Predicting crystal structure is one of the most fundamental problems in materials science and a key early step in computational materials design. Ab initio simulation methods are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Calculations, or DMQC) that applies data mining techniques to existing ab initio data in order to increase the efficiency of crystal structure prediction for new alloys. We find about a factor of three speedup in ab hitio prediction of crystal structures using DMQC as compared to naive random guessing. This study represents an extension of work done by Curtarolo, et al. [1] to a larger library of data.
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机译：预测晶体结构是材料科学中最基本的问题之一，以及计算材料设计的关键早期步骤之一。 AB Initio仿真方法是一种强大的工具，用于预测晶体结构，但太慢探索新合金的可能结构的极大空间。在这里，我们描述了对新的方法（量子计算的数据挖掘或DMQC）的持续工作，该方法将数据挖掘技术应用于现有的AB Initio数据，以提高新合金晶体结构预测的效率。与天真随机猜测相比，我们发现使用DMQC的晶体结构的AB Hitio预测中的三个加速度。本研究代表CTAROLO等人完成的工作延伸。 [1]到更大的数据库。
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