Building machine learning assisted phase diagrams: Three chemically relevant examples

Abstract

In this work, we present a systematic procedure to build phase diagrams for chemically relevant properties by the use of a semi-supervised machine learning technique called uncertainty sampling. Concretely, we focus on ground state spin multiplicity and chemical bonding properties. As a first step, we have obtained single-eutectic-point-containing solid–liquid systems that have been suitable for contrasting the validity of this approach. Once this was settled, on the one hand, we built magnetic phase diagrams for several Hooke atoms containing a few electrons (4 and 6) trapped in spheroidal harmonic potentials. Changing the parameters of the confinement potential, such as curvature and anisotropy, and interelectronic interaction strength, we have been able to obtain and rationalize magnetic phase transitions flipping the ground state spin multiplicity from singlet (nonmagnetic) to triplet (magnetic) states. On the other hand, Bader’s analysis is performed upon helium dimers confined by spherical harmonic potentials. Covalency is studied using descriptors as the sign for Δ ρ( r C) and H( r C), and the dependency on the degrees of freedom of the system is studied, i.e., potential curvature ω2 and interatomic distance R. As a result, we have observed that there may exist a covalent bond between He atoms for short enough distances and strong enough confinement. This machine learning procedure could, in principle, be applied to the study of other chemically relevant properties involving phase diagrams, saving a lot of computational resources.