Heikki Muhli

most citedMachine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C

_{60}

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cond-mat.mtrl-sci2024★ 1 cited

Atom-wise formulation of the many-body dispersion problem for linear-scaling van der Waals corrections

Heikki Muhli, Tapio Ala-Nissila, Miguel A. Caro

A common approach to modeling dispersion interactions and overcoming the inaccurate description of long-range correlation effects in electronic structure calculations is the use of…

cond-mat.mtrl-sci2021★ 58 cited

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C $_{60}$

Heikki Muhli, Xi Chen, Albert P. Bartók +4

We present a comprehensive methodology to enable addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potenti…

Atom-wise formulation of the many-body dispersion problem for linear-scaling van der Waals corrections

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60​

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C $_{60}$