Deep Neural Network for Accurate and Efficient Atomistic Modeling of Phase Change Memory

This letter presents a general-purpose fully-atomistic method to simulate phase change memory (PCM), by combining density functional theory (DFT) and deep neural network (DNN). Its maximum calculation error of atomic forces is about 10
⁻¹
eV/Å, which is 1–2 orders of magnitude more accurate than state-of-art artificial neural network (ANN) in PCM literature (over 10
¹
eV/Å). Its simulation time,
${t}_{\text {s}}$
, scales linearly with the number of atoms
${n}_{\text {a}}$
(
${t}_{\text {s}}\propto n_{\text {a}}$
), which is more efficient than DFT (
${t}_{\text {s}}\propto {n}_{\text {a}}^{3}$
) widely used to model PCM, leading to approximately 2, 4, 6 orders of magnitude reduction of modeling time when
${n}_{\text {a}}\approx {10}^{{1}}$
, 10
²
, 10
³
, for instance. Its efficiency and accuracy may be useful to develop next-generation atomistic modeling tools to enable in-depth optimization of PCM.