Mortazavi, B., Podryabinkin, E. V., Roche, S., Rabczuk, T., Zhuang, X., & Shapeev, A. V. (2020). Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures.
Citación estilo ChicagoMortazavi, Bohayra, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, y Alexander V. Shapeev. Machine-learning Interatomic Potentials Enable First-principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/borophene Heterostructures. 2020.
Cita MLAMortazavi, Bohayra, et al. Machine-learning Interatomic Potentials Enable First-principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/borophene Heterostructures. 2020.