# All-in-one foundational models learning across quantum chemical levels

arXiv: https://arxiv.org/abs/2409.12015 · HTML: https://mlsanity.com/paper/2409.12015/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)

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

- [Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations](https://mlsanity.com/paper/2505.08195/) (2025)
- [Coupled Cluster con M=oLe: Molecular Orbital Learning for Neural Wavefunctions](https://mlsanity.com/paper/2602.20232/) (2026)

## Abstract (arXiv)

Machine learning (ML) potentials typically target a single quantum chemical (QC) level while the ML models developed for multi-fidelity learning have not been shown to provide scalable solutions for foundational models. Here we introduce the all-in-one (AIO) ANI model architecture based on multimodal learning which can learn an arbitrary number of QC levels. Our all-in-one learning approach offers a more general and easier-to-use alternative to transfer learning. We use it to train the AIO-ANI-UIP foundational model with the generalization capability comparable to semi-empirical GFN2-xTB and DFT with a double-zeta basis set for organic molecules. We show that the AIO-ANI model can learn across different QC levels ranging from semi-empirical to density functional theory to coupled cluster. We also use AIO models to design the foundational model Delta-AIO-ANI based on Delta-learning with increased accuracy and robustness compared to AIO-ANI-UIP. The code and the foundational models are available at https://github.com/dralgroup/aio-ani; they will be integrated into the universal and updatable AI-enhanced QM (UAIQM) library and made available in the MLatom package so that they can be used online at the XACS cloud computing platform (see https://github.com/dralgroup/mlatom for updates).