# Machine Learning and Big Scientific Data

arXiv: https://arxiv.org/abs/1910.07631 · HTML: https://mlsanity.com/paper/1910.07631/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs](https://mlsanity.com/paper/2110.01717/) (2021)
- [MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems](https://mlsanity.com/paper/2110.11466/) (2021)
- [Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)](https://mlsanity.com/paper/2205.14630/) (2022)
- [Neural Context Flows for Meta-Learning of Dynamical Systems](https://mlsanity.com/paper/2405.02154/) (2024)
- [Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications](https://mlsanity.com/paper/2310.20425/) (2023)
- [A Triumvirate of AI Driven Theoretical Discovery](https://mlsanity.com/paper/2405.19973/) (2024)
- [Spline parameterization of neural network controls for deep learning](https://mlsanity.com/paper/2103.00301/) (2021)
- [Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data](https://mlsanity.com/paper/2206.06422/) (2022)
- [Engineering Systems for Data Analysis Using Interactive Structured Inductive Programming](https://mlsanity.com/paper/2503.14488/) (2025)
- [An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data](https://mlsanity.com/paper/2306.07291/) (2023)
- [Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams](https://mlsanity.com/paper/2408.02698/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data](https://mlsanity.com/paper/2402.03116/) (2024)
- [Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling](https://mlsanity.com/paper/2411.10483/) (2024)
- [An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry](https://mlsanity.com/paper/2506.11100/) (2025)

## Abstract (arXiv)

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory site at Harwell near Oxford. Such "Big Scientific Data" comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility, and the UK's Central Laser Facility. Increasingly, scientists are now needing to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and also to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now also used deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, they have been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the Rutherford Appleton Laboratory, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from a number of different scientific domains. We conclude with some initial examples of our "SciML" benchmark suite and of the research challenges these benchmarks will enable.