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Aprendizaje profundo evidencial para potenciales interatómicos

Han Xu^1,2, Taoyong Cui^1,3, Chenyu Tang¹

¹Shanghai Artificial Intelligence Laboratory, Shanghai, China.

Nature communications

|December 20, 2025

Resumen

Este resumen es generado por máquina.

Este estudio presenta un marco de aprendizaje profundo evidencial para potenciales interatómicos de aprendizaje automático. Ofrece una cuantificación de incertidumbre precisa para simulaciones moleculares sin costo computacional ni reducción de precisión.

Palabras clave:

aprendizaje profundo evidencial potenciales interatómicos cuantificación de incertidumbre simulaciones moleculares aprendizaje automático

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Área de la Ciencia:

Química computacional
Ciencia de materiales
Aprendizaje automático

Sus antecedentes:

Los potenciales interatómicos de aprendizaje automático (MLIPs) son cruciales para simulaciones moleculares a gran escala, ofreciendo precisión ab initio.
El aprendizaje activo expande iterativamente los conjuntos de datos de entrenamiento utilizando la incertidumbre para identificar datos fuera de distribución.
Los métodos actuales de cuantificación de incertidumbre (UQ) para MLIPs enfrentan desafíos con el gasto computacional o las compensaciones en la precisión de la predicción.

Objetivo del estudio:

Desarrollar un marco novedoso de aprendizaje profundo evidencial para UQ en MLIPs.
Lograr UQ precisa sin comprometer la eficiencia computacional o la precisión de la predicción.
Proporcionar una alternativa robusta y eficiente para UQ en simulaciones moleculares.

Principales métodos:

Se propone un marco de aprendizaje profundo evidencial para potenciales interatómicos.
El marco incorpora un diseño inspirado en la física.
La cuantificación de incertidumbre se integra directamente en el modelo de aprendizaje profundo.

Principales resultados:

El método propuesto logra UQ con una sobrecarga computacional mínima.
Se mantiene la precisión de la predicción, superando a los métodos UQ existentes en diversos conjuntos de datos.
Se demostraron aplicaciones en la exploración de configuraciones atómicas para potenciales universales y de agua.

Conclusiones:

El marco de aprendizaje profundo evidencial ofrece una solución UQ eficiente y precisa computacionalmente para MLIPs.
Este enfoque mejora la confiabilidad de las simulaciones moleculares a gran escala.
El método muestra un potencial significativo para avanzar en la simulación molecular y el descubrimiento de materiales.