Towards physics-informed explainable machine learning and causal models for materials research
A Ghosh - Computational Materials Science, 2024 - Elsevier
From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …
optimization of process parameters for achieving best performance–all key facets of …
Preparation of a Water–Gas Shift Database to Evaluate the Performance of Noble Metal Catalysts Using Theory-Guided Machine Learning
J Chattoraj, B Hamadicharef, YNA Syadzali… - ACS …, 2023 - ACS Publications
The pursuit of catalyst discovery through machine learning has garnered substantial
attention in recent years. The effectiveness of such a framework in uncovering appropriate …
attention in recent years. The effectiveness of such a framework in uncovering appropriate …
Structural and Catalytic Properties of Rh–CeO2/MWCNT Composite Catalysts
LS Kibis, AV Zadesenets, TY Kardash… - Journal of Structural …, 2024 - Springer
Composite catalysts consisting of Rh–CeO x structures supported on the surface of multi-
walled carbon nanotubes (MWCNTs) are studied. For supported active components (Rh …
walled carbon nanotubes (MWCNTs) are studied. For supported active components (Rh …
СТРУКТУРНЫЕ И КАТАЛИТИЧЕСКИЕ СВОЙСТВА КОМПОЗИТНЫХ КАТАЛИЗАТОРОВ RH-CEO 2/МУНТ
ЛС КИБИС, АВ ЗАДЕСЕНЕЦ, ТЮ КАРДАШ… - ЖУРНАЛ …, 2024 - elibrary.ru
Исследованы композитные катализаторы, представляющие собой композиции Rh-CeO
x, нанесенные на поверхность многостенных углеродных нанотрубок (МУНТ). Для …
x, нанесенные на поверхность многостенных углеродных нанотрубок (МУНТ). Для …