Nongnuch Artrith
Materials Chemistry · 2011–2021
A researcher in the Science corpus: 6 papers; trusts 2 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 61 citations into it; #1,022 of 11,513 by trust here, #3,689 by citations
- Atomic and Molecular Physics, and Optics — 28 citations into it
- Computational Theory and Mathematics — 21 citations into it
- Computational Chemistry & Drug Discovery — 14 citations into it
- Molecular Biology — 13 citations into it
- Physical and Theoretical Chemistry — 13 citations into it; #579 of 671 by trust here, #299 by citations
- AI for Physical Sciences — 5 citations into it
Whom Nongnuch Artrith trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Behler
- C. G. Broyden
- A. P. Bart'ok
- Matthias Rupp
- Sergei Manzhos
- U. Diebold
- Kieron Burke
- K. Momma
- Tobias Morawietz
Who trusts Nongnuch Artrith overall
- Sam Schoenholz
- J. Behler
- Ekin Dogus Cubuk
- A. P. Bart'ok
- Yuri Mishin
- Ivan S. Novikov
- Rohit Batra
- Christian Devereux
- Tobias Morawietz
- Chi Chen
Papers from Nongnuch Artrith's perspective
Ordered by how strongly the researchers Nongnuch Artrith trusts endorse each paper. Same corpus, a different order for every researcher.
- Neural Networks: Tricks of the Trade
- The Elements of Statistical Learning
- Generalized neural-network representation of high-dimensional potential-energy surfaces.
- and s
- I and J
- 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.
- Computational Materials Science X
- The journal of physical chemistry letters
Their papers
- High-dimensional neural network potentials for metal surfaces: A prototype study for copper (2012) — 243 citations
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide (2011) — 295 citations
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 (2016) — 458 citations
- Best practices in machine learning for chemistry (2021) — 384 citations
- Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species (2017) — 235 citations
- Construction of high-dimensional neural network potentials using environment-dependent atom pairs. (2012) — 126 citations