The Use of AI in Chemical Processes
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- 29 Sep 2022
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Artificial intelligence (AI) has taken on an increasingly central role in chemistry research and industrial development in the last few decades. In particular, analytical chemistry and biochemistry for life sciences have integrated tools such as machine learning algorithms and artificial neural networks (ANNs) to process large datasets and gain new knowledge in the field of chemistry. This article examines the role that AI plays in chemistry today.
The Recent Growth of AI
AI systems make predictions based on data models (i.e., if the data has these features and relationships, it is likely to behave in this way in the future). Generally, AI systems need to be “trained” on datasets with known values, to compare predictions with reality and refine the data models underpinning those predictions.
AI is particularly suited for solving problems that involve a lot of data, or data with complicated input and output relationships. Data like this is difficult or impractical to model with traditional manual procedures, so as AI has developed in the last few years, new modeling applications have risen alongside it.
How Do Chemists Use AI?
There are numerous chemical tasks and processes where data sets often feature complicated input and output relationships. For example, it is possible to predict how soluble a new compound will be with theoretical calculations or calculations with empirical data. AI programs that have learned about structure/solubility relationships from training data with known solubilities can also predict solubility in new compounds.
In general, chemists use AI to help reduce the effort required to design and perform experiments. This is achieved in a number of AI application areas: laboratory automation, predicting new drugs’ bioactivity, optimizing conditions for chemical reactions, and making suggestions for how to synthesize complicated target substances. Read More...
