Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …

Models can codify our understanding of chemical reactivity and serve a useful purpose in
the development of new synthetic processes via, for example, evaluating hypothetical …

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in
terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts …

Metal cation‐doped lead halide perovskite (LHP) quantum dots (QDs) with
photoluminescence quantum yields (PLQYs) higher than unity, due to quantum cutting …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Machine Learning (ML) techniques face significant challenges when predicting advanced
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …

As physical chemistry transitioned to computational chemistry, a new growth occurred in the
field with the advent of predictive catalysis, making it a key player in the optimization and …

High-throughput experimentation is a common practice in the optimization of chemical
synthesis. Chemists design reaction arrays to optimize the yield of couplings between …

We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and
functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can …

Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …