6 papers
AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations
Penghui Yang, Zhonghan Zhang, Yue Li +6
Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting a…
Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
Penghui Yang, Chendong Zhao, Bijun Tang +11
Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerat…
Large Language Models for Limited Noisy Data: A Gravitational Wave Identification Study
Yixuan Li, Yuhao Lu, Yang Liu +7
This work investigates whether large language models (LLMs) offer advantages over traditional neural networks for astronomical data processing, in regimes with non-Gaussian, non-st…
287,872 Supermassive Black Holes Masses: Deep Learning Approaching Reverberation Mapping Accuracy
Yuhao Lu, HengJian SiTu, Jie Li +4
We present a population-scale catalogue of 287,872 supermassive black hole masses with high accuracy. Using a deep encoder-decoder network trained on optical spectra with reverbera…
Machine Phenomenology: A Simple Equation Classifying Fast Radio Bursts
Yang Liu, Yuhao Lu, Rahim Moradi +4
This work shows how human physical reasoning can guide machine-driven symbolic regression toward discovering empirical laws from observations. As an example, we derive a simple equ…
MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
Yanchen Deng, Chendong Zhao, Yixuan Li +10
The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce…