Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …

ChemML is an open machine learning (ML) and informatics program suite that is designed
to support and advance the data‐driven research paradigm that is currently emerging in the …

Recent advances in graphics processing unit (GPU) hardware and improved efficiencies of
atomistic simulation programs allow for the screening of a large number of polymers to …

We present a high-throughput computational study to identify novel polyimides (PIs) with
exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our …

The process of developing new compounds and materials is increasingly driven by
computational modeling and simulation, which allow us to characterize candidates before …

In a previous study, we introduced a new computational protocol to accurately predict the
index of refraction (RI) of organic polymers using a combination of first-principles and data …

Machine learning has been emerging as a promising tool in the chemical and materials
domain. In this paper, we introduce a framework to automatically perform rational model …

We present a multitask, physics-infused deep learning model to accurately and efficiently
predict refractive indices (RIs) of organic molecules, and we apply it to a library of 1.5 million …

In the field of materials science and chemistry, machine learning has emerged as a
promising technique in the recent times for the accelerated discovery of novel materials. This …

In this dissertation, we highlight recent developments in the application of machine learning
for molecular modeling and simulation. After giving a brief overview of the foundations …