Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions for decision-making. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources.
To apply machine learning and deep learning techniques to predict material properties.
Molecular modeling can be combined with quantitative structure–property relationship (QSPR) models to predict flammability of hydrocarbons. Minimum ignition energy (MIE) is one of the most important parameters when characterizing probability of ignition. For the first time, my research group has proposed two QSPR models based on limited existing experimental data. Both models are validated to have excellent performances and hence are qualified to predict MIE values for chemicals with no experimental data available. These two validated models can also help gain a better understanding of effects of molecular structures on ignition properties of hydrocarbon fuels. This research provides general guideline and methodology of establishing QSPR models to predict hazardous properties of chemicals. The result was published in Industrial & Engineering Chemistry Research and was featured by the magazine Advances in Engineering. We have published a few invited review papers in this field.
(1) Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications, ACS Chemical Health & Safety, 2020, in press. 10.1021/acs.chas.0c00075
(2) Review of Recent Developments of Quantitative Structure-Property Relationship Models on Fire and Explosion Related Properties, Process Safety and Environmental Protection, 2019, 129, 280-290.
(3) Prediction of Minimum Ignition Energy from Molecular Structure Using Quantitative Structure-Property Relationship (QSPR) Models, Industrial & Engineering Chemistry Research 2017, 56 (1), 47–51.
(4) Thermal decomposition pathways of hydroxylamine: theoretical investigation on the initial steps, Journal of Physical Chemistry A 2010, 114 (34), 9262-9269.