fig1

PhyMLP: an automated strategy for machine-learning potential construction via data fusion and adaptive point-sampling

Figure 1. The workflow of PhyMLP automated sampling starts with file preparation and is divided into four sections: experiment, Rose, DFT, and traditional potential function. In this diagram, E represents energy, F represents force, and σ represents stress. PhyMLP: Physics-strengthened machine-learning potential; DFT: density functional theory; P-V: pressure-volume; VASP: Vienna Ab initio Simulation Package; BCC: body-centered cubic; FCC: face-centered cubic; HCP: hexagonal close-packed; EAM: embedded-atom method; MTP: moment tensor potential.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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