Molten salts are a promising class of ionic liquids for clean energy applications, such as nuclear and solar energy. However, efficient and accurate evaluation of salt properties from a fundamental, microscopic perspective remains a challenge. Here, we apply artificial neural networks to atomistic modeling of molten NaCl to accurately reproduce the properties from ab initio quantum mechanical calculations based on density functional theory (DFT). The obtained neural network interatomic potential (NNIP) effectively captures the effects of both long-range and short-range interactions, which are crucial for modeling ionic liquids. Extensive validations suggest that the NNIP is capable of predicting the structural, thermophysical, and transport properties of molten NaCl as well as properties of crystalline NaCl, demonstrating near-DFT accuracy and 10³× higher efficiency in atomistic simulations. This application of NNIP suggests a paradigm shift from empirical/semiempirical/ab initio approaches to an efficient and accurate machine learning scheme in molten salt modeling.

熔融盐是用于清洁能源应用（例如核能和太阳能）的有前途的一类离子液体。但是，从基本的微观角度有效，准确地评估盐的特性仍然是一个挑战。在这里，我们将人工神经网络应用于熔融NaCl的原子建模，以准确地从头开始重现特性。基于密度泛函理论（DFT）的量子力学计算。获得的神经网络原子间电势（NNIP）有效地捕获了长程和短程相互作用的影响，这对于建模离子液体至关重要。广泛的验证表明，NNIP能够预测熔融NaCl的结构，热物理和传输特性，以及结晶NaCl的特性，在原子模拟中证明了接近DFT的准确性和10 ³ ×更高的效率。NNIP的这种应用表明，从经验/半经验/从头开始方法向熔融盐建模中的有效且准确的机器学习方案的范式转变。