5 papers
Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking
Afroditi Kolomvaki, Fangshuo Liao, Evan Dramko +2
We investigate the convergence guarantee of two-layer neural network training with Gaussian randomly masked inputs. This scenario corresponds to Gaussian dropout at the input level…
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…