| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
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| Symposium
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Hume-Rothery Symposium on Connecting Macroscopic Materials Properties to Their Underlying Electronic Structure: The Role of Theory, Computation, and Experiment
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| Presentation Title |
Scale Bridging Materials Physics: Active Learning Workflows and Integrable Deep Neural Networks for Free Energy Function Representations in Alloys |
| Author(s) |
Krishna Garikipati, Gregory Teichert, Anirudh Natarajan, Sambit Das, Muratahan Aykol, Vikram Gavini, Anton Van der Ven |
| On-Site Speaker (Planned) |
Krishna Garikipati |
| Abstract Scope |
The free energy encodes the thermodynamic coupling between mechanics and chemistry within continuum descriptions of non-equilibrium materials phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains results in a free energy description that occupies a high-dimensional space. Scale bridging between the electronic structure of a solid and continuum descriptions of its non-equilibrium behavior can be realized with integrable deep neural networks (IDNN) that are trained to free energy derivative data generated by first-principles statistical mechanics simulations and then analytically integrating to recover a free energy density function. Here we combine the IDNN with an active learning workflow to ensure well-distributed sampling of the free energy derivative data in high-dimensional input spaces, thereby enabling true scale bridging between first-principles statistical mechanics and continuum phase field models. As a prototypical material systems we focus on applications in Ni-Al alloys and in the battery cathode material: LixCoO2. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Phase Transformations |