| Full text | |
| Author(s): |
de Azevedo, Luis Cesar
[1]
;
Pinheiro, Gabriel A.
[2]
;
Quiles, Marcos G.
[2]
;
Da Silva, Juarez L. F.
[3]
;
Prati, Ronaldo C.
[1]
Total Authors: 5
|
| Affiliation: | [1] Fed Univ ABC, Ctr Math Computat & Cognit, BR-5001 Santo Andre, SP - Brazil
[2] Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, BR-12247014 Sao Jose Dos Campos, SP - Brazil
[3] Univ Sao Paulo, Sao Carlos Inst Chem, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 61, n. 9, p. 4210-4223, SEP 27 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Most machine learning applications in quantum-chemistry (QC) data sets rely on a single statistical error parameter such as the mean square error (MSE) to evaluate their performance. However, this approach has limitations or can even yield incorrect interpretations. Here, we report a systematic investigation of the two components of the MSE, i.e., the bias and variance, using the QM9 data set. To this end, we experiment with three descriptors, namely (i) symmetry functions (SF, with two-body and three-body functions), (ii) many-body tensor representation (MBTR, with two- and three-body terms), and (iii) smooth overlap of atomic positions (SOAP), to evaluate the prediction process's performance using different numbers of molecules in training samples and the effect of bias and variance on the final MSE. Overall, low sample sizes are related to higher MSE. Moreover, the bias component strongly influences the larger MSEs. Furthermore, there is little agreement among molecules with higher errors (outliers) across different descriptors. However, there is a high prevalence among the outliers intersection set and the convex hull volume of geometric coordinates (VCH). According to the obtained results with the distribution of MSE (and its components bias and variance) and the appearance of outliers, it is suggested to use ensembles of models with a low bias to minimize the MSE, more specifically when using a small number of molecules in the training set. (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 |