48 citations · 300 across the 20 of their papers we have counts for
10 papers · 1 filter
The Roadmap of Inorganic Computational Materials Databases: Capabilities, Credibility, Coverage, and the Open Frontier
Miao Liu, Jianghao Jin, Tenglong Lu +4
Computational materials databases have become central infrastructure for data-driven discovery of inorganic materials, yet their growth remains strikingly uneven across property fa…
Accelerating dynamic simulations of photoexcited materials and their evolution by electron-informed machine learning
Yunzhe Jia, Fankai Xie, Yunfei Bai +3
Nonadiabatic coupled electron-nuclear dynamics upon electronic excitation underpin the microscopic mechanism and rational modulation of diverse photoinduced functional phenomena in…
Aligning Heterogeneous DFT Datasets: A Graph Neural Network Approach to Cross-Functional Formation Energies
Yidong Huang, Tenglong Lu, Hanwen Kang +3
Heterogeneous density functional theory (DFT) calculations, particularly plane-wave implementations, introduce systematic formation energy errors ranging from tens to hundreds of m…
Are Machine Learning Interatomic Potentials Truly Practical? A Benchmark of 23 Mainstream Models
Hanwen Kang, Tenglong Lu, Sheng Meng +1
Most MLIP benchmarks reward static accuracy while ignoring inference efficiency and hardware scalability -- driving model bloat with unclear real-world value. We benchmark 23 mains…
FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi +3
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy s…
GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials
Fankai Xie, Tenglong Lu, Sheng Meng +1
This study introduces a novel AI force field, namely graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and…
Recent Breakthrough in AI-Driven Materials Science: Tech Giants Introduce Groundbreaking Models
Miao Liu, Sheng Meng
A close look of Google's GNoME inorganic materials dataset [Nature 624, 80 (2023)], and 11 things you would like to know.
MatChat: A Large Language Model and Application Service Platform for Materials Science
Ziyi Chen, Fankai Xie, Meng Wan +5
The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehen…
Screening promising CsV3Sb5-like kagome materials from systematic first-principles evaluation
Yutao Jiang, Ze Yu, Yuxin Wang +4
CsV3Sb5 kagome lattice holds the promise for manifesting electron correlation, topology and superconducting. However, by far only three CsV3Sb5-like kagome materials have been expe…
A universal model for the formation energy prediction of inorganic compounds
Yingzong Liang, Mingwei Chen, Yanan Wang +5
Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of…