Atomic and Molecular Physics, and Optics

1. Marin Soljačić
2. John D. Joannopoulos
3. F. Baletto
4. R. Ferrando
5. Max Tegmark
6. Scott M. Woodley
7. R. Catlow
8. Shanhui Fan

In Materials Chemistry

1. F. Baletto
2. R. Ferrando
3. Marin Soljačić
4. Max Tegmark
5. John D. Joannopoulos
6. Scott M. Woodley
7. R. Catlow

Citations in the corpus

• Crystal structure prediction from first principles. (2008, background): “One popular choice in addressing the first example uses operators based on a phenotype (that is, based on real ionic coordinates), as opposed to genotype (based on analogous genes”
• Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects (2005, background): “Recent reviews are found in Hartke (2002) and Johnston (2003).”
• Nanophotonic particle simulation and inverse design using artificial neural networks (2017, background): “There are many different ways to solve inverse design problems, which can be classified into two main categories: the genetic algorithm (5, 6) (searching the space step by step) and adjoint method (7) (mathematically reversing the equations).”