ArticleNature communications2025
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials.
Article in Nature communications, 2025. 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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Who cites it
10 citing papers in PubMed.
- Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.Journal of chemical theory and computation · 2026Article
- Construction of Arbitrary-Order Internal Coordinate Transformations to Improve Studies of Large-Amplitude Motions.The journal of physical chemistry. A · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.Journal of the American Chemical Society · 2026Article
- Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.Journal of chemical theory and computation · 2026Article
- Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.The journal of physical chemistry letters · 2025Article
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent discrete elements as real-valued tensors. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs, and allows smooth interpolation between the compositional states of materials. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights. With this modification, we propose methodologies for optimizing the composition of solid solutions towards target macroscopic properties, characterizing order and disorder in multicomponent oxides, and conducting alchemical free energy simulations to quantify the free energy of vacancy formation and composition changes.
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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 Socratic graph.