CALPHAD 89 (2025) 102825 · Jindal & Lele

Research Highlight

Contour plots of configurational entropy, enthalpy, and Gibbs energy of mixing for the Nb-Ti-V-Zr high entropy alloy system

Fig. 20. Configurational entropy, enthalpy, and Gibbs energy of mixing at 1273K for the Nb-Ti-V-Zr high entropy alloy system.

Multicomponent Cluster Variation Method: Application to High Entropy Alloys

V. Jindal & S. Lele · Calphad 89 (2025) 102825

A new framework lets cluster expansion coefficients of binary and ternary subsystems be directly inherited into higher-order systems, enabling self-consistent databases analogous to CALPHAD. Applied to the Nb-Ti-V-Zr refractory HEA system, the model shows entropy and enthalpy both stabilise the single-phase bcc region, with Ti addition suppressing phase separation.

  • Binary/ternary parameters inherit directly into multicomponent databases, with no exponential growth in CFs/CECs
  • Entropy and enthalpy both drive bcc stabilisation in Nb-Ti-V-Zr HEAs
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Materials Modeling Lab (MML)

The Materials Modeling Lab is part of the Department of Metallurgical Engineering at IIT (BHU), Varanasi. We develop next-generation computational and experimental tools for designing advanced materials, from high-entropy alloys and Ti-TiB composites to beta-titanium biomedical implants and functionally graded armour materials.

Our work bridges classical thermodynamic modelling (CALPHAD, CVM, CE) with modern machine learning and atomistic simulations, placing us at the intersection of traditional materials science and data-driven discovery.

Research Focus

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Computational Thermodynamics

CALPHAD, Cluster Variation Method (CVM), and Cluster Expansion (CE) for phase diagram assessment and Gibbs energy modelling.

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High Entropy Alloys

Thermodynamic modelling of short-range ordering, neural-network-driven enthalpy prediction, and experimental validation of HEAs.

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Machine Learning in Materials

ML + DFT for hardness prediction in refractory HEAs, neural network models for SRO, and data-driven alloy design pipelines.

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Ti-TiB Composites & FGMs

CALPHAD-guided processing of Ti-TiB composites and Functionally Graded Armour Materials for defence and biomedical applications.

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Beta-Ti Biomedical Alloys

Low-modulus beta-titanium alloys for dental and orthopaedic implants, using thermodynamic design and experimental characterisation.

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Atomistic Simulations

MD and MC simulations for phase transitions, dislocation evolution, and alloy thermodynamics at the atomic scale.

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Latest News

Jun 2026
Paper Comparative study of microstructure and hardness in Ti-TiB composites and FGMs, Trans. Indian Inst. Met. 79 (2026)
May 2026
Paper Hybrid CE-ML framework for predicting enthalpy of mixing in BCC refractory binary alloys, Model. Simul. Mater. Sci. Eng. (2026)
May 2026
Paper Mechanistic & data-driven hardness prediction in refractory HEAs, Intermetallics 194 (2026)
Apr 2026
Paper Atomistic simulation of Cu phase transition, J. Mater. Sci. (2026)
Mar 2026
Paper Ti-TiB Composites and FGMs: Mechanical Properties & Fracture Behavior, J. Mater. Eng. Perform. (2026)
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Current PhD Students

Murali Padiri
Murali Padiri
PhD Student

Ti-TiB Composites & FGMs

Ishu Yadav
Ishu Yadav
PhD Student

Beta-Ti Biomedical Alloys

HK
Harish Kumar
PhD Student

Computational Thermodynamics

RK
Roshan Kumar
PhD Student

Atomistic Simulations

Meet the Full Team →

Join the MML Group

We are seeking motivated PhD students, M.Tech project students, and post-doctoral researchers with a passion for computational materials science and data-driven design.

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Funded by IIT(BHU), SERB (CRG), DRDO–ARMREB, SRTMI–SAIL, and IIT(BHU) Challenge Grant