Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
17883 | 577 | 45.9 | 72% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Cluster id | Level | Cluster label | #P |
---|---|---|---|
2 | 4 | MATERIALS SCIENCE, MULTIDISCIPLINARY//PHYSICS, APPLIED//PHYSICS, CONDENSED MATTER | 2836879 |
656 | 3 | QUASICRYSTALS//APPROXIMANT//DECAGONAL QUASICRYSTAL | 11222 |
3526 | 2 | AFLOW//POWDER INDEXING//COMPUTAT DESIGN DISCOVERY NOVEL MAT | 1395 |
17883 | 1 | AFLOW//COMPUTAT DESIGN DISCOVERY NOVEL MAT//ENSEMBLES OF FEED FORWARD NEURAL NETWORKS | 577 |
Terms with highest relevance score |
rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|---|
1 | AFLOW | authKW | 264599 | 1% | 100% | 5 |
2 | COMPUTAT DESIGN DISCOVERY NOVEL MAT | address | 235728 | 1% | 64% | 7 |
3 | ENSEMBLES OF FEED FORWARD NEURAL NETWORKS | authKW | 158759 | 1% | 100% | 3 |
4 | NSF INT MAT | address | 158759 | 1% | 100% | 3 |
5 | MATERIALS INFORMATICS | authKW | 147798 | 2% | 31% | 9 |
6 | COMPUTAT DESIGN DISCOVERY NOVEL MAT MA | address | 136060 | 2% | 21% | 12 |
7 | MAT GENOM MAT SCI ELECT ENGN PHYS CHEM | address | 119068 | 1% | 75% | 3 |
8 | ARGONNE LEADERSHIP COMP IL | address | 110319 | 2% | 15% | 14 |
9 | ETHANOL ADSORPTION ON AU111 | authKW | 105839 | 0% | 100% | 2 |
10 | MATERIAL DATA ESTIMATION | authKW | 105839 | 0% | 100% | 2 |
Web of Science journal categories |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | Physics, Atomic, Molecular & Chemical | 5050 | 37% | 0% | 214 |
2 | Chemistry, Physical | 2682 | 46% | 0% | 263 |
3 | Materials Science, Multidisciplinary | 540 | 24% | 0% | 137 |
4 | Physics, Condensed Matter | 434 | 16% | 0% | 93 |
5 | Mining & Mineral Processing | 255 | 2% | 0% | 13 |
6 | Mathematics, Interdisciplinary Applications | 154 | 4% | 0% | 24 |
7 | Mineralogy | 122 | 2% | 0% | 13 |
8 | Metallurgy & Metallurgical Engineering | 93 | 5% | 0% | 30 |
9 | Physics, Multidisciplinary | 91 | 7% | 0% | 43 |
10 | Nanoscience & Nanotechnology | 83 | 5% | 0% | 29 |
Address terms |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | COMPUTAT DESIGN DISCOVERY NOVEL MAT | 235728 | 1% | 64% | 7 |
2 | NSF INT MAT | 158759 | 1% | 100% | 3 |
3 | COMPUTAT DESIGN DISCOVERY NOVEL MAT MA | 136060 | 2% | 21% | 12 |
4 | MAT GENOM MAT SCI ELECT ENGN PHYS CHEM | 119068 | 1% | 75% | 3 |
5 | ARGONNE LEADERSHIP COMP IL | 110319 | 2% | 15% | 14 |
6 | MAT INFORMAT INTEGRAT | 84659 | 1% | 20% | 8 |
7 | COMBINATORIAL MAT SCI MAT INFORMAT | 70558 | 0% | 67% | 2 |
8 | COMBINATORIAL DISCOVERY | 58380 | 1% | 14% | 8 |
9 | AALTO ELECT ENGN | 52920 | 0% | 100% | 1 |
10 | CARLOS FS TEOR COMPUTAC 1 | 52920 | 0% | 100% | 1 |
Journals |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | JOURNAL OF CHEMICAL PHYSICS | 7684 | 19% | 0% | 112 |
2 | COMPUTATIONAL MATERIALS SCIENCE | 4627 | 4% | 0% | 25 |
3 | JOM | 4155 | 2% | 1% | 13 |
4 | APL MATERIALS | 3969 | 1% | 1% | 7 |
5 | JOURNAL OF CHEMICAL THEORY AND COMPUTATION | 3710 | 3% | 0% | 18 |
6 | MRS BULLETIN | 2965 | 2% | 1% | 11 |
7 | SCIENTIFIC DATA | 2670 | 1% | 2% | 3 |
8 | INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY | 1691 | 3% | 0% | 19 |
9 | ACS COMBINATORIAL SCIENCE | 1534 | 1% | 1% | 4 |
10 | COMPUTING IN SCIENCE & ENGINEERING | 984 | 1% | 0% | 4 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
---|---|---|---|---|
1 | BEHLER, J , (2015) CONSTRUCTING HIGH-DIMENSIONAL NEURAL NETWORK POTENTIALS: A TUTORIAL REVIEW.INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY. VOL. 115. ISSUE 16. P. 1032 -1050 | 50 | 53% | 23 |
2 | BEHLER, J , (2014) REPRESENTING POTENTIAL ENERGY SURFACES BY HIGH-DIMENSIONAL NEURAL NETWORK POTENTIALS.JOURNAL OF PHYSICS-CONDENSED MATTER. VOL. 26. ISSUE 18. P. - | 67 | 35% | 42 |
3 | BEHLER, J , (2011) ATOM-CENTERED SYMMETRY FUNCTIONS FOR CONSTRUCTING HIGH-DIMENSIONAL NEURAL NETWORK POTENTIALS.JOURNAL OF CHEMICAL PHYSICS. VOL. 134. ISSUE 7. P. - | 34 | 79% | 55 |
4 | BEHLER, J , (2011) NEURAL NETWORK POTENTIAL-ENERGY SURFACES IN CHEMISTRY: A TOOL FOR LARGE-SCALE SIMULATIONS.PHYSICAL CHEMISTRY CHEMICAL PHYSICS. VOL. 13. ISSUE 40. P. 17930 -17955 | 56 | 41% | 97 |
5 | HANDLEY, CM , POPELIER, PLA , (2010) POTENTIAL ENERGY SURFACES FITTED BY ARTIFICIAL NEURAL NETWORKS.JOURNAL OF PHYSICAL CHEMISTRY A. VOL. 114. ISSUE 10. P. 3371-3383 | 33 | 79% | 75 |
6 | GASTEGGER, M , MARQUETAND, P , (2015) HIGH-DIMENSIONAL NEURAL NETWORK POTENTIALS FOR ORGANIC REACTIONS AND AN IMPROVED TRAINING ALGORITHM.JOURNAL OF CHEMICAL THEORY AND COMPUTATION. VOL. 11. ISSUE 5. P. 2187 -2198 | 43 | 52% | 7 |
7 | BEHLER, J , (2016) PERSPECTIVE: MACHINE LEARNING POTENTIALS FOR ATOMISTIC SIMULATIONS.JOURNAL OF CHEMICAL PHYSICS. VOL. 145. ISSUE 17. P. - | 33 | 62% | 2 |
8 | SEKO, A , TAKAHASHI, A , TANAKA, I , (2015) FIRST-PRINCIPLES INTERATOMIC POTENTIALS FOR TEN ELEMENTAL METALS VIA COMPRESSED SENSING.PHYSICAL REVIEW B. VOL. 92. ISSUE 5. P. - | 36 | 64% | 1 |
9 | MALSHE, M , PUKRITTAYAKAMEE, A , RAFF, LM , HAGAN, M , BUKKAPATNAM, S , KOMANDURI, R , (2009) ACCURATE PREDICTION OF HIGHER-LEVEL ELECTRONIC STRUCTURE ENERGIES FOR LARGE DATABASES USING NEURAL NETWORKS, HARTREE-FOCK ENERGIES, AND SMALL SUBSETS OF THE DATABASE.JOURNAL OF CHEMICAL PHYSICS. VOL. 131. ISSUE 12. P. - | 34 | 77% | 5 |
10 | HANDLEY, CM , BEHLER, J , (2014) NEXT GENERATION INTERATOMIC POTENTIALS FOR CONDENSED SYSTEMS.EUROPEAN PHYSICAL JOURNAL B. VOL. 87. ISSUE 7. P. - | 51 | 40% | 6 |
Classes with closest relation at Level 1 |