  Recent advancements in machine learning have showcased its potential to
significantly accelerate the discovery of new materials. Central to this
progress is the development of rapidly computable property predictors, enabling
the identification of novel materials with desired properties from vast
material spaces. However, the limited availability of data resources poses a
significant challenge in data-driven materials research, particularly hindering
the exploration of innovative materials beyond the boundaries of existing data.
While machine learning predictors are inherently interpolative, establishing a
general methodology to create an extrapolative predictor remains a fundamental
challenge, limiting the search for innovative materials beyond existing data
boundaries. In this study, we leverage an attention-based architecture of
neural networks and meta-learning algorithms to acquire extrapolative
generalization capability. The meta-learners, experienced repeatedly with
arbitrarily generated extrapolative tasks, can acquire outstanding
generalization capability in unexplored material spaces. Through the tasks of
predicting the physical properties of polymeric materials and hybrid
organic--inorganic perovskites, we highlight the potential of such
extrapolatively trained models, particularly with their ability to rapidly
adapt to unseen material domains in transfer learning scenarios.
