Awesome Physics ML

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
• Atom-Density Representations for Machine Learning (2018)
  Michael J. Willatt et al.
  11.69
• InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery (2026)
  Shiyang Feng et al.
  11.14
• DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials (2025)
  Jinzhe Zeng et al.
  11.02
• Physics-Informed Diffusion Models (2024)
  Jan-Hendrik Bastek et al.
  10.79
• DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks (2025)
  Qile Jiang et al.
  10.30
• Recursive Flow Matching (2026)
  Jiahe Huang et al.
  10.00
• 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
• 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
• Multi-Objective Loss Balancing for Physics-Informed Deep Learning (2021)
  Rafael Bischof et al.
  9.39
• Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs) (2025)
  Prakash Thakolkaran et al.
  9.36
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics (2025)
  Gert Aarts et al.
  9.31
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (2023)
  Xuan Zhang et al.
  9.29
• Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning (2024)
  Luis Mandl et al.
  9.22
• Wyckoff Transformer: Generation of Symmetric Crystals (2025)
  Nikita Kazeev et al.
  9.15
• Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems (2025)
  Siavash Khodakarami et al.
  9.00
• 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
• Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study (2025)
  Jassem Abbasi et al.
  8.84
• A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials (2025)
  Dongjin Kim et al.
  8.80
• A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (2020)
  Youngkyu Kim et al.
  8.66
• 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
• Combining physics-based and data-driven models: advancing the frontiers of research with Scientific Machine Learning (2025)
  Alfio Quarteroni and Paola Gervasio and Francesco Regazzoni
  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
• Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble (2025)
  Zongren Zou 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
• PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations (2025)
  Ting Liang et al.
  8.06
• Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer (2024)
  Javier Robledo-Moreno et al.
  8.02
• A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity (2020)
  Mohammad Saber Hashemi et al.
  7.91
• Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics (2024)
  Fanny Lehmann et al.
  7.88
• AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach (2025)
  Amirhossein Khakpour et al.
  7.82
• Learning Hidden Physics and System Parameters with Deep Operator Networks (2024)
  Dibakar Roy Sarkar et al.
  7.77
• 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
• Critical Limitations in Quantum-Selected Configuration Interaction Methods (2025)
  Peter Reinholdt et al.
  7.71
• Physics-informed graph neural networks for flow field estimation in carotid arteries (2024)
  Julian Suk et al.
  7.55
• 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
• QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs (2025)
  Afrah Farea et al.
  7.40
• Computing solvation free energies of small molecules with experimental accuracy (2024)
  J. Harry Moore et al.
  7.38
• AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning (2025)
  Qile Jiang et al.
  7.33
• 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
• Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks (2025)
  Elham Kiyani et al.
  7.29
• A physics-informed Bayesian optimization method for rapid development of electrical machines (2025)
  Pedram Asef and Christopher Vagg
  7.24
• Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials (2025)
  Denisa Martonov\'a et al.
  7.17
• Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML (2023)
  Hrishikesh Viswanath et al.
  7.16
• Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework (2025)
  Divya Suman et al.
  7.13
• Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology (2025)
  Nazanin Ahmadi Daryakenari et al.
  7.13
• Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems (2024)
  Amanda A. Howard et al.
  7.05
• Pre-training, fine-tuning, and distillation (PFD): Automatically generating machine learning force fields from universal models (2025)
  Ruoyu Wang et al.
  7.02