{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/property-prediction/papers/5","list_of":"/task/property-prediction","task":"Property Prediction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"of":691,"counts":{"archive_papers_tagged":691,"with_a_code_link":352,"where_syntology_ran_a_sample":116,"not_listed_spam_title":0,"listed":691,"listed_where_code_ran":116,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":96,"every_run_a_failure_of_syntologys_instrument":20,"listed_with_a_run_with_no_instrument_failure":96,"listed_every_run_a_failure_of_syntologys_instrument":20,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/property-prediction","prev":"/task/property-prediction/papers/4","next":"/task/property-prediction/papers/6","papers":[{"url":null,"slug":"2503-01022","title":"LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery","date":"2025-03-02","arxiv_id":"2503.01022","repositories_listed":0,"syntology":null},{"url":null,"slug":"qcs-adme-quantum-circuit-search-for-drug","title":"QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation","date":"2025-03-02","arxiv_id":"2503.01927","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatmol-a-versatile-molecule-designer-based","title":"ChatMol: A Versatile Molecule Designer Based on the Numerically Enhanced Large Language Model","date":"2025-02-27","arxiv_id":"2502.19794","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-admet-an-effective-and-interpretable","title":"Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction","date":"2025-02-22","arxiv_id":"2502.16378","repositories_listed":0,"syntology":null},{"url":null,"slug":"moma-a-modular-deep-learning-framework-for","title":"MoMa: A Modular Deep Learning Framework for Material Property Prediction","date":"2025-02-21","arxiv_id":"2502.15483","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-graph-learning-will-lose-relevance","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","date":"2025-02-20","arxiv_id":"2502.14546","repositories_listed":0,"syntology":null},{"url":null,"slug":"matterchat-a-multi-modal-llm-for-material","title":"MatterChat: A Multi-Modal LLM for Material Science","date":"2025-02-18","arxiv_id":"2502.13107","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-aware-contrastive-heterogeneous","title":"Knowledge-aware contrastive heterogeneous molecular graph learning","date":"2025-02-17","arxiv_id":"2502.11711","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-smooth-and-expressive-interatomic","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","date":"2025-02-17","arxiv_id":"2502.12147","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-deployed-chain-of-thought-cot","title":"Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data","date":"2025-02-17","arxiv_id":"2502.12383","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-universal-scaling-and-ultra-small","title":"Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity","date":"2025-02-11","arxiv_id":"2502.07293","repositories_listed":0,"syntology":null},{"url":null,"slug":"cast-cross-attention-based-multimodal-fusion","title":"CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction","date":"2025-02-06","arxiv_id":"2502.06836","repositories_listed":0,"syntology":null},{"url":"/paper/mol-llm-generalist-molecular-llm-with","slug":"mol-llm-generalist-molecular-llm-with","title":"Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization","date":"2025-02-05","arxiv_id":"2502.02810","repositories_listed":0,"syntology":null},{"url":null,"slug":"regnet-reciprocal-space-aware-long-range","title":"ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction","date":"2025-02-04","arxiv_id":"2502.02748","repositories_listed":0,"syntology":null},{"url":null,"slug":"fragmentnet-adaptive-graph-fragmentation-for","title":"FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning","date":"2025-02-03","arxiv_id":"2502.01184","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-metal-microstructural-heterogeneity","title":"Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features","date":"2025-01-30","arxiv_id":"2501.18064","repositories_listed":0,"syntology":null},{"url":null,"slug":"molgraph-xlstm-a-graph-based-dual-level-xlstm","title":"MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability","date":"2025-01-30","arxiv_id":"2501.18439","repositories_listed":0,"syntology":null},{"url":null,"slug":"tensor-completion-for-surrogate-modeling-of","title":"Tensor Completion for Surrogate Modeling of Material Property Prediction","date":"2025-01-30","arxiv_id":"2501.18137","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-molecular-evolution-mechanism-enhance","title":"Can Molecular Evolution Mechanism Enhance Molecular Representation?","date":"2025-01-27","arxiv_id":"2501.15799","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-multiple-models-using-labeled-and","title":"Evaluating multiple models using labeled and unlabeled data","date":"2025-01-21","arxiv_id":"2501.11866","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-modality-representation-learning-for","title":"Dual-Modality Representation Learning for Molecular Property Prediction","date":"2025-01-11","arxiv_id":"2501.06608","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-generative-pre-trained-transformer","title":"Graph Generative Pre-trained Transformer","date":"2025-01-02","arxiv_id":"2501.01073","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-contrastive-learning-with","title":"Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-graph-neural-networks-on-graph","title":"Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements","date":"2025-01-01","arxiv_id":"2501.00773","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastchgnet-training-one-universal-interatomic","title":"FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs","date":"2024-12-30","arxiv_id":"2412.20796","repositories_listed":0,"syntology":null},{"url":null,"slug":"category-specific-topological-learning-of","title":"Category-Specific Topological Learning of Metal-Organic Frameworks","date":"2024-12-16","arxiv_id":"2412.11386","repositories_listed":0,"syntology":null},{"url":null,"slug":"evollama-enhancing-llms-understanding-of","title":"EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations","date":"2024-12-16","arxiv_id":"2412.11618","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-model-driven-a-protac-generation","title":"Language model driven: a PROTAC generation pipeline with dual constraints of structure and property","date":"2024-12-12","arxiv_id":"2412.09661","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokenizing-3d-molecule-structure-with","title":"Tokenizing 3D Molecule Structure with Quantized Spherical Coordinates","date":"2024-12-02","arxiv_id":"2412.01564","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-data-driven-predictions-of-band-gap","title":"Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials","date":"2024-11-21","arxiv_id":"2411.14034","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-graph-neural-networks-and","title":"Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction","date":"2024-11-20","arxiv_id":"2411.13688","repositories_listed":0,"syntology":null},{"url":null,"slug":"seqproft-applying-lora-finetuning-for","title":"SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions","date":"2024-11-18","arxiv_id":"2411.11530","repositories_listed":0,"syntology":null},{"url":null,"slug":"material-property-prediction-with-element","title":"Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning","date":"2024-11-13","arxiv_id":"2411.08414","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-pretraining-for-molecular-property","title":"Two-Stage Pretraining for Molecular Property Prediction in the Wild","date":"2024-11-05","arxiv_id":"2411.03537","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-representation-anchor-network-to","title":"Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery","date":"2024-10-28","arxiv_id":"2410.20711","repositories_listed":0,"syntology":null},{"url":null,"slug":"pepdora-a-unified-peptide-language-model-via","title":"PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation","date":"2024-10-28","arxiv_id":"2410.20667","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-guided-multi-property-molecular","title":"Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model","date":"2024-10-17","arxiv_id":"2410.13597","repositories_listed":0,"syntology":null},{"url":null,"slug":"helm-hierarchical-encoding-for-mrna-language","title":"HELM: Hierarchical Encoding for mRNA Language Modeling","date":"2024-10-16","arxiv_id":"2410.12459","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-sequence-impact-of-geometric-context","title":"Beyond Sequence: Impact of Geometric Context for RNA Property Prediction","date":"2024-10-15","arxiv_id":"2410.11933","repositories_listed":0,"syntology":null},{"url":null,"slug":"ka-gnn-kolmogorov-arnold-graph-neural","title":"KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction","date":"2024-10-15","arxiv_id":"2410.11323","repositories_listed":0,"syntology":null},{"url":null,"slug":"unigem-a-unified-approach-to-generation-and","title":"UniGEM: A Unified Approach to Generation and Property Prediction for Molecules","date":"2024-10-14","arxiv_id":"2410.10516","repositories_listed":0,"syntology":null},{"url":"/paper/predicting-molecular-ground-state","slug":"predicting-molecular-ground-state","title":"WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction","date":"2024-10-13","arxiv_id":"2410.09795","repositories_listed":0,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/predicting-molecular-ground-state#ran","syntology_url":"https://syntology.ai/paper/2410.09795","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09795"}},"official":null}},{"url":null,"slug":"tapweight-reweighting-pretraining-objectives","title":"TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining","date":"2024-10-13","arxiv_id":"2410.10006","repositories_listed":0,"syntology":null},{"url":null,"slug":"molecular-topological-deep-learning-for","title":"Molecular topological deep learning for polymer property prediction","date":"2024-10-07","arxiv_id":"2410.04765","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-multi-task-transfer-learning-for","title":"Scalable Multi-Task Transfer Learning for Molecular Property Prediction","date":"2024-10-01","arxiv_id":"2410.00432","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-addition-in-multi-task-learning-by","title":"Task Addition in Multi-Task Learning by Geometrical Alignment","date":"2024-09-25","arxiv_id":"2409.16645","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-in-drug-discovery","title":"Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries","date":"2024-09-24","arxiv_id":"2409.15645","repositories_listed":0,"syntology":null},{"url":"/paper/dumpling-gnn-hybrid-gnn-enables-better-adc","slug":"dumpling-gnn-hybrid-gnn-enables-better-adc","title":"Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure","date":"2024-09-23","arxiv_id":"2410.05278","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-performance-and-robustness-of","title":"Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions","date":"2024-09-22","arxiv_id":"2409.14572","repositories_listed":0,"syntology":null},{"url":null,"slug":"smirk-an-atomically-complete-tokenizer-for","title":"Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models","date":"2024-09-19","arxiv_id":"2409.15370","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-with-large-language-models-for","title":"Regression with Large Language Models for Materials and Molecular Property Prediction","date":"2024-09-09","arxiv_id":"2409.06080","repositories_listed":0,"syntology":null},{"url":null,"slug":"crysatom-distributed-representation-of-atoms","title":"CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction","date":"2024-09-07","arxiv_id":"2409.04737","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-training-of-transformers-for","title":"Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets","date":"2024-09-07","arxiv_id":"2409.04909","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-crystal-property","title":"Self-supervised learning for crystal property prediction via denoising","date":"2024-08-30","arxiv_id":"2408.17255","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-learning-for-chemistry-property","title":"Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning","date":"2024-08-27","arxiv_id":"2408.14964","repositories_listed":0,"syntology":null},{"url":null,"slug":"zeoformer-coarse-grained-periodic-graph","title":"PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction","date":"2024-08-23","arxiv_id":"2408.12984","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-chemistry-foundation-models-to","title":"Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design","date":"2024-08-21","arxiv_id":"2408.11793","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancements-in-molecular-property-prediction","title":"Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches","date":"2024-08-18","arxiv_id":"2408.09461","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-materials-property","title":"Out-of-distribution materials property prediction using adversarial learning based fine-tuning","date":"2024-08-17","arxiv_id":"2408.09297","repositories_listed":0,"syntology":null},{"url":null,"slug":"lipidbert-a-lipid-language-model-pre-trained","title":"LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library","date":"2024-08-12","arxiv_id":"2408.06150","repositories_listed":0,"syntology":null},{"url":null,"slug":"smiles-mamba-chemical-mamba-foundation-models","title":"SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction","date":"2024-08-11","arxiv_id":"2408.05696","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-molecular-machine-learned","title":"Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs","date":"2024-08-08","arxiv_id":"2408.04520","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-rephrasing-for-quantifying","title":"Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks","date":"2024-08-07","arxiv_id":"2408.03732","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01018","title":"GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs","date":"2024-08-02","arxiv_id":"2408.01018","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01530","title":"A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning","date":"2024-08-02","arxiv_id":"2408.01530","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-learning-for-molecular","title":"Distribution Learning for Molecular Regression","date":"2024-07-30","arxiv_id":"2407.20475","repositories_listed":0,"syntology":null},{"url":null,"slug":"rnacg-a-universal-rna-sequence-conditional","title":"RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching","date":"2024-07-29","arxiv_id":"2407.19838","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-residual-based-method-for-molecular","title":"Graph Residual based Method for Molecular Property Prediction","date":"2024-07-27","arxiv_id":"2408.03342","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-material-property-prediction-with","title":"Enhancing material property prediction with ensemble deep graph convolutional networks","date":"2024-07-26","arxiv_id":"2407.18847","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-latent-slot-diffusion-for-object","title":"Guided Latent Slot Diffusion for Object-Centric Learning","date":"2024-07-25","arxiv_id":"2407.17929","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotationally-invariant-latent-distances-for","title":"Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances","date":"2024-07-15","arxiv_id":"2407.10844","repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-with-fractional-denoising-to","title":"Pre-training with Fractional Denoising to Enhance Molecular Property Prediction","date":"2024-07-14","arxiv_id":"2407.11086","repositories_listed":0,"syntology":null},{"url":null,"slug":"token-mol-1-0-tokenized-drug-design-with","title":"Token-Mol 1.0: Tokenized drug design with large language model","date":"2024-07-10","arxiv_id":"2407.07930","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-01426","title":"MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction","date":"2024-07-09","arxiv_id":"2408.01426","repositories_listed":0,"syntology":null},{"url":null,"slug":"freecg-free-the-design-space-of-clebsch","title":"FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields","date":"2024-07-02","arxiv_id":"2407.02263","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-hop-a-framework-for-studying-the-importance","title":"T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction","date":"2024-06-29","arxiv_id":"2407.14270","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-performance-prediction-of","title":"Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model","date":"2024-06-28","arxiv_id":"2406.19792","repositories_listed":0,"syntology":null},{"url":null,"slug":"2406-15515","title":"Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots","date":"2024-06-20","arxiv_id":"2406.15515","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-neural-tangent-kernels","title":"Equivariant Neural Tangent Kernels","date":"2024-06-10","arxiv_id":"2406.06504","repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-neural-networks-go-persistent","title":"Topological Neural Networks go Persistent, Equivariant, and Continuous","date":"2024-06-05","arxiv_id":"2406.03164","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-context-learning-of-physical-properties","title":"In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs","date":"2024-06-03","arxiv_id":"2406.01808","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaffold-splits-overestimate-virtual","title":"Scaffold Splits Overestimate Virtual Screening Performance","date":"2024-06-02","arxiv_id":"2406.00873","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-generative-molecular-design-via","title":"Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders","date":"2024-05-31","arxiv_id":"2405.20573","repositories_listed":0,"syntology":null},{"url":null,"slug":"sheaf-hypernetworks-for-personalized","title":"Sheaf HyperNetworks for Personalized Federated Learning","date":"2024-05-31","arxiv_id":"2405.20882","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-model-with-bert-roberta-and-xlnet","title":"Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction","date":"2024-05-30","arxiv_id":"2406.06553","repositories_listed":0,"syntology":null},{"url":null,"slug":"determining-domain-of-machine-learning-models","title":"A General Approach for Determining Applicability Domain of Machine Learning Models","date":"2024-05-28","arxiv_id":"2406.05143","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-molecular-property-prediction","title":"Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models","date":"2024-05-25","arxiv_id":"2405.16041","repositories_listed":0,"syntology":null},{"url":null,"slug":"glad-synergizing-molecular-graphs-and","title":"GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices","date":"2024-05-23","arxiv_id":"2405.14203","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-quantum-graph-neural-network-for","title":"Hybrid Quantum Graph Neural Network for Molecular Property Prediction","date":"2024-05-08","arxiv_id":"2405.05205","repositories_listed":0,"syntology":null},{"url":null,"slug":"hemenet-heterogeneous-multichannel","title":"HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning","date":"2024-04-02","arxiv_id":"2404.01693","repositories_listed":0,"syntology":null},{"url":null,"slug":"alloybert-alloy-property-prediction-with","title":"AlloyBERT: Alloy Property Prediction with Large Language Models","date":"2024-03-28","arxiv_id":"2403.19783","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-exploration-of-high-tc","title":"A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints","date":"2024-03-20","arxiv_id":"2403.13627","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-consistency-training-for-hamiltonian","title":"Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction","date":"2024-03-14","arxiv_id":"2403.09560","repositories_listed":0,"syntology":null},{"url":null,"slug":"representing-molecules-as-random-walks-over","title":"Representing Molecules as Random Walks Over Interpretable Grammars","date":"2024-03-13","arxiv_id":"2403.08147","repositories_listed":0,"syntology":null},{"url":null,"slug":"sort-slice-a-simple-and-superior-alternative","title":"Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints","date":"2024-03-10","arxiv_id":"2403.17954","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-space-optimization-improved-molecule","title":"Molecule Design by Latent Prompt Transformer","date":"2024-02-27","arxiv_id":"2402.17179","repositories_listed":0,"syntology":null},{"url":null,"slug":"material-microstructure-design-using-vae","title":"Material Microstructure Design Using VAE-Regression with Multimodal Prior","date":"2024-02-27","arxiv_id":"2402.17806","repositories_listed":0,"syntology":null},{"url":null,"slug":"bba-bi-modal-behavioral-alignment-for","title":"BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models","date":"2024-02-21","arxiv_id":"2402.13577","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-pretrained-transformer-for","title":"An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning","date":"2024-02-20","arxiv_id":"2402.12714","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-domain-knowledge-and-multi","title":"Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey","date":"2024-02-11","arxiv_id":"2402.07249","repositories_listed":0,"syntology":null}],"record_sha256":"e12384028a3aa06ae5dd46dd7337a8c392c8c8e978d2be9d0333a201687cec52","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}