Crystal Structure Representation for Neural Networks using Topological Approach

Aleksandr V. Fedorov, Ivan V. Shamanaev

Результат исследования: Научные публикации в периодических изданияхстатьярецензирование

5 Цитирования (Scopus)


In the present work we describe a new approach, which uses topology of crystals for physicochemical properties prediction using artificial neural networks (ANN). The topologies of 268 crystal structures were determined using ToposPro software. Quotient graphs were used to identify topological centers and their neighbors. The topological approach was illustrated by training ANN to predict molar heat capacity, standard molar entropy and lattice energy of 268 crystals with different compositions and structures (metals, inorganic salts, oxides, etc.). ANN was trained using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Mean absolute percentage error of predicted properties was ≤8 %.

Язык оригиналаанглийский
Номер статьи1600162
Число страниц7
ЖурналMolecular Informatics
Номер выпуска8
СостояниеОпубликовано - авг. 2017


Подробные сведения о темах исследования «Crystal Structure Representation for Neural Networks using Topological Approach». Вместе они формируют уникальный семантический отпечаток (fingerprint).