A computational toolkit for predicting electronic instabilities

Accurate prediction of electronic instabilities, such as charge density waves (CDWs), in low-dimensional and correlated materials requires a precise evaluation of momentum-resolved electronic susceptibilities. We present NESTOR (Nesting and Electronic Susceptibility Toolkit for Ordered Responses), an open-source Python package for computing static and dynamic Lindhard susceptibilities, χ(q) and χ(q, ω), directly from first-principles electronic structures. NESTOR interfaces seamlessly with VASP and Quantum ESPRESSO, enabling efficient Brillouin-zone integration with optional interpolation, orbital-resolved form factors, and temperature-dependent occupations. This framework quantitatively identifies ordering vectors Q_CDW and nesting conditions. Benchmark studies on representative kagome and layered metallic systems reveal strong correspondence between susceptibility maxima and experimentally observed superlattice modulations, confirming the method’s predictive capability. Designed for scalability, NESTOR provides a computationally efficient and physically rigorous platform for exploring Fermi-surface-driven instabilities in quantum materials.