Self-Consistency Training for Hamiltonian Prediction

arXiv:2403.09560v1 Announce Type: new
Abstract: Hamiltonian prediction is a versatile formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. In this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which we propose an exact training method that does not require labeled data. This merit addresses the data scarcity difficulty, and distinguishes the task from other property prediction formulations with unique benefits: (1) self-consistency training …

abstract arxiv cs.lg data highlight machine machine learning molecular science prediction science training type work