72 citations · 161 across the 13 of their papers we have counts for
33 papers
Approximate label symmetries improve data efficiency
Scott Y. H. Kim, Mathis Lechaume-Robert, O. Anatole von Lilienfeld
Enforcing feature symmetries in machine learning (ML) models is a common strategy to mitigate data scarcity. Confirming expectations from statistical learning theory, we show that…
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber +1
Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest P…
Antisymmetry rules of response properties in certain chemical spaces
Takafumi Shiraogawa, Simon León Krug, Masahiro Ehara +1
Understanding chemical compound space (CCS), a set of molecules and materials, is crucial for the rational discovery of molecules and materials. Concepts of symmetry have recently…
Alchemical insights into approximately quadratic energies of iso-electronic atoms
Simon León Krug, O. Anatole von Lilienfeld
Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use t…
High-Tc superconductor candidates proposed by machine learning
Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim +1
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced com…
Combining Hammett constants for -machine learning and catalyst discovery
V. Diana Rakotonirina, Marco Bragato, Stefan Heinen +1
We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning and to lig…