论文标题
稀疏高斯工艺电位：应用于锂传导固体电解质的锂扩散率
Sparse Gaussian Process Potentials: Application to Lithium Diffusivity in Superionic Conducting Solid Electrolytes
论文作者
论文摘要
对于原子质潜力的机器学习，可扩展稀疏的高斯过程回归形式主义是通过数据有效的自适应采样算法引入的。通过这种方法，计算成本有效地降低了贝叶斯线性回归方法的计算成本，同时保持了确切高斯过程回归的吸引人特征。作为一个展示，对LI7P3S1111再现了实验性熔化和玻璃结晶温度，模拟了LI扩散率，并且揭示了一个未透明的相位，其li扩散率要低得多，应避免。
For machine learning of interatomic potentials a scalable sparse Gaussian process regression formalism is introduced with a data-efficient on-the-fly adaptive sampling algorithm. With this approach, the computational cost is effectively reduced to those of the Bayesian linear regression methods whilst maintaining the appealing characteristics of the exact Gaussian process regression. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11, Li diffusivity is simulated, and an unchartered phase is revealed with much lower Li diffusivity which should be circumvented.
