论文标题
使用反向蒙特卡洛和神经网络模型的组合加速构型自由能的计算：2D正方形和三角形晶格的吸附等温线
Accelerated calculation of configurational free energy using a combination of reverse Monte Carlo and neural network models: Adsorption isotherm for 2D square and triangular lattices
论文作者
论文摘要
我们证明了人工神经网络（ANN）模型在逆转基于蒙特卡洛的热力学计算中的应用。为2D正方形和三角形晶格生成吸附等温线。这些晶格被认为是因为它们对催化应用的重要性。通常，吸附物安排产生的构型自由能术语在处理方面具有挑战性，并且通常使用计算昂贵的蒙特卡洛模拟对其进行评估。我们表明，反向蒙特卡洛（RMC）和ANN模型的组合可以提供对构型自由能的准确估计。使用RMC模拟生成的数据训练/构建了ANN模型。使用此方法在几秒钟内，使用此方法在几秒钟内准确地获得了一系列吸附物吸附物相互作用，覆盖范围和温度的吸附等温线。通过比较MC计算来验证结果。另外，使用ANN/RMC方法研究了Ni（100）表面的H吸附。
We demonstrate the application of artificial neural network (ANN) models to reverse Monte Carlo based thermodynamic calculations. Adsorption isotherms are generated for 2D square and triangular lattices. These lattices are considered because of their importance to catalytic applications. In general, configurational free energy terms that arise from adsorbate arrangements are challenging to handle and are typically evaluated using computationally expensive Monte Carlo simulations. We show that a combination of reverse Monte Carlo (RMC) and ANN model can provide an accurate estimate of the configurational free energy. The ANN model is trained/constructed using data generated with the help of RMC simulations. Adsorption isotherms are accurately obtained for a range of adsorbate-adsorbate interactions, coverages and temperatures within few seconds on a desktop computer using this method. The results are validated by comparing to MC calculations. Additionally, H adsorption on Ni(100) surface is studied using the ANN/RMC approach.
