Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
AI-driven inverse design of materials: Past, present and future
XQ Han, XD Wang, MY Xu, Z Feng, BW Yao… - Chinese Physics …, 2024 - iopscience.iop.org
The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …
development and progress. The structures of materials and their corresponding properties …
Thermal conductivity predictions with foundation atomistic models
Advances in machine learning have led to the development of foundation models for
atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces …
atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces …
AI-driven materials design: a mini-review
Materials design is an important component of modern science and technology, yet
traditional approaches rely heavily on trial-and-error and can be inefficient. Computational …
traditional approaches rely heavily on trial-and-error and can be inefficient. Computational …
Representing Born effective charges with equivariant graph convolutional neural networks
Graph convolutional neural networks have been instrumental in machine learning of
material properties. When representing tensorial properties, weights and descriptors of a …
material properties. When representing tensorial properties, weights and descriptors of a …
Large language model-guided prediction toward quantum materials synthesis
The synthesis of inorganic crystalline materials is essential for modern technology,
especially in quantum materials development. However, designing efficient synthesis …
especially in quantum materials development. However, designing efficient synthesis …
Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
J Peng, J Damewood, J Karaguesian… - arxiv preprint arxiv …, 2024 - arxiv.org
Graph convolutional neural networks (GCNNs) have become a machine learning workhorse
for screening the chemical space of crystalline materials in fields such as catalysis and …
for screening the chemical space of crystalline materials in fields such as catalysis and …
Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
A Kutana, K Yoshimochi, R Asahi - arxiv preprint arxiv:2412.03541, 2024 - arxiv.org
Atomistic simulations of properties of materials at finite temperatures are computationally
demanding and require models that are more efficient than the ab initio approaches …
demanding and require models that are more efficient than the ab initio approaches …
AI-driven inverse design of materials: Past, present and future
H **ao-Qi, X Meng-Yuan, F Zhen, Y Bo-Wen… - Chin. Phys. Lett …, 2025 - cpl.iphy.ac.cn
The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …
development and progress. The structures of materials and their corresponding properties …