TY - JOUR
T1 - Operationalizing "Material Similarity" for Domain Adaptation: A Definitional Boundary for Composition-Transfer Learning
AU - Sanjay Kulkarni
AU - Meenal Joshi
AU - Rohan Patil
AU - Aniket Deshmukh
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2023
VL - 2
IS - 1
DO - 10.68159/y707593740
SP - 17
N2 - Material similarity functions as the implicit premise underlying domain adaptation in materials informatics, yet it remains largely undefined, rendering transfer claims difficult to evaluate and compare. This work establishes a task-dependent, operational definition of material similarity for composition-transfer learning, grounded in four orthogonal dimensions: composition, structure, property behavior, and local atomic environment. By examining current usage patterns, it reveals systematic ambiguities arising from single-dimension interpretations and demonstrates how these ambiguities contribute to inconsistent transfer outcomes. The proposed framework introduces threshold-based criteria and a weighted similarity score that jointly determine when transfer is justified, alongside boundary conditions that constrain successful adaptation. Gray zones and edge cases are analyzed to show how trade-offs across dimensions must be explicitly quantified rather than implicitly assumed. The resulting formulation repositions similarity as a measurable, task-specific precondition rather than a descriptive label, enabling pre-transfer validation and standardized reporting. This shift provides a conceptual foundation for reproducible domain adaptation, aligning materials machine learning with more rigorous principles of transferability and model generalization.
UR - https://iamrp.net/y707593740
ER -