论文标题
粒子和自组装方案的神经进化学习
Neuroevolutionary learning of particles and protocols for self-assembly
论文作者
论文摘要
在沉积在表面上的分子的模拟中，我们表明，神经进化学习可以设计颗粒和时间依赖性方案，以促进自组装，而无需从物理概念（例如热平衡或机械稳定性）中输入，并且没有对候选或竞争结构的先验知识。学习算法能够有针对性和探索性设计：它可以用用户定义的属性组装材料，也可以在指定顺序参数的空间中搜索新颖性。在后一种模式下，它探讨了可以制作的空间，而不是能量较低但不一定在动力学上访问的结构空间。
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures. The learning algorithm is capable of both directed and exploratory design: it can assemble a material with a user-defined property, or search for novelty in the space of specified order parameters. In the latter mode it explores the space of what can be made rather than the space of structures that are low in energy but not necessarily kinetically accessible.
