标题： MBFormer：一种用于真实材料中多体相互作用的通用基于Transformer的学习范式 显示英文标题

标题： MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials

摘要： 最近，机器学习（ML）的激进进展彻底改变了计算材料科学，实现了前所未有的快速材料发现和性质预测，但由于非局部和能量依赖相互作用的复杂性，量子多体问题——这是理解激发态性质（从输运到光学性质）的关键——仍然具有挑战性。 在此，我们提出了一种对称感知、无网格、基于变压器的模型MBFormer，该模型旨在直接从平均场输入中学习整个多体层次结构，利用注意力机制准确捕捉平均场状态之间的多体相关性。 作为原理证明，我们展示了MBFormer在基于GW加上Bethe Salpeter方程（GW-BSE）形式的预测能力，包括准粒子能量、激子能量、激子振子强度和激子波函数分布。 我们的模型在一个包含721种二维材料的数据集上进行训练，这些材料来自C2DB数据库，在不同材料的状态级准粒子和激子能量上的预测平均绝对误差（MAE）达到了0.1-0.2 eV的低水平，表现出最先进的性能。 此外，我们明确展示了注意力机制在捕捉多体相关性中的关键作用。 我们的框架提供了一个从基态到实际材料中一般多体预测的端到端平台，可以作为计算材料科学的基础模型。 显示英文摘要

摘要： Recently, radical progress in machine learning (ML) has revolutionized computational materials science, enabling unprecedentedly rapid materials discovery and property prediction, but the quantum many-body problem -- which is the key to understanding excited-state properties, ranging from transport to optics -- remains challenging due to the complexity of the nonlocal and energy-dependent interactions. Here, we propose a symmetry-aware, grid-free, transformer-based model, MBFormer, that is designed to learn the entire many-body hierarchy directly from mean-field inputs, exploiting the attention mechanism to accurately capture many-body correlations between mean-field states. As proof of principle, we demonstrate the capability of MBFormer in predicting results based on the GW plus Bethe Salpeter equation (GW-BSE) formalism, including quasiparticle energies, exciton energies, exciton oscillator strengths, and exciton wavefunction distribution. Our model is trained on a dataset of 721 two-dimensional materials from the C2DB database, achieving state-of-the-art performance with a low prediction mean absolute error (MAE) on the order of 0.1-0.2 eV for state-level quasiparticle and exciton energies across different materials. Moreover, we show explicitly that the attention mechanism plays a crucial role in capturing many-body correlations. Our framework provides an end-to-end platform from ground states to general many-body prediction in real materials, which could serve as a foundation model for computational materials science.