The convergence of large language models, materials-specific foundation models, and agentic artificial intelligence is reshaping the paradigm of computational materials discovery. While high-throughput computation, automated workflows, and data-driven modeling have greatly expanded the scale of materials exploration, t...
Two-dimensional (2D) semiconductors are highly promising for advanced optoelectronics but are fundamentally limited by strong excitonic binding and unbalanced carrier transport. Here, we design a Mg1In2Se4/Sb van der Waals heterostructure that simultaneously overcomes these bottlenecks through synergistic thermodynam...
Di Zhang, Sheng-Xian Liu, Jian Zhou et al.· Journal of Physical Chemistr...· 0 citations
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, curren...
Shucheng Xing, Jian Zhou, Zhimei Sun· AI Materials· 0 citations
ALKEMIE Agent is introduced, 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 trac...
This study 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 and found that the generative adversarial network achieved the best performance.
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
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