Key papers

• Artificial intelligence-driven approaches for materials design and discovery (2026)
  Mouyang Cheng et al.
  14.70
• 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
• 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.
  11.05
• DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials (2025)
  Jinzhe Zeng et al.
  10.91
• AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries (2026)
  Abdullah Bin Faheem et al.
  10.71
• Digital materials ecosystem: from databases to AI agents for autonomous discovery (2026)
  Di Zhang et al.
  10.48
• UMA: A Family of Universal Models for Atoms (2025)
  Brandon M. Wood et al.
  10.14
• Space Group Informed Transformer for Crystalline Materials Generation (2024)
  Zhendong Cao et al.
  9.73
• DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules (2025)
  Hongwei Du et al.
  9.38
• PET-MAD, a lightweight universal interatomic potential for advanced materials modeling (2025)
  Arslan Mazitov et al.
  9.37
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (2023)
  Xuan Zhang et al.
  9.29
• Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs) (2025)
  Prakash Thakolkaran et al.
  9.24
• 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.
  9.05
• Wyckoff Transformer: Generation of Symmetric Crystals (2025)
  Nikita Kazeev et al.
  9.04
• Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction (2025)
  Xiang Fu et al.
  8.98
• Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response (2024)
  Jia-Xin Zhu and Jun Cheng
  8.91
• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials (2025)
  Junfan Xia et al.
  8.83
• Large Language Models to Accelerate Organic Chemistry Synthesis (2025)
  Yu Zhang et al.
  8.79
• A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials (2025)
  Dongjin Kim et al.
  8.69
• Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields (2025)
  Ilyes Batatia et al.
  8.67
• Materials Informatics: Emergence to Autonomous Discovery in the Age of AI. (2026)
  T. Lookman et al.
  8.66
• 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.
  8.47
• Advances in modeling complex materials: The rise of neuroevolution potentials (2025)
  Penghua Ying et al.
  8.45
• Machine learning Hubbard parameters with equivariant neural networks (2024)
  Martin Uhrin et al.
  8.34
• A Graph Neural Network for the Era of Large Atomistic Models (2025)
  Duo Zhang et al.
  8.34
• 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.
  8.26
• A General Neural Network Potential for Energetic Materials with C, H, N, and O elements (2025)
  Mingjie Wen et al.
  8.17
• Deep Learning Sheds Light on Integer and Fractional Topological Insulators (2025)
  Xiang Li et al.
  8.17
• Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review (2025)
  Dilshod Nematov and Mirabbos Hojamberdiev
  8.17
• Bayesian Optimization of Catalysis With In-Context Learning (2023)
  Mayk Caldas Ramos et al.
  8.05
• Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer (2024)
  Javier Robledo-Moreno et al.
  8.02
• Regression with Large Language Models for Materials and Molecular Property Prediction (2024)
  Ryan Jacobs et al.
  7.99
• Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures (2025)
  Elham Kiyani (1) et al.
  7.96
• OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery (2025)
  Vignesh Prabhakar et al.
  7.96
• PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations (2025)
  Ting Liang et al.
  7.95
• A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity (2020)
  Mohammad Saber Hashemi et al.
  7.91
• The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture (2025)
  Anuroop Sriram et al.
  7.86
• High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture (2025)
  Haoyi Tan et al.
  7.78
• High pressure hydrogen by machine learning and quantum Monte Carlo (2021)
  Andrea Tirelli et al.
  7.76
• Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry (2025)
  Tsz Wai Ko et al.
  7.71
• AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach (2025)
  Amirhossein Khakpour et al.
  7.71
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science (2025)
  Lukas H\"ormann et al.
  7.71
• Spontaneous Surface Charging and Janus Nature of the Hexagonal Boron Nitride-Water Interface (2025)
  Yongkang Wang et al.
  7.64
• MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models (2025)
  Srivathsan Badrinarayanan et al.
  7.61
• System of Agentic AI for the Discovery of Metal-Organic Frameworks (2025)
  Theo Jaffrelot Inizan et al.
  7.50
• Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data (2025)
  Hyeonbin Moon et al.
  7.50
• Kosmos: An AI Scientist for Autonomous Discovery (2025)
  Ludovico Mitchener et al.
  7.41
• Advanced Space Mapping Technique Integrating a Shared Coarse Model for Multistate Tuning-Driven Multiphysics Optimization of Tunable Filters (2025)
  Haitian Hu et al.
  7.36
• Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs (2024)
  Daniil A. Boiko et al.
  7.30
• A multi-agentic framework for real-time, autonomous freeform metasurface design (2025)
  Robert Lupoiu et al.
  7.29
• Gold-Standard Chemical Database 137 (GSCDB137): A diverse set of accurate energy differences for assessing and developing density functionals (2025)
  Jiashu Liang and Martin Head-Gordon
  7.24
• A physics-informed Bayesian optimization method for rapid development of electrical machines (2025)
  Pedram Asef and Christopher Vagg
  7.13
• Multimodal machine learning with large language embedding model for polymer property prediction (2025)
  Tianren Zhang and Dai-Bei Yang
  7.13
• MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks (2025)
  Guobin Zhao et al.
  7.13
• Machine Learning Toolkits and Frameworks for Materials Design (2026)
  B. Abraham et al.
  7.13
• Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges (2026)
  Avadhesh Kumar Yadav
  7.13