Machine learning and big scientific data.

Area of Science:

• Scientific computing
• Artificial intelligence
• Materials science

Background:

• Large-scale experiments at UK national facilities (e.g., Diamond Light Source, ISIS Neutron and Muon Facility) generate vast amounts of 'Big Scientific Data'.
• Increasing demand for scientists to utilize advanced machine learning (ML) and artificial intelligence (AI) for data pipeline automation and scientific discovery.
• Deep learning has achieved significant breakthroughs in commercial applications (e.g., object recognition, natural language processing) and scientific problems (e.g., AlphaFold for protein folding).

Purpose of the Study:

• To review challenges and opportunities for AI in advancing materials science.
• To discuss the importance of developing realistic ML benchmarks using Big Scientific Data.
• To explore the transformative potential of deep learning for scientific problems beyond protein folding.

Main Methods:

• Review of initial ML applications at Rutherford Appleton Laboratory (RAL).
• Focus on AI challenges and opportunities in materials science.
• Discussion on creating ML benchmarks with Big Scientific Data from diverse scientific domains.

Main Results:

• Highlights the potential of AI and ML to address the challenges of Big Scientific Data.
• Identifies specific areas within materials science where AI can drive advancements.
• Introduces initial examples of a 'scientific machine learning' benchmark suite.

Conclusions:

• AI and ML are crucial for extracting scientific discoveries from large experimental datasets.
• The development of robust ML benchmarks is essential for advancing scientific AI.
• The proposed benchmarks will facilitate research into AI applications across various scientific domains.