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/A, which is 1–2 orders of magnitude more accurate than state-of-art artificial neural network (ANN) in PCM literature (over 101 eV/A). 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 ${n}_{\text {a}}\approx {10}^{{1}}$ , 102, 103, for instance. Its efficiency and accuracy may be useful to develop next-generation atomistic modeling tools to enable in-depth optimization of PCM.