Combining Reinforcement Learning with Graph Convolutional Neural Networks for Efficient Design of TiAl/TiAlN Atomic-Scale Interfaces

TL;DR

This paper introduces a novel approach combining reinforcement learning with graph convolutional neural networks to efficiently identify optimal TiAl/TiAlN interface structures with high adhesion, accelerating ceramic coating design.

Contribution

It presents a new method integrating reinforcement learning and GCNs to rapidly optimize atomic-scale interfaces, surpassing traditional computational techniques.

Findings

Optimal structures feature Al doping near the interface.

Higher bonding strength correlates with specific doping patterns.

Method significantly reduces computational time for interface design.

Abstract

Ti/TiN coatings are utilized in a wide variety of engineering applications due to their superior properties such as high hardness and toughness. Doping Al into Ti/TiN can also enhance properties and lead to even higher performance. Therefore, studying the atomic-level behavior of the TiAl/TiAlN interface is important. However, due to the large number of possible combinations for the 50 mol% Al-doped Ti/TiN system, it is time-consuming to use the DFT-based Monte Carlo method to find the optimal TiAl/TiAlN system with a high work of adhesion. In this study, we use a graph convolutional neural network as an interatomic potential, combined with reinforcement learning, to improve the efficiency of finding optimal structures with a high work of adhesion. By inspecting the features of structures in neural networks, we found that the optimal structures follow a certain pattern of doping Al near the interface. The electronic structure and bonding analysis indicate that the optimal TiAl/TiAlN structures have higher bonding strength. We expect our approach to significantly accelerate the design of advanced ceramic coatings, which can lead to more durable and efficient materials for engineering applications.