# Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis?

arXiv: https://arxiv.org/abs/2201.03822 · HTML: https://mlsanity.com/paper/2201.03822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 162 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics](https://mlsanity.com/paper/1707.09571/) (2017)
- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System](https://mlsanity.com/paper/2111.03563/) (2021)

## Abstract (arXiv)

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulations closer to reality. This perspective delineates the present status of the field from efforts of others in the field and some of your own work and discusses open questions and future prospects.