| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
A New AI/ML Framework for Materials Innovation |
| Author(s) |
Surya R. Kalidindi |
| On-Site Speaker (Planned) |
Surya R. Kalidindi |
| Abstract Scope |
A novel information gain-driven Bayesian AI/ML (artificial intelligence/machine learning) framework is presented with the following main features: (i) explicit consideration of the physics parameters as inputs (i.e., regressors) in the formulation of process-structure-property (PSP) surrogate models needed to drive materials improvement workflows; (ii) information gain-driven autonomous workflows for training efficient AI/ML surrogates to otherwise computationally expensive physics-based simulations; (iii) versatile feature engineering for multiscale material internal structure using the formalism of n-point spatial correlations; (iv) amenable to a broad suite of surrogate model building approaches (including Gaussian Process regression (GPR), convolutional neural networks (CNN)); and (v) Markov chain Monte Carlo (MCMC)-based computation of posteriors for physics parameters using all available experimental observations (usually disparate and sparse). The benefits of this framework in supporting accelerated design and development of heterogeneous materials will be demonstrated using multiple case studies. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, ICME, Computational Materials Science & Engineering |