论文标题
使用图神经网络预测能量景观集合的基态配置
Predicting ground state configuration of energy landscape ensemble using graph neural network
论文作者
论文摘要
许多科学问题试图在坚固的能源景观中找到基态，这一任务对于大型系统而言变得非常困难。但是，在特定类别的问题中，能量最小值内的短距离相关性可能与系统大小无关。这些相关性是否可以从已知基础状态的小问题推断出来，以加速寻找较大问题的基础状态？在这里，我们演示了有关自旋玻璃的策略，其中相互作用矩阵是从蛋白质接触图中绘制的。我们使用图形神经网络来学习从相互作用矩阵$ j $到基础状态配置的映射，从而为一组最可能的配置提供了猜测。鉴于这些猜测，我们表明基态配置可以比在香草模拟退火中快得多。对于大型问题，在小$ J $矩阵上训练的模型预测了一种配置，其能量远低于通过模拟退火获得的配置，这表明策略的尺寸可推广性。
Many scientific problems seek to find the ground state in a rugged energy landscape, a task that becomes prohibitively difficult for large systems. Within a particular class of problems, however, the short-range correlations within energy minima might be independent of system size. Can these correlations be inferred from small problems with known ground states to accelerate the search for the ground states of larger problems? Here, we demonstrate the strategy on Ising spin glasses, where the interaction matrices are drawn from protein contact maps. We use graph neural network to learn the mapping from an interaction matrix $J$ to a ground state configuration, yielding guesses for the set of most probable configurations. Given these guesses, we show that ground state configurations can be searched much faster than in vanilla simulated annealing. For large problems, a model trained on small $J$ matrices predicts a configurations whose energy is much lower than those obtained by simulated annealing, indicating the size generalizability of the strategy.
