ArticleNature communications2022
Path sampling of recurrent neural networks by incorporating known physics.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by Nano-GPT.Journal of chemical theory and computation · 2025Article
- Adversarial training for dynamics matching in coarse-grained models.The Journal of chemical physics · 2025Article
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Precision to plate: AI-driven innovations in fermentation and hyper-personalized diets.Frontiers in nutrition · 2025Review
- One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.The journal of physical chemistry. B · 2024Article
- Simulating the whole brain as an alternative way to achieve AGI.Quantitative biology (Beijing, China) · 2023Article
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2023Review
- Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.Journal of chemical information and modeling · 2023Review
- State-of-the-art and novel approaches to mild solubilization of inclusion bodies.Frontiers in bioengineering and biotechnology · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.