Compact atomic descriptors enable accurate predictions via linear models.
Compact atomic descriptors enable accurate predictions via linear models.
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紧凑的原子描述符可以通过线性模型进行准确的预测。
DOI：
10.1063/5.0052961
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Stefano de Gironcoli
中科院分区：
文献类型：
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作者：
C. Zeni;K. Rossi;Aldo Glielmo;Stefano de Gironcoli
我们利用从原子簇扩展导出的三体局部密度表示来探讨线性岭回归的准确性。我们在预测分子和固体中的形成能和原子力方面对这个框架的准确性进行了基准测试。我们发现，这样一个简单的回归框架的性能与最先进的机器学习方法相当，在大多数情况下，后者更复杂且计算要求更高。随后，我们寻找稀疏描述符的方法并进一步提高该方法的计算效率。为此，我们使用主成分分析和最小绝对收缩算子回归对六个单元素数据集进行能量拟合。这两种方法都强调了构建比原始描述符小四倍的描述符的可能性，并且精度相似甚至更高。此外，我们发现简化的描述符在六个独立数据集中共享相当大一部分的特征，这暗示了设计与材料无关的、最佳压缩的和准确的描述符的可能性。
We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecules and solids. We find that such a simple regression framework performs on par with state-of-the-art machine learning methods which are, in most cases, more complex and more computationally demanding. Subsequently, we look for ways to sparsify the descriptor and further improve the computational efficiency of the method. To this aim, we use both principal component analysis and least absolute shrinkage operator regression for energy fitting on six single-element datasets. Both methods highlight the possibility of constructing a descriptor that is four times smaller than the original with a similar or even improved accuracy. Furthermore, we find that the reduced descriptors share a sizable fraction of their features across the six independent datasets, hinting at the possibility of designing material-agnostic, optimally compressed, and accurate descriptors.