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Phase transformations in multi-component alloys underpin microstructural evolution and performance in advanced engineering systems. Yet, their prediction remains a persistent challenge due to the interplay of thermodynamic complexity, kinetic constraints, and sparse data regimes. While machine-learning approaches have shown promise in accelerating materials discovery, purely data-driven models often lack physical fidelity, interpretability, and robustness when extrapolated across high-dimensional compositional spaces. This paper introduces a conceptual framework for physics-constrained neural networks (PCNNs) to predict phase transformation pathways rather than static equilibrium states in multi-component alloy systems. The framework embeds thermodynamic and kinetic principles directly into the learning objective, reframing physical laws as epistemic constraints that govern admissible predictions. Unlike conventional physics-informed neural networks that solve predefined equations, the proposed approach integrates higher-level physical criteria—such as Gibbs free-energy minimization, phase coexistence rules, and diffusion-based kinetics—into a unified optimization logic. The contribution of this work is theoretical rather than empirical. By articulating how physical constraints regularize learning, enhance interpretability, and support generalization under data scarcity, the framework advances applied artificial intelligence as a decision-relevant modeling paradigm for materials science. Implications for alloy design, model trustworthiness, and AI-assisted exploration of complex phase spaces are discussed.

Crystal structure prediction remains a fundamental challenge in materials science, particularly in crystallography and solid-state physics, where identifying stable configurations under varying thermodynamic conditions is essential for the design of functional materials. Traditional methods that rely on ab initio calculations or evolutionary algorithms often struggle with the vast configurational space and complex constraints such as temperature, pressure, and phase equilibria. This paper proposes a new conceptual framework that combines reinforcement learning (RL) with thermodynamic principles to enhance the efficiency and accuracy of the crystal structure search. In conceptualizing the search process as a Markov decision process, the framework uses an RL agent to navigate structural modifications, guided by rewards derived from thermodynamic stability metrics such as Gibbs free energy and entropy contributions. The synthesis of literature shows that while machine learning has accelerated predictions, RL’s adaptive learning provides untapped potential for handling multifaceted constraints. The proposed model includes multi-objective optimization to balance stability and formation feasibility, avoiding reliance on empirical data. This purely theoretical approach fosters originality by redefining state-action spaces to embed symmetry and lattice constraints inherently. Implications extend to high-entropy alloys and polymorphic materials, potentially revolutionizing computational materials discovery. Through textual depiction of a conceptual diagram, the framework’s modularity is highlighted, enabling future extensions to quantum-informed rewards. Overall, this work bridges AI and thermodynamics, paving the way for conceptually robust, constraint-aware structure searches in applied artificial intelligence for materials science.