跨同位素异形体原子-双原子反应的机器学习模型
Machine learning models for atom-diatom reactions across isotopologues
AI总结：
本文提出用前馈神经网络预测 Ca + H2 → CaH + H 超热反应的振转态分布，并通过在 Ca + H2 和 Ca + T2 上训练实现跨同位素异形体泛化，成功预测未见过的 Ca + D2 反应结果。
摘要：
本工作表明，前馈神经网络能够预测在超热区（与缓冲气体化学相关）中 Ca + H2 → CaH + H 反应的非弹性过程和反应过程的最终振转态分布。此外，这些模型可以扩展到涉及氘和氚的反应同位素异形体。另外，我们开发了一个神经网络模型，该模型能够基于氢的同位素异形体在化学空间中进行学习。该模型可以预测反应物从未见过的反应结果。这是通过在 Ca + H2 和 Ca + T2 反应上训练，随后预测 Ca + D2 反应来实现的。
英文摘要：
This work shows that feed-forward neural networks can predict the final ro-vibrational state distributions of inelastic and reactive processes of the reaction of Ca $+$ H2 $\rightarrow$ CaH $+$ H in the hyperthermal regime, relevant for buffer gas chemistry. Furthermore, these models can be extended to the isotopologues of the reaction involving deuterium and tritium. In addition, we develop a neural network model that can learn across the chemical space based on the isotopologues of hydrogen. The model can predict the outcome of a reaction whose reactants have never been seen. This is done by training on the Ca $+$ H2 and Ca $+$ T2 reactions and subsequently predicting the Ca $+$ D2 reaction.
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