arXiv AI

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening

arXiv:2606. 19133v1 Announce Type: cross Abstract: Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells.

arXiv AI

Jul 29

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.

By Sanjay Chakraborty

benchmarks

arXiv Machine Learning

1d ago

EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

arXiv:2302. 12177v4 Announce Type: replace-cross Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery.

By Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang

benchmarks

arXiv AI

Jul 13

Model Agnostic Graph Prompt Learning for Crystal Property Prediction

arXiv:2607. 08996v1 Announce Type: cross Abstract: Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

benchmarks

arXiv Machine Learning

Jun 2

Latent Diffusion Pretraining for Crystal Property Prediction

arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

llms diffusion fine-tuning

arXiv AI

Aug 10

Optimizing Spectral Prediction in MXene-Based Metasurfaces Through Multi-Channel Spectral Refinement and Savitzky-Golay Smoothing

arXiv:2602. 08406v2 Announce Type: replace-cross Abstract: The prediction of electromagnetic spectra for MXene-based solar absorbers, where MXenes are a family of two-dimensional transition metal carbides and nitrides, is a computationally intensive task traditionally addressed using full-wave solvers.

By Shujaat Khan, Waleed Iqbal Waseer, Muhammad Shahid Jabbar

fine-tuning

arXiv AI

Jul 29

DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction

arXiv:2607. 24785v1 Announce Type: cross Abstract: Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers.

By Cen Chen, Haitao Huang, Jiazhi Mao, Feifan Xu, Zhe Zhuang, Yuxiang Ren

efficiency safety

arXiv AI

Jun 30

Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop

arXiv:2606. 29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science.

By Chenmu Zhang, Boris I. Yakobson

llms rag agents benchmarks

arXiv AI

Jul 22

Spectral Higher-Order Neural Networks Have Sharp Expressivity Bounds

arXiv:2607. 19042v1 Announce Type: cross Abstract: Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning.

By Gianluca Peri, Diego Febbe, Duccio Fanelli

benchmarks safety

arXiv Machine Learning

Jul 27

Spectral Flow Certificates for Depth-Aware Long-Range Propagation in Graph Neural Networks

arXiv:2607. 21607v1 Announce Type: cross Abstract: Graph Neural Networks propagate information through local message passing, but the graph topologies themselves can silently prevent any amount of training from solving long-range tasks.

By Ranjan Veerabhadraswamy, Ajith Jubilson Emerson

benchmarks

arXiv Machine Learning

Jul 31

Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets

arXiv:2607. 28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions.

By Ali Rayat, Yunhao Fan, Gia-Wei Chern

benchmarks

arXiv Machine Learning

Jun 30

MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

arXiv:2606. 28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales.

By Manasa Kaniselvan, Alexander Maeder, Denghui Lu, Alexandros Nikolaos Ziogas, Mathieu Luisier

benchmarks

arXiv Machine Learning

Jul 22

GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots

arXiv:2607. 18591v1 Announce Type: cross Abstract: In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms.

By Lara Goncebat (Instituto de Qu\'imica Aplicada del Litoral IQAL), Rodrigo Echeveste (Instituto de Investigaci\'on en Se\~nales, Sistemas e Inteligencia Computacional sinc), Mat\'ias Gerard (Instituto de Investigaci\'on en Se\~nales, Sistemas e Inteligencia Computacional sinc), Frederik Tielens (General Chemistry), Gustavo Belletti (Instituto de Qu\'imica Aplicada del Litoral IQAL), Paola Quaino (Instituto de Qu\'imica Aplicada del Litoral IQAL)

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