# Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics

arXiv: https://arxiv.org/abs/2306.02015 · HTML: https://mlsanity.com/paper/2306.02015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation](https://mlsanity.com/paper/2003.02183/) (2020)
- [Capturing dynamical correlations using implicit neural representations](https://mlsanity.com/paper/2304.03949/) (2023)
- [Deep reinforcement learning for quantum multiparameter estimation](https://mlsanity.com/paper/2209.00671/) (2022)
- [Simplified algorithms for adaptive experiment design in parameter estimation](https://mlsanity.com/paper/2202.08344/) (2022)

## Abstract (arXiv)

Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries.