化學資訊整合入強化學習以增強分子幾何結構優化

Abstract

在計算化學領域中，分子的幾何構型優化是一項關鍵的步驟，而優化演算法的效率提升，對於縮減計算成本具有極其關鍵且不可或缺的影響力。在此研究中，我們引進了一種新穎的基於強化學習(reinforcement learning)的優化器(optimizer)，其效率超越了傳統的方法。我們的模型與眾不同之處在於其能夠將化學資訊整合至優化過程中。透過探索不同狀態表示法，像是結合梯度(gradient)、位移(displacements)、原始類型標籤(primitive type labels)，以及來自SchNet模型的額外化學資訊，我們的強化學習優化器展現了優異的成果。與傳統的優化演算法相比，特別是在處理較糟的初始幾何結構時，我們的方法所需的優化步數，平均減少了約50%。更重要的是，這種強化學習優化器在不同的理論層次上展現了可靠的通用性，突顯了其在提升分子幾何結構優化之領域的多樣性和潛力。這項研究強調了強化學習演算法可透過結合化學知識而提升效能的重要性，並為計算化學領域的未來開啟了新的可能性。

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement learning based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms we examined, particularly when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in the field of computational chemistry.