Key papers

• Position: The Inevitable Transition to Machine Learning in Quantum Chemistry (2026)
  Karen Sargsyan et al.
  3.45
• Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery (2026)
  Panagiotis Krokidas et al.
  1.94
• My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents (2026)
  C\'esar Ojeda et al.
  1.89
• GAT-GCN: Drug Molecular Solubility Prediction Based on Graph Neural Networks (2026)
  Yiyue Hu
  1.89
• Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators (2026)
  S. A. Shteingolts et al.
  1.83
• VIANA: character Value-enhanced Intensity Assessment via domain-informed Neural Architecture (2026)
  Luana P. Queiroz et al.
  1.78
• XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures (2026)
  Vitor F. Grizzi et al.
  1.78
• ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures (2026)
  Jinming Fan et al.
  1.72
• Autotuning T-PaiNN: Enabling Data-Efficient GNN Interatomic Potential Development via Classical-to-Quantum Transfer Learning (2026)
  Vivienne Pelletier et al.
  1.72
• A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement (2026)
  Chengchun Liu et al.
  1.61
• AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations (2025)
  Mingxu Zhang and Dazhong Shen and Ying Sun
  1.56
• Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction (2025)
  Mikhail Tsitsvero et al.
  1.56
• Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery (2025)
  Guikun Xu et al.
  1.56
• Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction (2025)
  Jonathan Broadbent et al.
  1.50
• Transformers Discover Molecular Structure Without Graph Priors (2025)
  Tobias Kreiman et al.
  1.44
• RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models (2025)
  Shikun Liu et al.
  1.39
• Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study (2025)
  Aryan Gupta
  1.39
• DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction (2025)
  Manajit Das et al.
  1.39
• Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials (2025)
  Cong Fu et al.
  1.28
• Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model (2025)
  Jianshen Zhu et al.
  1.28
• Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations (2025)
  Shagun Maheshwari et al.
  1.22
• Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution (2025)
  Moin Uddin Maruf and Sungmin Kim and Zeeshan Ahmad
  1.06
• FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning (2025)
  Ankur Samanta et al.
  1.00
• FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching (2025)
  Joongwon Lee et al.
  1.00
• Graph Neural Networks embedded into Margules model for vapor-liquid equilibria prediction (2025)
  Edgar Ivan Sanchez Medina and Kai Sundmacher
  1.00
• EquiBoost: An Equivariant Boosting Approach to Molecular Conformation Generation (2025)
  Yixuan Yang et al.
  0.94
• Efficient Transition State Searches by Freezing String Method with Graph Neural Network Potentials (2025)
  Jonah Marks and Joseph Gomes
  0.94
• A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation (2025)
  \`Alex Sol\'e et al.
  0.94
• Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration (2025)
  Paul Fuchs et al.
  0.94
• Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics (2024)
  Zihan Pengmei et al.
  0.83
• MoFlow: An Invertible Flow Model for Generating Molecular Graphs (2020)
  Chengxi Zang and Fei Wang
  —
• Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits (2020)
  Miko{\l}aj Sacha et al.
  —
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features (2020)
  Zhuoran Qiao et al.
  —
• Coarse Graining Molecular Dynamics with Graph Neural Networks (2020)
  Brooke E. Husic et al.
  —
• Energy-based View of Retrosynthesis (2020)
  Ruoxi Sun et al.
  —
• Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (2020)
  Benjamin Kurt Miller et al.
  —
• Non-normality and non-monotonic dynamics in complex reaction networks (2020)
  Zachary G. Nicolaou et al.
  —
• Scalar Coupling Constant Prediction Using Graph Embedding Local Attention Encoder (2020)
  Caiqing Jian et al.
  —
• Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder (2020)
  Navid Shervani-Tabar et al.
  —
• Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020)
  Tomohide Masuda et al.
  —
• Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces (2020)
  Zhuoran Qiao et al.
  —
• On the equivalence of molecular graph convolution and molecular wave function with poor basis set (2020)
  Masashi Tsubaki and Teruyasu Mizoguchi
  —
• Symmetry-Aware Actor-Critic for 3D Molecular Design (2020)
  Gregor N. C. Simm et al.
  —
• Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction (2020)
  Agnieszka Pocha et al.
  —
• High throughput screening with machine learning (2020)
  Oleksandr Gurbych et al.
  —
• Auto-Encoding Molecular Conformations (2021)
  Robin Winter et al.
  —
• Equivariant message passing for the prediction of tensorial properties and molecular spectra (2021)
  Kristof T. Sch\"utt et al.
  —
• Graph Energy-based Model for Substructure Preserving Molecular Design (2021)
  Ryuichiro Hataya et al.
  —
• Molecular Contrastive Learning of Representations via Graph Neural Networks (2021)
  Yuyang Wang et al.
  —
• Learning Neural Generative Dynamics for Molecular Conformation Generation (2021)
  Minkai Xu et al.
  —
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions (2021)
  Ramin Ansari and Amirata Ghorbani
  —
• Learning Gradient Fields for Molecular Conformation Generation (2021)
  Chence Shi et al.
  —
• E(n) Equivariant Normalizing Flows (2021)
  Victor Garcia Satorras et al.
  —
• Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs (2021)
  Willis Hoke et al.
  —
• Multiresolution Equivariant Graph Variational Autoencoder (2021)
  Truong Son Hy and Risi Kondor
  —
• MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation (2021)
  Maksim Kuznetsov et al.
  —
• ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction (2021)
  Xiaomin Fang et al.
  —
• GemNet: Universal Directional Graph Neural Networks for Molecules (2021)
  Johannes Gasteiger et al.
  —
• Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks (2021)
  Michael Hunter Ashby and Jenna A. Bilbrey
  —
• Realistic molecule optimization on a learned graph manifold (2021)
  R\'emy Brossard et al.
  —