| Full text | |
| Author(s): Show less - |
Pinheiro, Gabriel A.
;
Da Silva, Juarez L. F.
;
Soares, Marinalva D.
;
Quiles, Marcos G.
;
Gervasi, O
;
Murgante, B
;
Misra, S
;
Garau, C
;
Blecic, I
;
Taniar, D
;
Apduhan, BO
;
Rocha, AMAC
;
Tarantino, E
;
Torre, CM
;
Karaca, Y
Total Authors: 15
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I; v. 12249, p. 13-pg., 2020-01-01. |
| Abstract | |
Machine learning has become a new hot-topic in Materials Sciences. For instance, several approaches from unsupervised and supervised learning have been applied as surrogate models to study the properties of several classes of materials. Here, we investigate, from a graph-based clustering perspective, the Quantum QM9 dataset. This dataset is one of the most used datasets in this scenario. Our investigation is two-fold: 1) understand whether the QM9 samples are organized in clusters, and 2) if the clustering structure might provide us with some insights regarding anomalous molecules, or molecules that jeopardize the accuracy of supervised property prediction methods. Our results show that the QM9 is indeed structured into clusters. These clusters, for instance, might suggest better approaches for splitting the dataset when using cross-correlation approaches in supervised learning. However, regarding our second question, our finds indicate that the clustering structure, obtained via Simplified Molecular Input Line Entry System (SMILES) representation, cannot be used to filter anomalous samples in property prediction. Thus, further investigation regarding this limitation should be conducted in future research. (AU) | |
| FAPESP's process: | 17/11631-2 - CINE: computational materials design based on atomistic simulations, meso-scale, multi-physics, and artificial intelligence for energy applications |
| Grantee: | Juarez Lopes Ferreira da Silva |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |
| FAPESP's process: | 16/23642-6 - Characterization of Time-Varying Complex Networks |
| Grantee: | Alessandra Marli Maria Morais Gouvêa |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 18/21401-7 - Multi-User Equipment approved in grant 2017/11631-2: cluster computational de alto desempenho - ENIAC |
| Grantee: | Juarez Lopes Ferreira da Silva |
| Support Opportunities: | Multi-user Equipment Program |