Papers

2020 - JCP - EA for organic molecules with many local minimums

• Unsupervised search of low-lying conformers with spectroscopic accuracy: A two-step algorithm rooted into the island model
• G. Mancini, M. Fusè, F. Lazzari, B. Chandramouli, and V. Barone, J. Chem. Phys. 153, 124110 (2020).
• https://aip.scitation.org/doi/10.1063/5.0018314

evolutionary algorithm

2018 - Phys. Rev. Lett. - Improvements in Rattle and Crossover

• On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization
• T.L. Jacobsen, M.S. Jørgensen, and B. Hammer, Phys. Rev. Lett. 120, 026102 (2018).
• https://link.aps.org/doi/10.1103/PhysRevLett.120.026102

This paper uses machine learned atomic potential to guide the rattle and crossover operations in an EA run, thus accelerating the structure search in SnO2(110)-(4x1) reconstruction, anatase TiO2(001)-(1x4) ridge reconstruction and quinoline(C9H7N). The atomic potential is defined as the energy difference between one structure (n atoms) and the same structure lacking one atom (n-1 atoms). $$ E _ {atomic} = E(\bold{R} _ n) - E(\bold{R} _ {n-1}) $$ A low $E_{atomic}$ means this atom is in a stable environment. Thus the atom with high $E_{atomic}$ should have high probabilities to be displaced in the rattle operation and removed in crossover operation.