Machine learning interatomic potential shell simulation methods

A machine learning interatomic potential (MLIP) method of determining a physical state of interaction between atoms from one or more physical properties of the atoms for use in a chemical system, the method comprising:

• dynamically evolving a first subset of the atoms via the MLIP within a central region based on the one or more physical properties of the atoms in a number of simulation steps while fixing a second subset of the atoms surrounding the central region in a shell during at least a portion of the number of simulation steps to determine the physical state of interaction between the atoms;
• and
• using the physical state of interaction between the atoms to control the chemical system.

A machine learning interatomic potential (MLIP) method of determining a physical state of interaction between atoms from one or more physical properties of the atoms, the method comprising:

• dynamically evolving a first subset of the atoms via the MLIP within a central high-fidelity region based on the one or more physical properties of the atoms in a number of simulation steps while fixing a second subset of the atoms surrounding the central high-fidelity region in a shell during at least a portion of the number of simulation steps to determine the physical state of interaction between the atoms and to only scale computations within a size of the central high-fidelity region and not the shell.