pycalphad is a Python framework for CALPHAD thermodynamic modelling, phase equilibrium calculations and phase diagrams for materials. The post pycalphad – computational thermodynamics and phase equilibria appeared first on LinuxLinks.

Equilipy performs multicomponent, multiphase equilibrium calculations using CALPHAD thermodynamic models and database sources. The post Equilipy – multicomponent multiphase equilibrium software appeared first on LinuxLinks.

Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods …

arXiv abstract link

arXiv abstract link

arXiv abstract link

arXiv abstract link

arXiv abstract link

arXiv abstract link

arXiv abstract link

arXiv abstract link

Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A;) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagrams). We fine-tuned Mistral, an open-source pre-trained LLM, for two distinct Q&A; formats: multiple-choice and short-answer. Benchmark evaluations demonstrate that fine-tuning substantially enhances performance on multiple-choice phase diagram questions. Moreover, the short-answer model of aLLoyM exhibits the ability to generate novel phase diagrams from its components alone, underscoring its potential to accelerate the discovery of previously unexplored materials systems. To promote further research and adoption, we have publicly released the short-answer fine-tuned version of aLLoyM, along with the complete benchmarking Q&A; dataset, on Hugging Face.

High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase stability play a crucial role, making phase diagram calculations significantly important. However, phase diagram calculations with conventional CALPHAD assessments based on experimental or ab-initio data can be expensive. With the emergence of machine-learning interatomic potentials (MLIPs), we have developed a program named PhaseForge, which integrates MLIPs into the Alloy Theoretic Automated Toolkit (ATAT) framework using our MLIP calculation library, MaterialsFramework, to enable efficient exploration of alloy phase diagrams. Moreover, our workflow can also serve as a benchmarking tool for evaluating the quality of different MLIPs.

Nature Communications, Published online: 14 April 2026; doi:10.1038/s41467-026-71927-6Researchers combined CALPHAD, machine learning, and multi-objective optimisation to design an AM-specific titanium alloy for implant and orthopaedic applications. Laser powder bed fusion produced low-stiffness (~43 GPa), high-ductility (~31%) components, with good cell compatibility.

Nature Communications, Published online: 14 April 2026; doi:10.1038/s41467-026-71927-6Researchers combined CALPHAD, machine learning, and multi-objective optimisation to design an AM-specific titanium alloy for implant and orthopaedic applications. Laser powder bed fusion produced low-stiffness (~43 GPa), high-ductility (~31%) components, with good cell compatibility.