collaborators

5 papers

cs.NE2026

AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions

Esteban Real, Mirko Rossini, Connal de Souza +7

Transcendental functions, such as the exponential, are central to scientific computing, yet they cannot be natively calculated by digital hardware. Instead, computers must approxim…

cond-mat.supr-con2026

Search for thermodynamically stable ambient-pressure superconducting hydrides in GNoME database

Antonio Sanna, Tiago F. T. Cerqueira, Ekin Dogus Cubuk +2

Hydrides are considered to be one of the most promising families of compounds for achieving high temperature superconductivity. However, there are very few experimental reports of…

cond-mat.mtrl-sci2025

Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures

Sean Sullivan, Seungjun Lee, Nathan J. Szymanski +4

Ferromagnetic pyrochlore and spinel materials with topological flat bands are of interest for their potential to exhibit a giant anomalous Hall effect (AHE). In this work, we prese…

cond-mat.mtrl-sci2025

System of Agentic AI for the Discovery of Metal-Organic Frameworks

Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan +12

Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical…

cond-mat.mtrl-sci2025

A practical guide to machine learning interatomic potentials -- Status and future

Ryan Jacobs, Dane Morgan, Siamak Attarian +27

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…