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Aproximaciones de matrices y tensores de bajo rango para la compresión de potenciales interatómicos de aprendizaje

Igor Vorotnikov1, Fedor Romashov1, Nikita Rybin2,3

  • 1Faculty of Computer Science, HSE University, Pokrovsky Boulevard 11, Moscow 109028, Russian Federation.

The Journal of chemical physics
|December 30, 2025
PubMed
Resumen

Los potenciales interatómicos de aprendizaje automático (MLIP) ahora se pueden comprimir hasta en un 50% utilizando la factorización de bajo rango, lo que mejora significativamente la eficiencia computacional sin sacrificar la precisión en las simulaciones de ciencia de materiales.

Palabras clave:
potenciales interatómicos de aprendizaje automáticocompresiónfactorización de bajo rangociencia de materialeseficiencia computacional

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Área de la Ciencia:

  • Ciencia de Materiales Computacional
  • Aprendizaje Automático en Física
  • Computación Científica

Sus antecedentes:

  • Los potenciales interatómicos de aprendizaje automático (MLIP) ofrecen una precisión superior a los campos de fuerza tradicionales.
  • La flexibilidad de los MLIP depende de los conjuntos de bases que describen los entornos atómicos locales.
  • La reducción de los parámetros de MLIP es clave para simulaciones eficientes.

Objetivo del estudio:

  • Desarrollar y validar una metodología de compresión para MLIP.
  • Mejorar la eficiencia computacional de las simulaciones de MLIP.
  • Explorar el impacto de la compresión en la precisión de la superficie de energía potencial.

Principales métodos:

  • Factorizaciones de matrices y tensores de bajo rango con restricciones de rango fijo.
  • Algoritmo de aumento de rango automático para optimizar el ajuste del potencial.
  • Verificación utilizando Moment Tensor Potential (MTP) y Atomic Cluster Expansion (ACE).

Principales resultados:

  • Se logró hasta un 50% de compresión de los MLIP sin pérdida de precisión.
  • Se demostró la compresión exitosa en sistemas multicomponente (aleación Mo-Nb-Ta-W, sal LiF-NaF-KF, cristal de glicina).
  • Se validó la universalidad de la metodología de compresión en diferentes modelos de MLIP.

Conclusiones:

  • La factorización de bajo rango proporciona un método eficaz para comprimir MLIP.
  • El enfoque desarrollado mejora la eficiencia de la simulación manteniendo el poder predictivo.
  • Esta metodología es ampliamente aplicable a varios modelos de MLIP en ciencia de materiales.