Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling

Key points

• Strictly local models enable efficient spatial parallelization but neglect long-range interactions, whereas existing electrostatic extensions introduce global dependencies that hinder distributed computation.1
• Here, we overcome this limitation by analytically separating the long-range electrostatic forces and Born effective charges (BECs) into a global electrostatics calculation and strictly local derivatives of learned latent charges.1
• We further demonstrate its capabilities by recovering the characteristic slim polarization hysteresis of relaxor \ce{Pb(Mg{1/3}Nb{2/3})O3}, which is obscured by finite-size effects in smaller simulations; predicting a room-temperature field-driven ionic conductivity of $6.8 \pm 0.7$ mS/cm in amorphous \ce{Li{0.9}NbO{0.9}Cl{4.1}}; and modeling interfacial thermal transport across Si/diamond interfaces with slab-corrected electrostatics.1
• By combining long-range physics, electrical-response inference, and distributed computation, this framework extends predictive atomistic modeling toward device-relevant length scales.1

Sources (1)

• [1]Scalable long-range machine learning interatomic potentials with electrical response for atomistic material modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:44 PM

  Accurate atomistic simulations of materials under electric fields require long-range electrostatics and electrical-response modeling at length scales beyond the reach of conventional first-principles methods.

  Strictly local models enable efficient spatial parallelization but neglect long-range interactions, whereas existing electrostatic extensions introduce global dependencies that hinder distributed computation.

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