2 papers
cond-mat.mtrl-sci2026
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor +1
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful t…
cond-mat.mtrl-sci2026
Direct Summation of the Madelung Constant Using Axial Multipoles
Joven V. Calara, Jan D. Miller
We present an absolutely convergent real-space summation method for electrostatic potentials in ionic lattices. The method constructs the lattice from translated axial multipole un…