Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA.
J Chem Phys. 2010 May 7;132(17):174103. doi: 10.1063/1.3407440.
The identification of complex multicomponent material formulations that possess specific optimal properties is a challenging task in materials discovery. The high dimensional composition space needs to be adequately sampled and the properties measured with the goal of efficiently identifying effective formulations. This task must also take into account mass fraction and possibly other constraints placed on the material components. Either combinatorial or noncombinatorial sampling of the composition space may be employed in practice. This paper introduces random sampling-high dimensional model representation (RS-HDMR) as an algorithmic tool to facilitate these nonlinear multivariate problems. RS-HDMR serves as a means to accurately interpolate over sampled materials, and simulations of the technique show that it can be very efficient. A variety of simulations is carried out modeling multicomponent-->property relationships, and the results show that the number of sampled materials to attain a given level of accuracy for a predicted property does not significantly depend on the number of components in the formulation. Although RS-HDMR best operates in the laboratory by guided iterative rounds of random sampling of the composition space along with property observation, the technique was tested successfully on two existing databases of a seven component phosphor material and a four component deNO(x) catalyst for reduction of NO with C(3)H(6).
鉴定具有特定最佳性能的复杂多组分材料配方是材料发现中的一项具有挑战性的任务。需要充分采样高维组成空间，并测量性能，以有效地识别有效的配方。这项任务还必须考虑到材料成分的质量分数和可能存在的其他限制。在实践中，可以采用组合或非组合的组成空间采样。本文引入随机采样-高维模型表示（RS-HDMR）作为一种算法工具，以促进这些非线性多变量问题的解决。RS-HDMR 是一种在采样材料上进行精确插值的方法，并且该技术的模拟表明它非常高效。进行了多种模拟来对多组分-性质关系进行建模，结果表明，达到给定预测性质精度所需的采样材料数量并不显著取决于配方中的成分数量。尽管 RS-HDMR 在实验室中通过沿着组成空间进行有指导的随机采样以及对性质进行观察的迭代轮次中最佳运行，但该技术在两个现有的七组分磷光材料数据库和四组分用于用 C(3)H(6)还原 NO 的脱氮(x)催化剂数据库上的测试均获得了成功。
