Joint E-MRS/MRS Tutorial on “Artificial Intelligence for Advancing Materials Science”
Sunday, May 24
Congress & Exhibition Centre, Strasbourg
- MRS: Carla P. Gomes (Department of Computer Science, Cornell University, USA)
- E-MRS: Stefan Sandfeld (Micromechanical Materials Modelling, University of Freiberg, Germany)
- Carla P. Gomes (Department of Computer Science, Cornell University, USA)
- Stefan Sandfeld (Micromechanical Materials Modelling, University of Freiberg, Germany)
- NN (European Community on Computational Methods in Applied Sciences)
- Kristofer Reyes (Materials Design and Innovation, University at Buffalo, USA)
The joint E-MRS/MRS tutorial on “Artificial Intelligence (AI) for Advancing Materials Science” aims to introduce scientists and engineers from the field of materials science to a novel computer based planning and execution of experiments as well as approach of data analysis in order to support the research and development of new materials and processes. It is believed that AI-based working techniques will significantly change the way how research and development will be carried out in the future. The aim of the workshop is to acquaint the participants with the state-of-the-art methods applied in AI-based research and development.
Importance of AI-based working methods for young researchers and their careers:
In recent years, we have seen exciting progress in artificial intelligence (AI). AI systems are now reaching human-level and even superhuman-level performance on a range of tasks, such as speech recognition, image interpretation, machine translation (Google translate), and gameplay (DeepBlue for Chess, Watson for Jeopardy, and AlphaGo for Go). There is a general belief that AI is poised to radically transform many components of our society and economy. Just to mention one example, self-driving cars and trucks, which incorporate real-time image recognition and control, are close to becoming a reality.
Given these advances in AI and machine learning (ML), the scientific community has taken note and is exploring the use of AI for scientific discovery. Deep learning and AI reasoning methods enable scientists to uncover new types of structure in large amounts of data and design new experiments leading to the most promising areas for further experimentation. These techniques also open up new opportunities for accelerating materials research and discovery. To reap the full potential of these developments, it is critical to educate the materials science community, in particular students and young researchers: Not only can AI dramatically accelerate the pace of materials discovery; AI can also reduce the cost of scientific discovery.
Outline for the workshop:
The most promising AI approaches for scientific discovery, in general, and materials science, in particular, involves a combination of AI techniques: Machine learning, deep learning, search and optimization, reasoning, knowledge representation, and decision making. These techniques should be combined with human insights, simulations, and experimentation.
Schedule (Suggestion: each lecture 45 minutes: 2 lectures + lunch break + 2 lectures + coffee break + 2 lectures)
- AI for Materials Discovery: Overview (Cara P. Gomes)
Overview of AI techniques for Materials Science
Problem representation, problem solving, and computational complexity
- Machine Learning Intro (Stefan Sandfeld)
Concepts in statistical and machine learning, Data processing techniques
- Machine Learning – Supervised Learning (Stefan Sandfeld)
Parametric Models (Statistical Models, Classification and regression with models), Non-parametric models (k-Nearest Neighbors, Decision Trees, Support Vector Machines, Gaussian processes, Ensemble learning)
- Machine Learning – Deep Learning (NN)
Artificial Neural Networks (Neural Network Definition and Elements, Custom layers, Activation Functions, Loss functions), Deep Neural Networks (CNN, RNN)
- Machine Learning – Unsupervised learning and Deep Generative Models (Carla P. Gomes)
Clustering (Latent variable models (e.g., EM, PCA, ICA, NMF), Neural network-based models (Autoencoders, Deep Generative Models)
- Sequential Decision Making and Reinforcement Learning (Kristofer Reyes)
Bayesian Optimization, Markov Decision Processes, Reinforcement Learning