Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning

Youngwoo Cho, Seunghoon Yi, Wooil Yang, Sungmo Kang, Young-Woo Son, Jaegul Choo, Joonseok Lee, Soo Kyung Kim, Hongkee Yoon

Keywords: machine learning interatomic potentials, equivariance, sparsity-promoting

TL;DR: This work proposes a sparsity-promoting fine-tuning method for equivariant MLIPs that integrates equivariant constraints with selective parameter pruning.

Abstract: Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces. However, they often require domain-specific calibration due to physicochemical diversity as well as mismatches between practical computational settings and those used in constructing the pre-training data. To address this, we propose a sparsity-promoting fine-tuning method that selectively updates model parameters by exploiting the structural properties of E(3)-equivariant materials foundation models. On energy and force prediction tasks across molecular and crystalline benchmarks, our method matches or surpasses full fine-tuning and equivariant low-rank adaptation while updating only ~3 \% of parameters, and in some cases as little as \~0.5 \%. Beyond energy and force calibration, we further demonstrate task generalizability by applying our method to magnetic moment prediction and magnetism-aware total energy modeling. Finally, analysis of sparsity patterns reveals physically interpretable signatures, such as enhanced $d$-orbital contributions in transition metal systems. Overall, our results establish sparsity-promoting fine-tuning as a flexible and interpretable method for domain specialization of equivariant materials foundation models.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 2948