Alastair J. A. Price

University of Toronto — Canada

Physics and Astronomy · Physical Sciences

12h-index495citations25works18.52yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Advanced Chemical Physics Studies(11), Machine Learning in Materials Science(11), Crystallography and molecular interactions(8), X-ray Diffraction in Crystallography(4), Spectroscopy and Quantum Chemical Studies(3)

Publications24 total

AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
arXiv (Cornell University)·2025Open Access
Adapting hybrid density functionals with machine learning
Science Advances·2025· 19 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
The seventh blind test of crystal structure prediction: structure ranking methods
Acta Crystallographica Section B Structural Science Crystal Engineering and Materials·2024· 59 citedOpen Access
Assessment of the exchange-hole dipole moment dispersion correction for the energy ranking stage of the seventh crystal structure prediction blind test
Acta Crystallographica Section B Structural Science Crystal Engineering and Materials·2024· 6 citedOpen Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
Reducing training data needs with minimal multilevel machine learning (M3L)
Machine Learning Science and Technology·2024· 18 citedOpen Access
Adaptive hybrid density functionals
arXiv (Cornell University)·2024· 2 citedOpen Access
Comparison of Density-Functional Theory Dispersion Corrections for the DES15K Database
The Journal of Physical Chemistry A·2023· 28 cited
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
arXiv (Cornell University)·2023· 1 citedOpen Access
Supplementary Information for: Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Zenodo (CERN European Organization for Nuclear Research)·2023· 1 citedOpen Access
Accurate and efficient polymorph energy ranking with XDM-corrected hybrid DFT
CrystEngComm·2023· 23 cited
XDM-corrected hybrid DFT with numerical atomic orbitals predicts molecular crystal lattice energies with unprecedented accuracy
Chemical Science·2022· 50 citedOpen Access
Small molecule binding to surface-supported single-site transition-metal reaction centres
Nature Communications·2022· 12 citedOpen Access
XDM-Corrected Hybrid DFT with Numerical Atomic Orbitals Predicts Molecular Crystal Lattice Energies with Unprecedented Accuracy
2022· 4 citedOpen Access
Accurate and Efficient Polymorph Energy Ranking with XDM-corrected hybrid DFT
2022Open Access
XDM-Corrected Hybrid DFT with Numerical Atomic Orbitals Predicts Molecular Crystal Lattice Energies with Unprecedented Accuracy
2022· 1 citedOpen Access
A density-functional benchmark of vibrational free-energy corrections for molecular crystal polymorphism
The Journal of Chemical Physics·2022· 12 cited
page 1 of 2Next →

Frequent Co-authors

Erin R. Johnson(12), O. Anatole von Lilienfeld(9), Alberto Otero‐de‐la‐Roza(8), Danish Khan(8), Stefan Heinen(7), Bing Huang(5), Igor Poltavsky(5), Adil Kabylda(5), Alexandre Tkatchenko(5), R. Alex Mayo(4), Mirela Puleva(4), Anton Charkin-Gorbulin(4), Grégory Fonseca(4), Ilyes Batatia(4), Nicholas J. Browning(4), Stefan Chmiela(4), Mengnan Cui(4), J. Thorben Frank(4), Silvan Käser(4), Carolin Müller(4)