Zentropy theory has emerged as a multiscale thermodynamic framework that bridges quantum mechanics, statistical mechanics, and macroscopic materials behavior by embedding internal degrees of freedom within configurational ensembles. This review summarizes its theoretical foundations, representative applications, current limitations, and future directions. By incorporating intrinsic configurational entropy and free-energy-based statistical weighting, zentropy theory enables improved descriptions of phase stability, thermal expansion, and phase transitions in materials such as ferroelectrics, magnetic systems, high-entropy materials, and superconductors. Recent extensions also connect zentropy with artificial intelligence through data-driven thermodynamic modeling. Despite these advances, several challenges remain, including the ambiguity of configurational coarse-graining, strong cross-degree-of-freedom coupling, propagation of density functional theory errors, and limited applicability to delocalized or non-crystalline states. Future progress will require theoretical advances, including non-ergodic extensions, rigorous mathematical treatment of recursive multiscale entropy, and improved descriptions of low-temperature quantum effects. These efforts should be complemented by standardized software workflows, machine learning integration, and robust uncertainty quantification. Addressing these bottlenecks will help to further develop zentropy theory as a critically assessed framework for multiscale thermodynamic modeling and materials design.
Shucheng Xing, Jian Zhou, Zhimei Sun· AI Materials· 0 citations
Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions. This growing gap between methodological capability and practical execution highlights the need for a new kind of autonomous computational framework, one that can coordinate tools, knowledge, and workflows in a more unified and adaptive way. Here, we introduce ALKEMIE Agent, an agentic platform in which retrieval-augmented generation, a materials-computation knowledge base, registered skills, database-supported provenance, AI-assisted structure modeling, bounded task execution, tool-calling iteration, and error-diagnostic assistance are integrated within a traceable control loop. The capabilities of ALKEMIE Agent are demonstrated through applications including materials recommendation, structure modeling, phonon calculations, machine-learned interatomic potential training, LAMMPS simulations, Ab Initio Monte Carlo (AIMC) sampling, and active-learning-based materials screening. Finally, we outline the future directions and challenges for the development of agentic platforms for computational materials design.
Surface energy is a fundamental physical quantity that governs the stability and properties of van der Waals crystals, yet accurate estimation remains challenging due to the limitations of experimental and first-principles approaches. Herein we developed an efficient framework integrating density functional theory with machine learning methods to predict surface energies in vdW crystals. By combining structural characteristics with elemental properties, we trained several models and found that the generative adversarial network achieved the best performance (R2 = 96.97%, MSE = 1.693). Leveraging this model, we predicted surface energies for ∼800 vdW crystals, ranging from 0.67 to 42.47 meV/Å2. Further feature and bonding analysis revealed surface energy is significantly influenced by interlayer distance, atomic volume, and periodic elemental properties. Our study provides theoretical insights and a cost-effective, high-accuracy pathway for predicting surface energies, facilitating the design of 2D nanosheets and heterostructures.
Shangbin Wu, Naihua Miao, Yu Shu et al.· Journal of Physical Chemistr...· 0 citations
AI2Pot is presented, a scalable and unified MLIP framework that seamlessly integrates model training, evaluation, and large-scale MD simulations with PyTorch-compatible ecosystem, and offers an user-friendly end-to-end framework for the developing, training, and deploying MLIPs for large scale MD.
Hanyu Liu, Linggang Zhu, Xuanguang Zhang et al.· 0 citations