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Found 6 papers, 5 papers with code
Stoichiometry Representation Learning with Polymorphic Crystal Structures
Despite the recent success of machine learning (ML) in materials science, its success heavily relies on the structural description of crystal, which is itself computationally demanding and occasionally unattainable.
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy.
Shift-Robust Molecular Relational Learning with Causal Substructure
To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables.
Conditional Graph Information Bottleneck for Molecular Relational Learning
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications.
Predicting Density of States via Multi-modal Transformer
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials.
Step Size Optimization
This paper proposes a new approach for step size adaptation in gradient methods.
