Machine-learned potentials for next-generation matter simulations
By Pascal Friederich, Florian Hase, Jonny Proppe, Alán Aspuru-Guzik. Nature Materials, 2021
488 citations recorded by the source corpus.
The choice of simulation methods in computational materials science is driven by a fundamental trade-off: bridging large time- and length-scales with highly accurate simulations at an affordable computational cost. Venturing the investigation of complex phenomena on large scales requires fast yet accurate computational methods. We review the emerging field of machine-learned potentials, which promises to reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. This Review will summarize the basic principles of the underlying machine learning methods, the data acquisition process and active learning procedures. We highlight multiple recent applications of machine-learned potentials in various fields, ranging from organic chemistry and biomolecules to inorganic crystal structure predictions and surface science. We furthermore discuss the developments required to promote a broader use of ML potentials, and the possibility of using them to help solve open questions in materials science and facilitate fully computational materials design. Materials simulations are now ubiquitous for explaining material properties. This Review discusses how machine-learned potentials break the limitations of system-size or accuracy, how active-learning will aid their development, how they are applied, and how they may become a more widely used approach.
Subfields: Materials Chemistry, Structural Biology
View Paper: https://www.semanticscholar.org/paper/fdadbd8b22b249dc9422b2c4681f45c6f288a798
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In Materials Chemistry
- Florian Hase
- Pascal Friederich
- Alán Aspuru-Guzik
- José Miguel Hernández-Lobato
- Ryan-Rhys Griffiths
- Tommi S. Jaakkola
- Nicholas J. Schork
Citations in the corpus
- On scientific understanding with artificial intelligence (2022, background): “More Complex Systems – One obvious but nevertheless important research direction is increasing the complexity as well as the accuracy of computer simulations[28].”