{"list":"other","total":103,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"2407.21783","title":"The Llama 3 Herd of Models","created":"2024-07-31","cited_by_count":14715},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"2210.03629","title":"ReAct: Synergizing Reasoning and Acting in Language Models","created":"2022-10-06","cited_by_count":8695},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"1803.05457","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","created":"2018-03-14","cited_by_count":4886},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2305.10601","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","created":"2023-05-17","cited_by_count":3211},{"id":"1609.07843","title":"Pointer Sentinel Mixture Models","created":"2016-09-26","cited_by_count":3096},{"id":"1911.11641","title":"PIQA: Reasoning about Physical Commonsense in Natural Language","created":"2019-11-26","cited_by_count":2883},{"id":"2308.11432","title":"A Survey on Large Language Model based Autonomous Agents","created":"2023-08-22","cited_by_count":2242},{"id":"2209.09513","title":"Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering","created":"2022-09-20","cited_by_count":1919},{"id":"2112.09118","title":"Unsupervised Dense Information Retrieval with Contrastive Learning","created":"2021-12-16","cited_by_count":1216},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"1506.06724","title":"Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books","created":"2015-06-22","cited_by_count":1043},{"id":"2201.07207","title":"Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents","created":"2022-01-18","cited_by_count":1035},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"1707.06209","title":"Crowdsourcing Multiple Choice Science Questions","created":"2017-07-19","cited_by_count":765},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"1905.13319","title":"MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms","created":"2019-05-30","cited_by_count":662},{"id":"2304.05376","title":"ChemCrow: Augmenting large-language models with chemistry tools","created":"2023-04-11","cited_by_count":631},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"2206.07697","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields","created":"2022-06-15","cited_by_count":474},{"id":"2212.04088","title":"LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models","created":"2022-12-08","cited_by_count":462},{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441},{"id":"2004.07180","title":"SPECTER: Document-level Representation Learning using Citation-informed Transformers","created":"2020-04-15","cited_by_count":430},{"id":"2306.02707","title":"Orca: Progressive Learning from Complex Explanation Traces of GPT-4","created":"2023-06-05","cited_by_count":379},{"id":"2010.09885","title":"ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction","created":"2020-10-19","cited_by_count":337},{"id":"1811.09621","title":"GuacaMol: Benchmarking Models for De Novo Molecular Design","created":"2018-11-22","cited_by_count":297},{"id":"2305.16367","title":"Role-Play with Large Language Models","created":"2023-05-25","cited_by_count":294},{"id":"1811.12823","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","created":"2018-11-29","cited_by_count":275},{"id":"2110.06197","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","created":"2021-10-12","cited_by_count":270},{"id":"2204.05249","title":"Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics","created":"2022-04-11","cited_by_count":256},{"id":"2010.09990","title":"The Open Catalyst 2020 (OC20) Dataset and Community Challenges","created":"2020-10-20","cited_by_count":246},{"id":"1906.08230","title":"Evaluating Protein Transfer Learning with TAPE","created":"2019-06-19","cited_by_count":231},{"id":"1811.02633","title":"Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction","created":"2018-11-06","cited_by_count":229},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2411.12372","title":"RedPajama: an Open Dataset for Training Large Language Models","created":"2024-11-19","cited_by_count":182},{"id":"1906.00957","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","created":"2019-06-02","cited_by_count":172},{"id":"2102.05013","title":"Spherical Message Passing for 3D Graph Networks","created":"2021-02-09","cited_by_count":168},{"id":"2212.10505","title":"DePlot: One-shot visual language reasoning by plot-to-table translation","created":"2022-12-20","cited_by_count":160},{"id":"2309.04475","title":"Crystal Structure Prediction by Joint Equivariant Diffusion","created":"2023-07-30","cited_by_count":148},{"id":"2405.03548","title":"MAmmoTH2: Scaling Instructions from the Web","created":"2024-05-06","cited_by_count":145},{"id":"2312.03687","title":"MatterGen: a generative model for inorganic materials design","created":"2023-12-06","cited_by_count":98},{"id":"2206.08917","title":"The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts","created":"2022-06-17","cited_by_count":95},{"id":"2301.12586","title":"Unifying Molecular and Textual Representations via Multi-task Language Modelling","created":"2023-01-29","cited_by_count":93},{"id":"2402.04379","title":"Fine-Tuned Language Models Generate Stable Inorganic Materials as Text","created":"2024-02-06","cited_by_count":93},{"id":"2209.11807","title":"Periodic Graph Transformers for Crystal Material Property Prediction","created":"2022-09-23","cited_by_count":87},{"id":"2109.15290","title":"MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction","created":"2021-09-30","cited_by_count":83},{"id":"2305.10688","title":"MolXPT: Wrapping Molecules with Text for Generative Pre-training","created":"2023-05-18","cited_by_count":81},{"id":"2110.11624","title":"SciCap: Generating Captions for Scientific Figures","created":"2021-10-22","cited_by_count":77},{"id":"2402.03992","title":"Space Group Constrained Crystal Generation","created":"2024-02-06","cited_by_count":73},{"id":"2206.08515","title":"ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs","created":"2022-06-17","cited_by_count":69},{"id":"1910.00617","title":"Predicting materials properties without crystal structure: Deep representation learning from stoichiometry","created":"2019-10-01","cited_by_count":62},{"id":"2305.05708","title":"Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files","created":"2023-05-09","cited_by_count":59},{"id":"2410.23405","title":"FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions","created":"2024-10-30","cited_by_count":53},{"id":"2202.01338","title":"Regression Transformer: Concurrent sequence regression and generation for molecular language modeling","created":"2022-02-01","cited_by_count":50},{"id":"2205.14311","title":"MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation","created":"2022-05-28","cited_by_count":49},{"id":"2406.13163","title":"LLMatDesign: Autonomous Materials Discovery with Large Language Models","created":"2024-06-19","cited_by_count":42},{"id":"2403.15734","title":"Space Group Informed Transformer for Crystalline Materials Generation","created":"2024-03-23","cited_by_count":41},{"id":"2304.04662","title":"SELFormer: Molecular Representation Learning via SELFIES Language Models","created":"2023-04-10","cited_by_count":40},{"id":"2402.05200","title":"Are LLMs Ready for Real-World Materials Discovery?","created":"2024-02-07","cited_by_count":40},{"id":"2305.08264","title":"MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling","created":"2023-05-14","cited_by_count":35},{"id":"2403.11857","title":"Complete and Efficient Graph Transformers for Crystal Material Property Prediction","created":"2024-03-18","cited_by_count":34},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28},{"id":"2311.12410","title":"nach0: Multimodal Natural and Chemical Languages Foundation Model","created":"2023-11-21","cited_by_count":26},{"id":"2209.13136","title":"A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing","created":"2022-09-27","cited_by_count":22},{"id":"2310.08511","title":"HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science","created":"2023-10-12","cited_by_count":22},{"id":"2411.00177","title":"LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction","created":"2024-10-31","cited_by_count":21},{"id":"2206.07048","title":"A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing","created":"2022-06-15","cited_by_count":19},{"id":"2312.00111","title":"Multimodal Foundation Models for Material Property Prediction and Discovery","created":"2023-11-30","cited_by_count":19},{"id":"2412.09560","title":"Foundational Large Language Models for Materials Research","created":"2024-12-12","cited_by_count":18},{"id":"2502.13107","title":"MatterChat: A Multi-Modal LLM for Material Science","created":"2025-02-18","cited_by_count":18},{"id":"2403.05075","title":"Benchmarking Large Language Models for Molecule Prediction Tasks","created":"2024-03-08","cited_by_count":17},{"id":"2207.03928","title":"Accelerating Material Design with the Generative Toolkit for Scientific Discovery","created":"2022-07-08","cited_by_count":16},{"id":"2311.12289","title":"ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science","created":"2023-11-21","cited_by_count":15},{"id":"2210.17484","title":"The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science","created":"2022-10-31","cited_by_count":14},{"id":"2302.02303","title":"Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature","created":"2023-02-05","cited_by_count":13},{"id":"2405.04912","title":"GP-MoLFormer: A Foundation Model For Molecular Generation","created":"2024-04-04","cited_by_count":11},{"id":"2408.08044","title":"Crystalline Material Discovery in the Era of Artificial Intelligence","created":"2024-08-15","cited_by_count":11},{"id":"2411.08063","title":"MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration","created":"2024-11-10","cited_by_count":11},{"id":"2310.07197","title":"MatChat: A Large Language Model and Application Service Platform for Materials Science","created":"2023-10-11","cited_by_count":10},{"id":"2411.09429","title":"AI-driven inverse design of materials: Past, present and future","created":"2024-11-14","cited_by_count":10},{"id":"2410.21341","title":"Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge","created":"2024-10-28","cited_by_count":9},{"id":"2307.05378","title":"M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery","created":"2023-06-14","cited_by_count":8},{"id":"2403.10301","title":"Uni-SMART: Universal Science Multimodal Analysis and Research Transformer","created":"2024-03-15","cited_by_count":7},{"id":"2506.12557","title":"Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials","created":"2025-06-14","cited_by_count":7},{"id":"2407.20267","title":"A Large Encoder-Decoder Family of Foundation Models For Chemical Language","created":"2024-07-24","cited_by_count":5}],"next_cursor":"100"}