| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Accelerating Atomistic Monte Carlo Simulations with Autoregressive Models |
| Author(s) |
Rafael Gomezbombarelli, James Damewood |
| On-Site Speaker (Planned) |
Rafael Gomezbombarelli |
| Abstract Scope |
In order for high-throughput virtual screening platforms to overcome the challenges of modeling the diversity and complexity of metal systems, modern algorithms must efficiently leverage computational resources. While Monte Carlo simulations are frequently used to predict phase stability or material properties at equilibrium, these calculations can be prohibitively expensive when exploring phase transitions or systems involving many components. New neural network-based generative models have developed into powerful sampling methods that can be integrated into existing Monte Carlo workflows to accelerate traditional approaches. We will present predictions of these machine learning led simulations on tasks relevant for computational discovery in metals. We will examine the sampling of ground states of a Copper-Gold system and the miscibility gap in Nickel-Gold. We will discuss the extension of the model to study multicomponent systems and analyze the scaling of the method with system size and evaluate the potential for usage within high-throughput screening strategies. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, High-Entropy Alloys |