.png){kind=icon}
Return
Machine-learned potentials for next-generation matter simulations
.png){kind=icon}
PRE
AI
DOI：10.1038/s41563-020-0777-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
NEURAL-NETWORK POTENTIALS
MOLECULAR-DYNAMICS
ENERGY SURFACES
FORCE-FIELD
MODELS
REPRESENTATIONS
COMMUNICATION
CHEMISTRY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
38.5
Papers:
6.8K
Citations:
11.5W
Organization
Cited Papers
ShareSaveShareSaveSynthesis and structure of free-standing germanium quantum dots and their application in live cell imaging.png)
RSC Advances
OAAI
RSC AdvancesIF0
OAAIShareSaveA cobalt tungstate compound sensing electrode for hydrogen detection based upon mixed-potential type sensorsRSC AdvancesOAAI
RSC AdvancesIF0
OAAIShareSaveShareSaveMachine learning enables long time scale molecular photodynamics simulationsChemical ScienceOAAI
Chemical ScienceIF7.4
OAAIShareSave
View more