加速的结构稳定性无能量计算器
Accelerated structure-stability energy-free calculator
AI总结：
本研究提出一种结合三个神经网络的无能量机器学习计算器，用于预测颗粒物的能量和原子力，在DFT对比中达到低误差，并展示了模型的可解释性。
摘要：
计算建模是催化研究的重要组成部分。通过它，人们正在开发和实施新方法，以提高模拟的准确性，同时降低计算成本。特别是，已经应用了特定的机器学习技术，从ab initio结果中构建原子间势。在这里，我们报告了一种无能量机器学习计算器，它结合了三个单独训练的神经网络来预测颗粒物的能量和原子力。研究了三种结构：单金属纳米粒子、双金属纳米合金和负载金属晶粒。通过图神经网络预测原子能量，导致与密度泛函理论（DFT）计算的均方误差（MAE）在0.004 eV以内。预测原子力的任务被分配给两个前馈网络，一个预测力的范数，另一个预测其方向。力预测导致与DFT结果的MAE在0.080 eV/A以内。通过以内聚能的形式强调单金属粒子的物理学，展示了图神经网络预测的可解释性。
英文摘要：
Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have been applied to build interatomic potential from ab initio results. Here, We report an energy-free machine-learning calculator that combines three individually trained neural networks to predict the energy and atomic forces of particulate matter. Three structures were investigated: a monometallic nanoparticle, a bimetallic nanoalloy, and a supported metal crystallites. Atomic energies were predicted via a graph neural network, leading to a mean absolute error (MAE) within 0.004 eV from Density Functional Theory (DFT) calculations. The task of predicting atomic forces was split over two feedforward networks, one predicting the force's norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/A against DFT results. The interpretability of the graph neural network predictions was demonstrated by underlying the physics of the monometallic particle in the form of cohesion energy.
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