820 citations · 12k across the 115 of their papers we have counts for
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Thermodynamics and dielectric response of by data-driven modeling
Lorenzo Gigli, Max Veit, Michele Kotiuga +3
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate descri…
Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat
Benjamin A. Helfrecht, Giovanni Pireddu, Rocio Semino +2
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of distinct siliceous networks can be generated by computer-aided…
Local invertibility and sensitivity of atomic structure-feature mappings
Sergey N. Pozdnyakov, Liwei Zhang, Christoph Ortner +2
The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping…
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Jigyasa Nigam, Michael Willatt, Michele Ceriotti
Symmetry considerations are at the core of the major frameworks used to provide an effective mathematical representation of atomic configurations that is then used in machine-learn…
The importance of nuclear quantum effects for NMR crystallography
Edgar A. Engel, Venkat Kapil, Michele Ceriotti
The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candi…
Learning electron densities in the condensed phase
Alan M. Lewis, Andrea Grisafi, Michele Ceriotti +1
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which e…