Skip to content

Machine learning

We are dedicated to advancing machine learning (ML) techniques combined with quantum-based methods. Our aim is to bring innovation to these fields by leveraging the power of advanced algorithms and ML methodologies.

Enhancing dynamic system exploration with machine learning

One focus lies in the development of ML approaches for speed up of simulations and tailored for predicting functional properties such as energy, force, and more. We have introduced a novel ML force field based on molecular dynamic trajectories, designed to expedite ab initio molecular dynamics (AIMD) simulations. Notably, our interest extends to feature engineering, where we uncover hidden chemical system information, steering clear of the “black-box” approach.

We also harness ML algorithms to enhance biasing in advanced sampling methods, such as metadynamics, e.g. by formulating collective variables using deep learning techniques for complex chemical systems or developing methods to enhance interpretability of results. To enhance the model’s learning capacity, we concurrently develop novel structural representations adaptable to diverse chemical systems. Moreover, we have developed an in-house software suite, collaboratively used with experimentalists, to address real-world chemistry challenges.

Functional Properties from Machine Learning

Other efforts have focused on advancing wavefunction-based methods beyond DFT using machine learning. For example, we have developed energy density-based descriptors to predict correlation energy at the post-Hartree–Fock level. Another key direction is machine learning-enhanced spectroscopy.

Y. Schubert, S. Luber, N. Marzari, E. Linscott
Predicting electronic screening for fast Koopmans spectral functional calculations
npj Comput. Mater., 2024, 10, 299

R. Ketkaew, F. Creazzo, K. Sivula, S. Luber
A metadynamics study of water oxidation reactions at (001)-WO3/liquid-water interface
Chem. Cat. 2024, 0, 101085

D. Tang, R. Ketkaew, S. Luber
Machine Learning Interatomic Potentials for Heterogeneous Catalysis
Chem. Eur. J., 2024, e202401148

R. Han, J. Mattiat, S. Luber
Automatic purpose-driven basis set truncation for time-dependent Hartree–Fock and density-functional theory
Nat. Commun. 2023, 14, 106

R. Ketkaew, F. Creazzo, S. Luber
Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
J. Phys. Chem. Lett. 2022, 13, 7, 1797–1805

R. Ketkaew, S. Luber
DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space
J. Chem. Inf. Model. 2022, 62, 24, 6352-6364

R. Han, R. Ketkaew, S. Luber
A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra
J. Phys. Chem. A 2022, 126, 6, 801-812

R. Han, S. Luber
Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions
J. Phys. Chem. Lett., 2021, 12, 22, 5324-5331

R. Han, M.Rodríguez-Mayorga, S. Luber
A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds
J. Chem. Theory Comput., 2021, 17, 2, 777–790

R. Han, S. Luber
Trajectory-based machine learning method and its application to molecular dynamics
Molecular Physics, 2020, 118:19-20