Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.180
Publication date: 18th December 2023
Supervised machine learning (ML) has proven to be an incredibly powerful enabling technology for electronic structure calculations. We now routinely see highly accurate predictions at a fraction of the computational cost of traditional methods. Machine learning has enabled electronic structure simulation at length-scales which previously seemed out-of-reach. The number of papers which use machine learning is increasing exponentially, with no signs of slowing down.
Unfortunately, these advantages have come at a high price - ML models such as deep neural networks provide no intuitive explaination about how they arrived at a particular prediction. They also offer limited generalization capabilities: each problem is treated as a new one. Unlike simple models that appear in textbooks (and inform our intuition), machine learning models are often treated as black boxes by practitioners. Even popular explainability tools fall short - they highlight correlations rather than causation.
I will discuss some of the failures and limitations of machine learning and provide examples which attempt to provide generalized insight and intutition.
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