通过电子级联合电荷密度与能量目标改进分子能量学习
Improved Learning of Molecular Energetics Through an Electron-Wise Joint Charge Density and Energy Objective
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中文总结 AI 辅助
提出一种图神经网络框架，通过联合预测GGA电子密度和B3LYP能量，利用密度空间信息加速能量学习，共享等变表示实现统一框架，为电荷密度学习提供新途径。
中文摘要 AI 辅助
我们提出了一种基于图神经网络的框架，该框架被训练用于联合预测电子密度和分子能量。模型被训练以预测使用广义梯度近似（GGA）泛函计算的电子密度，同时学习预测在B3LYP理论水平上通过混合泛函计算获得的分子能量。我们的结果表明，电子密度分布中丰富的空间信息可用于改进并加速对准确能量的学习。通过在密度和能量预测头之间共享等变分子表示，模型在统一框架内学习互补的分子量。这为许多近期用于原子尺度模拟的电荷密度学习框架的实际应用提供了一条新的有前景的途径。
英文摘要
We present a graph neural network-based learning framework trained to jointly predict electron densities and molecular energies. The model is trained to predict electron densities calculated with a Generalized Gradient Approximation (GGA) functional while simultaneously learning to predict molecular energies obtained with hybrid functional calculations at the B3LYP level of theory. Our results indicate that the rich spatial information in electron density distribution can be used to improve and accelerate the learning of accurate energies. By sharing an equivariant molecular representation across density and energy prediction heads, the model learns complementary molecular quantities within a unified framework. This provides a new promising route for practical use cases of many recent charge density learning frameworks for atomic scale simulations.
发表机构
- Technical University of Denmark（丹麦技术大学）
- CAPeX Pioneer Center（CAPeX先锋中心）
机构由 AI 辅助整理，请以论文原文为准。
