4 papers
On The Finetuning of MLIPs Through the Lens of Iterated Maps With BPTT
Evan Dramko, Yizhi Zhu, Aleksandar Krivokapic +4
Accurate structural relaxation is critical for advanced materials design. Traditional approaches built on physics-derived first-principles calculations are computationally expensiv…
Completion of partial structures using Patterson maps with the CrysFormer machine learning model
Tom Pan, Evan Dramko, Mitchell D. Miller +2
Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been appli…
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
Evan Dramko, Yihuang Xiong, Yizhi Zhu +4
Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale f…
RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs
Tom Pan, Evan Dramko, Mitchell D. Miller +2
Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce , a hybrid model that exploits the str…