Recently, some researchers have tried to use machine-learning models to discover transition state structures. However, models developed so far require considering two reactants as a single entity in which the reactants maintain the same orientation with respect to each other. Any othe...
language modelsmachine learningregressionMolecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical ...
One of the fascinating advances in machine learning has been the development of large language models (LLMs), so-called foundation models1,2,3,4,5,6. These models are appealing because of their simplicity; given a phrase, they return text that completes phrases in natural language such that,...
Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models are making correct predictions because they inferred the salient chemistry, nor is it clear which training data they are ...
这个问题其实本来没有回答的必要，在google上面用“machine learning”和“computational chemistry”做关键词...
We also evaluate several RNN models that implement a gating mechanism, including the long short-term memory (LSTM) unit and the gated recurrent unit (GRU) as the microstructure-learning engine. We analyze the different combinations of these methods on the spinodal decomposition of a two-phase ...
To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H2, O2,...
Thus, a general practice for the future work is to simplify state-of-the-art models and evaluate model shrinkage as part of a cost-benefit analysis. For instance, a learning curve based on the number of trainable parameters (or number and type of layers) should become a trend in ...
machine learning tasks14,15, training general and robust models that perform well on a wide variety of downstream tasks remains a pressing challenge8,16,17. The enormity of chemical space and heterogeneity of these tasks motivates investigations of large-scale models in chemistry, because such ...
In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods and progress towards machine learning approaches.