Awesome Materials

Key papers

• A foundation model for atomistic materials chemistry (2024)
  Ilyes Batatia et al.
  14.08
• 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
• Artificial intelligence-driven approaches for materials design and discovery (2026)
  Mouyang Cheng et al.
  12.85
• Atom-Density Representations for Machine Learning (2018)
  Michael J. Willatt et al.
  11.69
• The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models (2025)
  Daniel S. Levine et al.
  11.16
• DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials (2025)
  Jinzhe Zeng et al.
  11.02
• Digital materials ecosystem: from databases to AI agents for autonomous discovery (2026)
  Di Zhang et al.
  9.81
• Space Group Informed Transformer for Crystalline Materials Generation (2024)
  Zhendong Cao et al.
  9.73
• UMA: A Family of Universal Models for Atoms (2025)
  Brandon M. Wood et al.
  9.52
• DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules (2025)
  Hongwei Du et al.
  9.49
• PET-MAD, a lightweight universal interatomic potential for advanced materials modeling (2025)
  Arslan Mazitov et al.
  9.48
• Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs) (2025)
  Prakash Thakolkaran et al.
  9.36
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (2023)
  Xuan Zhang et al.
  9.29
• Wyckoff Transformer: Generation of Symmetric Crystals (2025)
  Nikita Kazeev et al.
  9.15
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials (2025)
  Junfan Xia et al.
  8.94
• Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction (2025)
  Xiang Fu 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
• A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials (2025)
  Dongjin Kim et al.
  8.80
• Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields (2025)
  Ilyes Batatia et al.
  8.78
• AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries (2026)
  Abdullah Bin Faheem et al.
  8.77
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text (2024)
  Nate Gruver et al.
  8.63
• Roadmap on Advancements of the FHI-aims Software Package (2025)
  Joseph W. Abbott et al.
  8.58
• 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
• Machine learning Hubbard parameters with equivariant neural networks (2024)
  Martin Uhrin et al.
  8.34
• A General Neural Network Potential for Energetic Materials with C, H, N, and O elements (2025)
  Mingjie Wen et al.
  8.29
• Deep Learning Sheds Light on Integer and Fractional Topological Insulators (2025)
  Xiang Li et al.
  8.29
• OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery (2025)
  Vignesh Prabhakar et al.
  8.07
• PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations (2025)
  Ting Liang et al.
  8.06
• Bayesian Optimization of Catalysis With In-Context Learning (2023)
  Mayk Caldas Ramos et al.
  8.05
• Regression with Large Language Models for Materials and Molecular Property Prediction (2024)
  Ryan Jacobs et al.
  7.99
• The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture (2025)
  Anuroop Sriram et al.
  7.97
• A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity (2020)
  Mohammad Saber Hashemi et al.
  7.91
• High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture (2025)
  Haoyi Tan et al.
  7.89
• High pressure hydrogen by machine learning and quantum Monte Carlo (2021)
  Andrea Tirelli et al.
  7.76
• Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures (2025)
  Elham Kiyani (1) et al.
  7.71
• Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials (2024)
  Arif Ullah et al.
  7.68
• System of Agentic AI for the Discovery of Metal-Organic Frameworks (2025)
  Theo Jaffrelot Inizan et al.
  7.61
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science (2025)
  Lukas H\"ormann et al.
  7.55
• Advanced Space Mapping Technique Integrating a Shared Coarse Model for Multistate Tuning-Driven Multiphysics Optimization of Tunable Filters (2025)
  Haitian Hu et al.
  7.47
• Large Language Models to Accelerate Organic Chemistry Synthesis (2025)
  Yu Zhang et al.
  7.46
• Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning (2025)
  Qi You et al.
  7.29
• A physics-informed Bayesian optimization method for rapid development of electrical machines (2025)
  Pedram Asef and Christopher Vagg
  7.24
• Multimodal machine learning with large language embedding model for polymer property prediction (2025)
  Tianren Zhang and Dai-Bei Yang
  7.24
• MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks (2025)
  Guobin Zhao et al.
  7.24
• Self-Assembled Monolayers in p-i-n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning-Accelerated Material Discovery. (2026)
  Asmat Ullah et al.
  7.24
• Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials (2025)
  Denisa Martonov\'a et al.
  7.17
• Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework (2025)
  Divya Suman et al.
  7.13
• Does Hessian Data Improve the Performance of Machine Learning Potentials? (2025)
  Austin Rodriguez and Justin S. Smith and Jose L. Mendoza-Cortes
  7.07
• Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models (2025)
  Ruoyu Wang et al.
  7.02
• Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water (2025)
  Niamh O'Neill et al.
  6.97
• High-performance training and inference for deep equivariant interatomic potentials (2025)
  Chuin Wei Tan et al.
  6.95
• 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
• Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution (2025)
  Moin Uddin Maruf and Sungmin Kim and Zeeshan Ahmad
  6.89
• 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