3 papers
cs.LG2026
Learning from almost nothing: How neural networks survive heavy input corruption
Justin Tahmassebpur, Asadullah Bhuiyan, Hyejin Kim +1
Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnability. Here we examine attribut…
cond-mat.mtrl-sci2026
A geometric basis for materials families in inorganic solids
Justin Tahmassebpur, Sarvesh Chaudhari, Cristóbal Méndez +6
The thermodynamic stability of inorganic solids spans a vast compositional space, yet materials scientists have long organized their intuition around a manageable number of materia…
cond-mat.mtrl-sci2025
Effective Atom Theory: Gradient-Driven ab initio Materials Design
Justin Tahmassebpur, Brandon Li, Boris Barron +3
We introduce Effective Atom Theory (EAT), a framework that transforms combinatorial materials design into a smooth, gradient-driven optimization within density functional theory (D…