标题： 机器学习在能量八个数量级上的化学反应预测 显示英文标题

标题： Machine learning prediction of a chemical reaction over 8 decades of energy

摘要： 最近机器学习的进展激发了利用这项技术预测化学反应结果的更大兴趣。 此类努力的最终目标是开发一个通用模型，该模型可以在给定反应物和物理条件的情况下预测任何化学反应的产物。 为了追求更加通用的化学预测器，已经开发了用于原子-双原子和双原子-双原子反应的机器学习模型，但目前尚无适用于三分子反应的此类模型。 因此，我们引入了用于预测原子重组反应消光函数的神经网络。 我们的模型预测了Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs和Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$在多个数量级的能量下的重组，得到了相对误差为$\lesssim 10\%$的总体结果。 即使超出训练期间看到的能量范围，我们的模型也能准确预测原子重组反应速率。 因此，该机器能够学习原子重组反应动力学背后的物理规律。 显示英文摘要

摘要： Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs and Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$ over multiple orders of magnitude of energy, yielding overall results with a relative error $\lesssim 10\%$. Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics.