mlsanityData to 14 Oct 2026

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

arXiv 2307.08423 ↗First posted 17 Jul 2023cs.LG physics.comp-phcited by 121

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Abstract (arXiv)

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.

Key facts. Cited by 121 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 14; 2024: 46; 2025: 43; 2026: 18.

121 papers.

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  1. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  2. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  3. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 68
  4. 2024Neural Operators with Localized Integral and Differential Kernelscited by 62
  5. 2025AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformerscited by 53
  6. 2024Sequential Controlled Langevin Diffusionscited by 48
  7. 2023Does Invariant Graph Learning via Environment Augmentation Learn Invariance?cited by 42
  8. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluationcited by 42
  9. 2024Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Productscited by 36
  10. 2024On the Scalability of GNNs for Molecular Graphscited by 36
  11. 2024Complete and Efficient Graph Transformers for Crystal Material Property Predictioncited by 34
  12. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 33
38 more
  1. 2024Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learningcited by 32
  2. 2025DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectracited by 32
  3. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  4. 2024Recent Advances on Machine Learning for Computational Fluid Dynamics: A Surveycited by 28
  5. 2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operatorscited by 27
  6. 2023Uncovering Neural Scaling Laws in Molecular Representation Learningcited by 23
  7. 2025Trust Region Constrained Measure Transport in Path Space for Stochastic Optimal Control and Inferencecited by 23
  8. 2024L+M-24: Building a Dataset for Language + Molecules @ ACL 2024cited by 21
  9. 2024ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictorcited by 21
  10. 2024Compositional Generative Inverse Designcited by 20
  11. 2024Equivariance via Minimal Frame Averaging for More Symmetries and Efficiencycited by 19
  12. 2025Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learningcited by 19
  13. 2025Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matchingcited by 19
  14. 2025Operator Learning: A Statistical Perspectivecited by 17
  15. 2023Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelcited by 16
  16. 2025A Periodic Bayesian Flow for Material Generationcited by 16
  17. 2024Accelerating PDE Data Generation via Differential Operator Action in Solution Spacecited by 15
  18. 2024Crystalformer: Infinitely Connected Attention for Periodic Structure Encodingcited by 15
  19. 2025Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptationcited by 15
  20. 2024Knowledge-guided Machine Learning: Current Trends and Future Prospectscited by 14
  21. 2024SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equationscited by 14
  22. 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learningcited by 13
  23. 2025Towards Mechanistic Interpretability of Graph Transformers via Attention Graphscited by 13
  24. 2024Solving Poisson Equations using Neural Walk-on-Spherescited by 11
  25. 2025Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physicscited by 11
  26. 2023Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channelcited by 10
  27. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  28. 2024NeuralDEM -- Real-time Simulation of Industrial Particulate Flowscited by 10
  29. 2025Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientistscited by 10
  30. 2025Generative AI in Transportation Planning: A Surveycited by 8
  31. 2025mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Moleculescited by 8
  32. 2026Innovator-VL: A Multimodal Large Language Model for Scientific Discoverycited by 8
  33. 2023Towards out-of-distribution generalizable predictions of chemical kinetics propertiescited by 7
  34. 2023Supercharging Graph Transformers with Advective Diffusioncited by 7
  35. 2023Towards Autonomous Hypothesis Verification via Language Models with Minimal Guidancecited by 7
  36. 2025GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulationscited by 7
  37. 2023A Versatile Graph Learning Approach through LLM-based Agentcited by 6
  38. 2024Navigating Chemical Space with Latent Flowscited by 6

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