6 papers
Composable Crystals: Controllable Materials Discovery via Concept Learning
Nian Liu, Yuwei Zeng, Ryoji Kubo +7
De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely…
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8
De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize th…
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
Jian Cao, Nian Liu, Ying Lin
We introduce a novel framework for constructing scalable and flexible covariance kernels for Gaussian processes (GPs) by directly learning the covariance structure under a regressi…
SVD-Preconditioned Gradient Descent Method for Solving Nonlinear Least Squares Problems
Zhipeng Chang, Wenrui Hao, Nian Liu
This paper introduces a novel optimization algorithm designed for nonlinear least-squares problems. The method is derived by preconditioning the gradient descent direction using th…
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
Yuan Sui, Yufei He, Nian Liu +3
Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as ext…
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent +3
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing…