Professor
Publications
First-principles study of alkali-metal intercalation in disordered carbon anode materials
Journal of Materials Chemistry A
(2019)
7
19070
(doi: 10.1039/C9TA05453G)
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
(2019)
(doi: 10.48550/arxiv.1906.08888)
Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
(2019)
(doi: 10.48550/arxiv.1906.07816)
De novo exploration and self-guided learning of potential-energy surfaces
(2019)
(doi: 10.48550/arxiv.1905.10407)
Machine-learned multi-system surrogate models for materials prediction
(2019)
(doi: 10.48550/arxiv.1809.09203)
Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon
Angewandte Chemie - International Edition
(2019)
58
7057
(doi: 10.1002/anie.201902625)
Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon
Angewandte Chemie
(2019)
131
7131
(doi: 10.1002/ange.201902625)
Equation of State of Fluid Methane from First Principles with Machine Learning Potentials.
Journal of Chemical Theory and Computation
(2019)
15
2574
(doi: 10.1021/acs.jctc.8b01242)
A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule
(2019)
(doi: 10.26434/chemrxiv.7679798.v1)
Machine Learning of Atomic-Scale Properties Based on Physical Principles
In: Andreoni W., Yip S. (eds) Handbook of Materials Modeling. Springer, Cham
(2019)
1
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