Portrait de Khang Ngo

Khang Ngo

khang.ngo@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Siamak Ravanbakhsh
Sujets de recherche
Apprentissage automatique pour les sciences physiques
IA pour la science
Modélisation moléculaire
Google Scholar
GitHub

Publications

Molecule property prediction with molecular orbitals
Yan Zhang
Khang Ngo
Sékou-Oumar Kaba
Daniel T. Levy
Siamak Ravanbakhsh
Aristide Baratin
Kisoo Kwon
MiYoung Jang
Eun Hyun Cho
Sangha Park
Sanghyun Yoo
Young-Seok Kim
Hasup Lee
Molecular orbitals describe the distribution of electrons in a molecule and are frequently used by chemists to understand properties of mole… (voir plus)cules, yet machine learning has neglected them so far. If atom coordinates are obtained through DFT anyway, they can be obtained for free at the same time and are thus a useful source of additional data, particularly when data is scarce We give an introduction to molecular orbitals for a machine learning audience and propose models to process three different representations of them. Experiments on a dataset with experimental properties show that including MOs significantly improves performance and sample efficiency over a pretrained molecular foundation model on this real-world task.
2026-03-01
AI4Mat @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Scaling Laws and Symmetry, Evidence from Neural Force Fields
Khang Ngo
Siamak Ravanbakhsh
We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even more at larger… (voir plus) scales; we show a clear power-law scaling behaviour with respect to data, parameters and compute with ``architecture-dependent exponents''. In particular, we observe that equivariant architectures, which leverage task symmetry, scale better than non-equivariant models. Moreover, among equivariant architectures, higher-order representations translate to better scaling exponents. Our analysis also suggests that for compute-optimal training, the data and model sizes should scale in tandem regardless of the architecture. At a high level, these results suggest that, contrary to common belief, we should not leave it to the model to discover fundamental inductive biases such as symmetry, especially as we scale, because they change the inherent difficulty of the task and its scaling laws.
2025-10-09
ArXiv (prépublication)
doi.org
arxiv.org