To discover how machine learning (ML) is revolutionizing molecular crystal design and crystallization, a new review in Engineering explores ML’s role in accelerating solvate and co-crystal development, predicting crystal properties, and optimizing crystallization processes. Learn about the latest advancements and future prospects in this field.

The hidden Markov model (HMM), a statistical model widely applied in machine learning, has proven effective in addressing various problems in bioinformatics. Once primarily regarded as a mathematical framework for modeling stochastic processes, HMMs have become indispensable tools for solving a wide range of biological sequence problems, from gene prediction to protein structure analysis.

Specialized AI models can better emulate short-term and long-term dynamics in the Gulf of Mexico while avoiding “hallucinations” — physically impossible scenarios. These models could soon be deployed for applications like managing port operations, directing ships during bad weather, and monitoring extreme events like hurricanes.