  Accurate and scalable universal interatomic potentials are key for
understanding material properties at the atomic level, a task often hindered by
the steep computational scaling. Although recent developments of machine
learning potential has made significant progress, the flexibility and expansion
to a wide range of compounds within a single model seems still challenging to
build in particular for the kernel-based models. Here, we introduce the
Bayesian Committee Machine (BCM) potential to handle a wide array of
hydrocarbons in various phases (gas, clusters, liquid, and solid). The BCM
potential leverages the committee approach to bypass the poor scaling of kernel
regressors when dealing with large datasets. Its committee-based structure
allows for easy and cost-effective expansion, maintaining both transferrability
and scalability. Demonstrating its robustness, the BCM accurately models
challenging examples like the Diels-Alder reaction, structural strains, and
pi-pi interactions. Our systematic benchmarking positions the sparse BCM
potential as a promising candidate in the quest for a universal ab initio
machine learning potential.
