Virtual Screening of Novel Eco-Friendly Gaseous Dielectrics through Dimensionless Bond Decomposition and Machine Learning Algorithm

Identification of environmentally friendly gaseous dielectrics
to replace the most potent greenhouse gas SF6 is urgently
desired in the worldwide high-voltage electrical industry. However,
the great challenge for SF6-free technology remains because
of numerous contradictory requirements it has to meet simultaneously:
high dielectric strength, low boiling points, low global warming potential,
high arc quenching capability, low acute/subchronic inhalation toxicity,
and low flammability. Herein, the chemical bonds are revealed to be
the universal, unique, and unified descriptors to develop the predictive
models for efficient virtual screening of novel gaseous dielectrics.
By means of the automatic bond decomposition mechanism toward the
dimensionless SMILES formula, excellent correlations between experiments
and theory have been obtained successfully for eight types of key
properties of the insulation gases using the optimized artificial
neural networks. The bond-based machine-learning algorithm is stable
to both training and test sets within the leveraging applicability
domains. Mechanistic interpretations of the inherent coupling effect
have been carried out by the normalized importance of weights of the
bond descriptors. The bond-based networks were applied to a total
of 3727 C, H, N, O, S, and F-containing compounds as curated from
PubChem. Properties of each species were predicted, and the overall
performance was ordered by scoring with respect to SF6.
Although no gas could be identified to be superior to SF6 in all aspects, a shortlist of promising replacement gases with
well-balanced dielectric performance has been found by virtual screening
and might stimulate experimental synthesis and tests for practical
use. Moreover, the present work provides guidelines for the rational
design of structural characteristics of novel compounds influential
for gaseous dielectrics.