Spin Dynamics from Atomistic Quantum Simulations

TL;DR

This paper develops a theoretical framework for predicting spin dynamics in solid-state defects at high temperatures using quantum simulations and machine learning, validated by experiments on NV centers.

Contribution

It introduces a unified approach combining Kubo linear-response theory with machine learning-based molecular dynamics to compute spin relaxation times.

Findings

Derived expressions for T1 and T2 in terms of correlation functions.

Predicted T1 and T2 times using machine learning models trained on ab initio data.

Experimental T1 times for NV centers match theoretical predictions.

Abstract

Optically active solid-state spin defects are promising candidates for quantum applications, however a unified theoretical framework to predict their spin dynamics at high temperatures is not yet available. Here, using Kubo linear--response theory, we derive expressions of spin-lattice and decoherence times \(T_1\) and \(T_2\) in terms of correlation functions of spin--lattice couplings. We then evaluate \(T_1\) and \(T_2\) from molecular dynamics and spin--lattice interaction time--series generated by state--of--the--art machine learning models trained on {\it ab--initio} data. Finally we measure \(T_1\) times for the NV center in diamond and compare experimental and theoretical results, showing excellent agreement.