ArticleFaraday discussions2025
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.
Article in Faraday discussions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.Journal of chemical theory and computation · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.Journal of chemical theory and computation · 2026Article
- Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures atThe journal of physical chemistry letters · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium.npj computational materials · 2026Article
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 2025Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- An automated framework for exploring and learning potential-energy surfaces.Nature communications · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry.Chemical science · 2025Review
- Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.npj computational materials · 2025Article
- Fast and Fourier features for transfer learning of interatomic potentials.npj computational materials · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy - even with the aid of machine learning potentials - is a challenge that requires sub-kJ mol
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Registered trials
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.