Xiang-Guo Li
Materials Chemistry · 2019–2020
A researcher in the Science corpus: 4 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 200 citations into it
- Atomic and Molecular Physics, and Optics — 57 citations into it
- Mechanical Engineering — 32 citations into it
- AI for Physical Sciences — 17 citations into it
- Mechanics of Materials — 9 citations into it
Whom Xiang-Guo Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- G. Rohrer
- B. Gludovatz
- A. P. Bart'ok
- Tadao Watanabe
- Gábor Csányi
- Shyue Ping Ong
- S. Plimpton
- A. Hohenwarter
Who trusts Xiang-Guo Li overall
Papers from Xiang-Guo Li's perspective
Ordered by how strongly the researchers Xiang-Guo Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
- Machine learning based interatomic potential for amorphous carbon
- Comparing molecules and solids across structural and alchemical space.
- The journal of physical chemistry letters
- Computational Materials Science X
- Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
- A fracture-resistant high-entropy alloy for cryogenic applications (Science)
- Self-Consistent Equations Including Exchange and Correlation Effects
- Generalized Gradient Approximation Made Simple.
- Grain boundary energies in body-centered cubic metals
Their papers
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials. (2019) — 807 citations
- A Critical Review of Machine Learning of Energy Materials (2020) — 478 citations
- Grain boundary properties of elemental metals (2019) — 238 citations
- Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy (2019) — 239 citations