14 citations · 17 across the 3 of their papers we have counts for
3 papers
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
Fedor S. Avilov, Roman A. Eremin, Semen A. Budennyy +1
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliabilit…
Unleashing the power of novel conditional generative approaches for new materials discovery
Lev Novitskiy, Vladimir Lazarev, Mikhail Tiutiulnikov +6
For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI ha…
Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models
Alexey N. Korovin, Innokentiy S. Humonen, Artem I. Samtsevich +4
The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently…