45 citations · 127 across the 13 of their papers we have counts for
14 papers
Harnessing agent memory to build lifelong AI partners for materials scientists
Siyu Liu, Bo Hu, Beilin Ye +3
Materials research advances through accumulated experience - scripts that work, protocols that are trusted, warnings attached to failed calculations or experiments, and judgement t…
Atomistic Structure of Transient Switching States in Ferroelectric AlScN
Jiawei Huang, Jinyang Li, Xinyue Guo +5
We resolve the microscopic mechanism of polarization switching in wurtzite ferroelectric AlScN by integrating advanced thin-film fabrication, ferroelectric switching dynamics chara…
MatTools: Benchmarking Large Language Models for Materials Science Tools
Siyu Liu, Bo Hu, Beilin Ye +3
Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design.…
Inverse Materials Design by Large Language Model-Assisted Generative Framework
Yun Hao, Che Fan, Beilin Ye +7
Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we intro…
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
Zhuoyuan Li, Siyu Liu, Beilin Ye +2
Artificial intelligence (AI) is transforming materials science, enabling both theoretical advancements and accelerated materials discovery. Recent progress in crystal generation mo…
ABACUS: An Electronic Structure Analysis Package for the AI Era
Weiqing Zhou, Daye Zheng, Qianrui Liu +55
ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It…
Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design
Siyu Liu, Tongqi Wen, Beilin Ye +2
Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a…
A Multi-agent Framework for Physical Laws Discovery
Bo Hu, Siyu Liu, Beilin Ye +6
Discovering explicit physical laws has traditionally depended on human intuition and domain expertise. Recent advances in artificial intelligence, particularly large language model…
Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Xinzijian Liu, Yanbo Han, Zhuoyuan Li +15
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable…
An Extendable Cloud-Native Alloy Property Explorer
Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang +8
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials ide…
A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
Siyu Liu, Tongqi Wen, A. S. L. Subrahmanyam Pattamatta +1
Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models i…
Coherent and semicoherent / interfaces in titanium: structure, thermodynamics, migration
Siqi Wang, Tongqi Wen, Jian Han +1
The interface is central to the microstructure and mechanical properties of titanium alloys. We investigate the structure, thermodynamics and migration of the coherent and se…
A "Magnetic" Machine Learning Interatomic Potential for Nickel
Xiaoguo Gong, Zhuoyuan Li, A. S. L. Subrahmanyam Pattamatta +2
Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural appli…
Finite-temperature screw dislocation core structures and dynamics in -titanium
Anwen Liu, Tongqi Wen, Jian Han +1
A multiscale approach based on molecular dynamics (MD) and kinetic Monte Carlo (kMC) methods is developed to simulate the dynamics of an screw dislocat…