In-time machine learning hybrid simulation methods

A machine learning simulation 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 the atoms via a first model having a first accuracy during a first period of the machine learning simulation;
• dynamically evolving chemical reactions of the atoms via a second model having a second accuracy higher than the first accuracy during a second period of the machine learning simulation;
• identifying a flagging event to start and/or stop the second period of the machine learning simulation, the evolving simulation determining 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 simulation method of determining a physical state of interaction between atoms from one or more physical properties of the atoms, the method comprising:

• dynamically evolving the atoms via a first model having a first accuracy during a first period of the machine learning simulation, the first model is a classical force field or low-fidelity machine learning interatomic potential (MLIP) model;
• dynamically evolving chemical reactions of the atoms via a second model having a second accuracy higher than the first accuracy during a second period of the machine learning simulation, the second model is machine learning interatomic potential (MLIP);
• and
• identifying a flagging event to start and/or stop the second period of the machine learning simulation, the evolving simulation determining the physical state of interaction between the atoms.