Magnetic and thermodynamic properties of face-centered cubic Fe–Ni alloys
Literature Information
M. Yu. Lavrentiev, J. S. Wróbel, D. Nguyen-Manh, S. L. Dudarev
A model lattice ab initio parameterized Heisenberg–Landau magnetic cluster expansion Hamiltonian spanning a broad range of alloy compositions and a large variety of chemical and magnetic configurations has been developed for face-centered cubic Fe–Ni alloys. The thermodynamic and magnetic properties of the alloys are explored using configuration and magnetic Monte Carlo simulations over a temperature range extending well over 1000 K. The predicted face-centered cubic–body-centered cubic coexistence curve, the phase stability of ordered Fe3Ni, FeNi, and FeNi3 intermetallic compounds, and the predicted temperatures of magnetic transitions simulated as functions of alloy composition agree well with experimental observations. Simulations show that magnetic interactions stabilize the face-centered cubic phase of Fe–Ni alloys. Both the model Hamiltonian simulations and ab initio data exhibit a particularly large number of magnetic configurations in a relatively narrow range of alloy compositions corresponding to the occurrence of the Invar effect.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










![3,7-Di(1,1':3',1''-terphenyl-5'-yl)-10,11,12,13-tetrahydrodiindeno[7,1-de:1',7'-fg][1,3,2]dioxaphosphocin-5-ol 5-oxide structure 3,7-Di(1,1':3',1''-terphenyl-5'-yl)-10,11,12,13-tetrahydrodiindeno[7,1-de:1',7'-fg][1,3,2]dioxaphosphocin-5-ol 5-oxide structure](https://static.chemtradehub.com/structs/135/1352810-38-8-3f10.webp)



