Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This article addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial network is developed which allows for both randomness, and the possibility of Ohmic and non-Ohmic conduction, that are characteristic of such materials. These differing networks are then exploited by differential evolution, which optimises several configuration parameters (e.g., configuration voltages, weights, etc.), to solve different classification problems. We show that ideal nanomaterial choice depends upon problem complexity, with more complex problems being favoured by complex voltage dependence of conductivity and vice versa. Furthermore, we highlight how intrinsic nanomaterial electrical properties can be exploited by differing configuration parameters, clarifying the role and limitations of these techniques. These findings provide guidance for the rational design of nanomaterials and algorithms for future Evolution-in-Materio processors.

Evolution-in-Materio 是一种计算范式，其中算法重新配置材料的属性以实现特定的计算功能。本文解决了如何通过选择纳米材料和目标应用的进化算法来设计成功和性能良好的 Evolution-in-Materio 处理器的问题。开发了纳米材料网络的物理模型，该模型允许随机性以及欧姆和非欧姆传导的可能性，这些都是此类材料的特征。这些不同的网络随后被差分进化所利用，差分进化优化了几个配置参数（例如，配置电压、权重等），以解决不同的分类问题。我们表明，理想的纳米材料选择取决于问题的复杂性，电导率的复杂电压依赖性有利于解决更复杂的问题，反之亦然。此外，我们强调了如何通过不同的配置参数来利用纳米材料的固有电学特性，阐明了这些技术的作用和局限性。这些发现为合理设计纳米材料和未来 Evolution-in-Materio 处理器的算法提供了指导。