Setting standards for data driven materials science

Published

October 1, 2024

Author(s)

Keith Butler, Kamal Choudhary, Gabor Csanyi, Alex Ganose, Sergei Kalinin, Dane Morgan

Abstract

A young Steve Jobs once called computers 'bicycles for the mind' – he was referring to the dramatic decrease in the energetic cost of transportation that could be obtained with the bicycle, which breaks all scaling laws for how efficiently an animal can achieve motion. The creativity of new approaches to old materials science problems facilitated by the dramatic uptake of machine learning (ML) is a testament to this notion. The dramatic uptake of ML in our subject has been facilitated by exceptional efforts to provide open datasets, e.g. NIST-JARVIS, NOMAD, Materials Project, Aflow, OQMD and many others, as well as the extremely high quality of openly available software packages such as scikit-learn, PyTorch, JAX, Quantum Espresso, LAMMPS. These resources, along with continued extraordinary developments in hardware, are helping a wide-range of researchers integrate ML into their work.

Citation

npj Computational Materials

Pub Type

Journals

Citation

Butler, K. , Choudhary, K. , Csanyi, G. , Ganose, A. , Kalinin, S. and Morgan, D. (2024), Setting standards for data driven materials science, npj Computational Materials (Accessed November 25, 2025)