{"total":25,"rows":[{"id":"2306.10045","title":"Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction","created":"2023-06-12","cited_by_count":37,"area":null},{"id":"2411.00177","title":"LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction","created":"2024-10-31","cited_by_count":21,"area":null},{"id":"2401.08032","title":"Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study","created":"2024-01-16","cited_by_count":17,"area":null},{"id":"2211.02235","title":"Materials Property Prediction with Uncertainty Quantification: A Benchmark Study","created":"2022-11-04","cited_by_count":15,"area":null},{"id":"2501.03278","title":"DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules","created":"2025-01-05","cited_by_count":13,"area":null},{"id":"2204.11953","title":"Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials","created":"2022-04-25","cited_by_count":8,"area":null},{"id":"2201.05649","title":"Formula graph self-attention network for representation-domain independent materials discovery","created":"2022-01-14","cited_by_count":7,"area":null},{"id":"2502.10828","title":"The Vendiscope: An Algorithmic Microscope For Data Collections","created":"2025-02-15","cited_by_count":5,"area":null},{"id":"2309.06710","title":"Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm","created":"2023-09-13","cited_by_count":4,"area":null},{"id":"2212.11246","title":"Material Property Prediction using Graphs based on Generically Complete Isometry Invariants","created":"2022-12-19","cited_by_count":2,"area":null},{"id":"2506.11332","title":"Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control","created":"2025-06-12","cited_by_count":2,"area":null},{"id":"2206.13578","title":"Materials Transformers Language Models for Generative Materials Design: a benchmark study","created":"2022-06-27","cited_by_count":1,"area":null},{"id":"2209.11234","title":"Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering","created":"2022-09-15","cited_by_count":1,"area":null},{"id":"2310.18907","title":"Topological, or Non-topological? A Deep Learning Based Prediction","created":"2023-10-29","cited_by_count":1,"area":null},{"id":"2501.05903","title":"Discovery of sustainable energy materials via the machine-learned material space","created":"2025-01-10","cited_by_count":1,"area":null},{"id":"2511.11697","title":"Benchmarking GNNs for OOD Materials Property Prediction with Uncertainty Quantification","created":"2025-11-12","cited_by_count":1,"area":null},{"id":"2306.10766","title":"Substitutional Alloying Using Crystal Graph Neural Networks","created":"2023-06-19","cited_by_count":0,"area":null},{"id":"2307.04351","title":"MD-HIT: Machine learning for materials property prediction with dataset redundancy control","created":"2023-07-10","cited_by_count":0,"area":null},{"id":"2401.15089","title":"Accelerating Material Property Prediction using Generically Complete Isometry Invariants","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2404.04810","title":"AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning","created":"2024-04-07","cited_by_count":0,"area":null},{"id":"2503.07378","title":"A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2509.08418","title":"Facet: highly efficient E(3)-equivariant networks for interatomic potentials","created":"2025-09-10","cited_by_count":0,"area":null},{"id":"2510.13916","title":"Element2Vec: Build Chemical Element Representation from Text for Property Prediction","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2606.29717","title":"Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.11526","title":"AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking","created":"2026-07-13","cited_by_count":0,"area":null}],"next_cursor":null}