# Accurate melting point prediction through autonomous physics-informed learning

arXiv: https://arxiv.org/abs/2306.13345 · HTML: https://mlsanity.com/paper/2306.13345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 45 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics](https://mlsanity.com/paper/1712.03641/) (2017)
- [Automated discovery of a robust interatomic potential for aluminum](https://mlsanity.com/paper/2003.04934/) (2020)

## Built on by (most cited first, 2 of 2)

- [Bayesian inference of composition-dependent phase diagrams](https://mlsanity.com/paper/2309.01271/) (2023)
- [Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning](https://mlsanity.com/paper/2506.17719/) (2025)

## Abstract (arXiv)

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.