Work

Predicting electronic structure properties of transition metal complexes with neural networks

Year: 2017

Type: article

Abstract: , support vector regression and kernel ridge regression), demonstrating particularly improved performance in transferability, as measured by prediction errors on the diverse test set. We establish the value of new uncertainty quantification tools to estimate ANN prediction uncertainty in computational chemistry, and we provide additional heuristics for identification of when a compound of interest is likely to be poorly predicted by the ANN. The ANNs developed in this work provide a strategy for screening transition metal complexes both with direct ANN prediction and with improved structure generation for validation with first principles simulation.

Source: Chemical Science

DOI: 10.1039/c7sc01247k

Authors: Jon Paul Janet, Heather J. Kulik

Institution: Massachusetts Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Burroughs Wellcome Fund, Division of Electrical, Communications and Cyber Systems

Awards: G1624461081, G4542911715, SNM: Knowledge-based Continuous and Scalable Manufacture of Quantum Dots, XSEDE: eXtreme Science and Engineering Discovery Environment