Over the past decade, evolutionary algorithms, data mining, and other methods showed great success in solving the main problem of theoretical crystallography: finding the stable structure for a given chemical composition. Here, we develop a method that addresses the central problem of computational materials science: the prediction of material(s), among all possible combinations of all elements, that possess the best combination of target properties. This nonempirical method combines our new coevolutionary approach with the carefully restructured “Mendelevian” chemical space, energy filtering, and Pareto optimization to ensure that the predicted materials have optimal properties and a high chance to be synthesizable. The first calculations, presented here, illustrate the power of this approach. In particular, we find that diamond (and its polytypes, including lonsdaleite) are the hardest possible materials and that bcc-Fe has the highest zero-temperature magnetization among all possible compounds.

在过去的十年中，进化算法，数据挖掘和其他方法在解决理论晶体学的主要问题方面取得了巨大成功：找到给定化学成分的稳定结构。在这里，我们开发了一种解决计算材料科学的中心问题的方法：在所有元素的所有可能组合中，对材料的预测具有目标特性的最佳组合。这种非经验方法将我们的新协同进化方法与经过精心重组的“孟德尔式”化学空间，能量过滤和帕累托优化相结合，以确保所预测的材料具有最佳性能，并且极有可能被合成。这里介绍的第一个计算说明了这种方法的强大功能。尤其是，