  One of the main goals and challenges of materials discovery is to find the
best candidates for each interest property or application. Machine learning
rises in this context to efficiently optimize this search, exploring the
immense materials space, consisting of simultaneously the atomic,
compositional, and structural spaces. Topological insulators, presenting
symmetry-protected metallic edge states, are a promising class of materials for
different applications. However, further, development is limited by the
scarcity of viable candidates. Here we present and discuss machine
learning-accelerated strategies for searching the materials space for
two-dimensional topological materials. We show the importance of detailed
investigations of each machine learning component, leading to different
results. Using recently created databases containing thousands of ab initio
calculations of 2D materials, we train machine learning models capable of
determining the electronic topology of materials, with an accuracy of over 90%.
We can then generate and screen thousands of novel materials, efficiently
predicting their topological character without the need for a priori structural
knowledge. We discover 56 non-trivial materials, of which 17 novel insulating
candidates for further investigation, for which we corroborate their
topological properties with density functional theory calculations. This
strategy is 10$\times$ more efficient than the trial-and-error approach while
few orders of magnitude faster and is a proof of concept for guiding improved
materials discovery search strategies.
