|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Hume-Rothery Symposium on Connecting Macroscopic Materials Properties to Their Underlying Electronic Structure: The Role of Theory, Computation, and Experiment
||Scale Bridging Materials Physics: Active Learning Workflows and Integrable Deep Neural Networks for Free Energy Function Representations in Alloys
||Krishna Garikipati, Gregory Teichert, Anirudh Natarajan, Sambit Das, Muratahan Aykol, Vikram Gavini, Anton Van der Ven
|On-Site Speaker (Planned)
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.
||Computational Materials Science & Engineering, Machine Learning, Phase Transformations