Machine Learning Aided Investigation of Reactive Chemistry at Surfaces

该项目致力于解决创建准确的机器学习潜力的挑战，特别是创建一个强大的和可转移的自动化协议，探索所需的分子-表面催化相互作用，在描述反应物和催化剂时具有相同的准确性。这将涉及开发一个有效的迭代训练协议。这将涉及确定一组合适的模拟，以及每个模拟的数据量，以包括在下一次迭代中，以探索相关的表面过程。在整个项目中，我们将逐步扩大反应的复杂性和可以应用潜力的表面。这项工作将在多个方面展开。首先，创建一个用于最佳数据生成的协议，特别是用于理解表面过程。其次，在生命前有机反应领域的测试和发展可以导致对生命起源以及表面相互作用和催化在该过程中所起作用的重要见解。生物前化学反应的主题是高度感兴趣的。本研究将开始于探讨胺基酸的合成以及胺基酸在氧化铝与矽土上的结合。这两种氧化物与在粘土上再现这些反应的最终目标有关。这是由于粘土在第一生物分子的产生方面的潜在作用而引起的兴趣。最后，我们希望探讨掺杂剂的加入和RNA表面结合复制的前景。

The project takes on tackling the challenges of creating accurate machine learning potentials, in particular creating a robust and transferable automated protocol that explores desired molecule-surface catalytic interaction, with equal accuracy in describing the reactants and the catalysts. This will involve the development of an efficient iterative training protocol. This will involve determining a suitable set of simulations to do and the amount of data from each to include in the next iteration to explore relevant surface processes. Throughout the project we will be gradually expanding the complexity of the reactions and surfaces where the potential can be applied.The work will expand on multiple fronts. Firstly, with the creation of a protocol for optimal data generation, specifically for understanding surface process. Secondly, testing and developing on the realm of prebiotic organic reaction could lead to important insights into the origins of life and the role that surface interactions and catalysis played in the process. The topic of prebiotic chemical reactions are of high interest. The research will begin with investigating amino acid synthesis and the bonding between glycine on alumina and silica. These two oxides are relevant in the final goal of reproducing these reactions on clays. This is of interest due to the potential role of clays in relation to the creation of the first biomolecules. Finally, we hope to explore the addition of dopants and the prospects of surface bound replicating of RNA.