J Phys Chem B. 2018 May 31;122(21):5547-5556. doi: 10.1021/acs.jpcb.7b11841. Epub 2018 Mar 8.
We discuss how a machine learning approach based on relative entropy optimization can be used as an inverse design strategy to discover isotropic pair interactions that self-assemble single- or multicomponent particle systems into Frank-Kasper phases. In doing so, we also gain insights into the self-assembly of quasicrystals.
我们讨论了如何基于相对熵优化的机器学习方法作为反向设计策略,来发现各向同性对相互作用,从而将单组分或多组分粒子系统自组装成弗兰克-凯泽相。在这个过程中,我们也深入了解了准晶的自组装。