Accurate representation of formation energies of crystalline alloys with many components

Abstract

In this paper I propose a new model for representing the formation energies of multicomponent crystalline alloys as a function of atom types. In the cases when displacements of atoms from their equilibrium positions are not large, the proposed method has a similar accuracy as the state-of-the-art cluster expansion method, and a better accuracy when the fitting dataset size is small. The proposed model has only two tunable parameters-one for the interaction range and one for the interaction complexity.

Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials

“…This is just one of a class of recently popularized machine learning methods for creating nonparametric interatomic potentials, which has been shown to be very successful in tackling difficult materials modelling problems, ranging from investigating the structure of amorphous materials (carbon (Deringer et al, 2017(Deringer et al, , 2019, silicon (Bartók et al, 2018)), the mechanics of metals (tungsten (Szlachta et al, 2014), iron (Dragoni et al, 2018)) to molecular liquids such (water (Bartók et al, 2013a), methane (Veit et al, 2019). There are many alternatives, using other regression frameworks, such as artificial neural networks (Behler and Parrinello, 2007) and even linear regression (Shapeev, 2017;Drautz, 2019). All these methods are improvable, since using more input data typically leads to more accurate potentials, due to the nonparametric nature of the functional forms.…”

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

“…In the on-lattice model, LRP, 18 the energy of each configuration is partitioned into contributions of the separate atomic environments of each atom as…”

Экспериментально-теоретическое исследование эволюции атомной структуры высокоэнтропийных сплавов на основе Fe, Cr, Ni, Mn и Co при термическом и радиационном старении

“…Для моделирования эволюции атомной структуры необходимо описывать зависимость энергии системы от конфигурации атомных окружений. В данной работе для этой цели использовался малоранговый потенциал межатомного взаимодействия на решетке (выше был обозначен LRP) [48]. LRP относится к классу машиннообучаемых потенциалов, его основной задачей является интерполяция результатов квантово-механических расчетов.…”

“…Использование облучения для ускорения диффузионных процессов позволяет детектировать структурные изменения при более низкихтемпературах [46,47]. Теоретическая часть исследования проводится с помощью атомистического Монте-Карло с использованием потенциала [48].…”