49 citations · 92 across the 5 of their papers we have counts for
3 papers · 1 filter
Orb: A Fast, Scalable Neural Network Potential
Mark Neumann, James Gin, Benjamin Rhodes +5
We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable unde…
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials
Hitarth Choubisa, Md Azimul Haque, Tong Zhu +4
The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the dive…
Interpretable discovery of new semiconductors with machine learning
Hitarth Choubisa, Petar Todorović, Joao M. Pina +5
Machine learning models of materials accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated resul…