Awesome Chemistry

Key papers

• A foundation model for atomistic materials chemistry (2024)
  Ilyes Batatia et al.
  14.08
• From enhanced sampling to reaction profiles (2021)
  Enrico Trizio et al.
  13.39
• i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations (2018)
  Venkat Kapil et al.
  13.35
• Ab initio thermodynamics of liquid and solid water (2018)
  Bingqing Cheng et al.
  13.23
• BERT Learns (and Teaches) Chemistry (2020)
  Josh Payne et al.
  10.07
• MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules (2023)
  D\'avid P\'eter Kov\'acs et al.
  10.02
• MassSpecGym: A benchmark for the discovery and identification of molecules (2024)
  Roman Bushuiev et al.
  9.94
• Space Group Informed Transformer for Crystalline Materials Generation (2024)
  Zhendong Cao et al.
  9.73
• DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively (2025)
  Yixuan Weng et al.
  9.55
• UMA: A Family of Universal Models for Atoms (2025)
  Brandon M. Wood et al.
  9.52
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials (2025)
  Junfan Xia et al.
  8.94
• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response (2024)
  Jia-Xin Zhu and Jun Cheng
  8.91
• Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields (2025)
  Ilyes Batatia et al.
  8.78
• Graph Neural Networks in Modern AI-aided Drug Discovery (2025)
  Odin Zhang et al.
  8.75
• Equivariant Neural Diffusion for Molecule Generation (2025)
  Fran\c{c}ois Cornet and Grigory Bartosh and Mikkel N. Schmidt and Christian A. Naesseth
  8.75
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text (2024)
  Nate Gruver et al.
  8.63
• Advances in modeling complex materials: The rise of neuroevolution potentials (2025)
  Penghua Ying et al.
  8.56
• A Graph Neural Network for the Era of Large Atomistic Models (2025)
  Duo Zhang et al.
  8.45
• A General Neural Network Potential for Energetic Materials with C, H, N, and O elements (2025)
  Mingjie Wen et al.
  8.29
• Learning-Order Autoregressive Models with Application to Molecular Graph Generation (2025)
  Zhe Wang et al.
  8.18
• Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional (2025)
  Sanjeev Raja et al.
  8.12
• Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer (2024)
  Javier Robledo-Moreno et al.
  8.02
• The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture (2025)
  Anuroop Sriram et al.
  7.97
• Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning (2025)
  Mateusz Praski et al.
  7.83
• Critical Limitations in Quantum-Selected Configuration Interaction Methods (2025)
  Peter Reinholdt et al.
  7.71
• Amortized Sampling with Transferable Normalizing Flows (2025)
  Charlie B. Tan et al.
  7.52
• Computing solvation free energies of small molecules with experimental accuracy (2024)
  J. Harry Moore et al.
  7.38
• MDCrow: Automating Molecular Dynamics Workflows with Large Language Models (2025)
  Quintina Campbell et al.
  7.35
• ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge (2025)
  Zihan Zhao et al.
  7.30
• MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks (2025)
  Guobin Zhao et al.
  7.24
• Unified modeling of 3D molecular generation via atomic interactions with PocketXMol. (2026)
  Xingang Peng et al.
  7.24
• Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces (2025)
  Henry B. Moss et al.
  7.11
• Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models (2025)
  Ruoyu Wang et al.
  7.02
• Systematic Analysis of Biomolecular Conformational Ensembles with PENSA (2022)
  Martin V\"ogele et al.
  7.01
• Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water (2025)
  Niamh O'Neill et al.
  6.97
• First-principles Hubbard parameters with automated and reproducible workflows (2025)
  Lorenzo Bastonero et al.
  6.89
• Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry (2025)
  Tsz Wai Ko et al.
  6.89
• PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences (2023)
  Martin Buttenschoen et al.
  6.77
• Benchmarking vibrational spectra: 5000 accurate eigenstates of acetonitrile using tree tensor network states (2025)
  Henrik R. Larsson
  6.75
• Less can be more for predicting properties with large language models (2024)
  Nawaf Alampara et al.
  6.74
• A predictive machine learning force field framework for liquid electrolyte development (2024)
  Sheng Gong et al.
  6.67
• Autofocused oracles for model-based design (2020)
  Clara Fannjiang and Jennifer Listgarten
  6.66
• MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models (2025)
  Srivathsan Badrinarayanan et al.
  6.64
• Accurate and scalable exchange-correlation with deep learning (2025)
  Giulia Luise et al.
  6.64
• Leveraging neural network interatomic potentials for a foundation model of chemistry (2025)
  So Yeon Kim et al.
  6.64
• Auxiliary-field quantum Monte Carlo method with quantum selected configuration interaction (2025)
  Yuichiro Yoshida et al.
  6.63
• Reasoning-Enhanced Large Language Models for Molecular Property Prediction (2025)
  Jiaxi Zhuang et al.
  6.62
• LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation (2025)
  Subhojyoti Khastagir et al.
  6.62
• The Open Catalyst 2025 (OC25) Dataset and Models for Solid-Liquid Interfaces (2025)
  Sushree Jagriti Sahoo et al.
  6.56
• Spontaneous Surface Charging and Janus Nature of the Hexagonal Boron Nitride-Water Interface (2025)
  Yongkang Wang et al.
  6.53
• An accurate and efficient framework for modelling the surface chemistry of ionic materials (2024)
  Benjamin X. Shi et al.
  6.52
• Green Drug Discovery: Leveraging Biodiversity for Sustainable Pharmaceutical Solutions (2026)
  Arshdeep Singh et al.
  6.52
• Democratising real-world drug discovery through agentic AI. (2026)
  Jiazhen He et al.
  6.52
• Simulating open-system molecular dynamics on analog quantum computers (2024)
  V. C. Olaya-Agudelo et al.
  6.45
• A deep learning model for chemical shieldings in molecular organic solids including anisotropy (2025)
  Matthias Kellner et al.
  6.39
• Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors (2025)
  Francesco Belli et al.
  6.39
• Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation (2025)
  Athena Caesura et al.
  6.36
• Fast and Fourier Features for Transfer Learning of Interatomic Potentials (2025)
  Pietro Novelli et al.
  6.34
• AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules (2026)
  Stephen E. Farr et al.
  6.34
• GP-MoLFormer: A Foundation Model For Molecular Generation (2024)
  Jerret Ross et al.
  6.33