Professor
Publications
Roadmap on multiscale materials modeling
Modelling and Simulation in Materials Science and Engineering
(2020)
28
043001
(doi: 10.1088/1361-651x/ab7150)
Hierarchical machine learning of potential energy surfaces
J Chem Phys
(2020)
152
204110
(doi: 10.1063/5.0006498)
Combining phonon accuracy with high transferability in Gaussian approximation potential models
(2020)
(doi: 10.48550/arxiv.2005.07046)
Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials
Geochimica Et Cosmochimica Acta
(2020)
291
5
(doi: 10.1016/j.gca.2020.03.028)
Comparing molecules and solids across structural and alchemical space
(2020)
(doi: 10.48550/arxiv.1601.04077)
Gaussian Approximation Potentials: a brief tutorial introduction
(2020)
(doi: 10.48550/arxiv.1502.01366)
Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials
Machine Learning: Science and Technology
(2020)
1
015004
(doi: 10.1088/2632-2153/ab527c)
Performance and Cost Assessment of Machine Learning Interatomic Potentials.
J Phys Chem A
(2020)
124
731
(doi: 10.1021/acs.jpca.9b08723)
Machine-Learning of Atomic-Scale Properties Based on Physical Principles
(2020)
968
99
(doi: 10.1007/978-3-030-40245-7_6)
Machine Learning of Atomic-Scale Properties Based on Physical Principles
(2020)
1911
(doi: 10.1007/978-3-319-44677-6_68)
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