ArticleProceedings of the National Academy of Sciences of the United States of America2024
Inferring phase transitions and critical exponents from limited observations with thermodynamic maps.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Transferable generative models bridge femtosecond to nanosecond time-step molecular dynamics.Science advances · 2026Article
- Detecting Phase Transitions from Data Using Generative Learning.Entropy (Basel, Switzerland) · 2026Article
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Article
- Deep generative modeling of temperature-dependent structural ensembles of proteins.Communications chemistry · 2025Article
- 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
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- Thermodynamic Interpolation: A Generative Approach to Molecular Thermodynamics and Kinetics.Journal of chemical theory and computation · 2025Article
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
- Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.eLife · 2024Article
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4 authors.
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Abstract
Phase transitions are ubiquitous across life, yet hard to quantify and describe accurately. In this work, we develop an approach for characterizing generic attributes of phase transitions from very limited observations made deep within different phases' domains of stability. Our approach is called thermodynamic maps (TM), which combines statistical mechanics and molecular simulations with score-based generative models. TM enable learning the temperature dependence of arbitrary thermodynamic observables across a wide range of temperatures. We show its usefulness by calculating phase transition attributes such as melting temperature, temperature-dependent heat capacities, and critical exponents. For instance, we demonstrate the ability of TM to infer the ferromagnetic phase transition of the Ising model, including temperature-dependent heat capacity and critical exponents, despite never having seen samples from the transition region. In addition, we efficiently characterize the temperature-dependent conformational ensemble and compute melting curves of the two RNA systems: a GCAA tetraloop and the HIV-TAR RNA, which are notoriously hard to sample due to glassy-like energy landscapes.
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