| A Machine Learning Approach That Beats Large Rubik's Cubes |
Graph Theory, Group Theory, RL |
arXiv 2025 |
Code |
| Advancing mathematics by guiding human intuition with AI |
Knot Theory, Representation Theory |
Nature 2021 |
Code |
| AI-driven research in pure mathematics and theoretical physics |
Survey |
Nature Reviews Physics 2025 |
|
| Algorithm-assisted discovery of an intrinsic order among mathematical constants |
Number Theory |
PNAS 2024 |
Code |
| AlphaEvolve: A coding agent for scientific and algorithmic discovery |
Matrix Multiplication, Analysis, Combinatorics, Discrete Geometry, LLM |
arXiv 2025 |
Unofficial Code |
| AlphaTensor: Discovering faster matrix multiplication algorithms |
Matrix Multiplication, RL |
Nature 2022 |
Code Blog |
| An algorithm for Aubert-Zelevinsky duality à la Mœglin-Waldspurger |
Representation Theory, Neural Network |
arXiv 2025 |
Code |
| Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences |
Number Theory |
ICML 2023 |
Code |
| Can Transformers Do Enumerative Geometry? |
Algebraic Geometry, Interpretability, Transformer |
ICLR 2025 |
Code |
| CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs |
Graph Theory, Group Theory, RL |
arXiv 2025 |
Code |
| Constructions in combinatorics via neural networks |
Graph Theory, RL |
arXiv 2021 |
Code |
| Data-scientific study of Kronecker coefficients |
Representation Theory, PCA |
Experimental Mathematics 2023 |
|
| Discovery of Unstable Singularities |
Analysis, PINN |
arXiv 2025 |
|
| From Euler to AI: Unifying Formulas for Mathematical Constants |
Number Theory, LLM |
arXiv 2025 |
Code |
| Generating conjectures on fundamental constants with the Ramanujan Machine |
Number Theory |
Nature 2021 |
Code |
| Generative AI for brane configurations and coamoeba |
Mathematical Physics, VAE |
Physical Review D 2025 |
|
| Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers |
Analysis, Transformer |
NeurIPS 2024 |
Code |
| Hilbert series, machine learning, and applications to physics |
Algebraic Geometry, Mathematical Physics, Neural Network |
Physics Letters B 2024 |
Code |
| Int2Int: a framework for mathematics with transformers |
Number Theory, Transformer |
arXiv 2025 |
Code |
| Interpretable Machine Learning for Kronecker Coefficients |
Representation Theory, Neural Network, Symbolic Regression, PCA, Transformer |
arXiv 2025 |
|
| Lattice-Valued Bottleneck Duality |
Combinatorics |
arXiv 2024 |
|
| Learning Euler factors of elliptic curves |
Number Theory, Transformer |
arXiv 2025 |
|
| Learning Fricke signs from Maass form Coefficients |
Number Theory, LDA |
arXiv 2025 |
|
| Machine Learning Approaches to the Shafarevich-Tate Group of Elliptic Curves |
Number Theory, Neural Network, Decision Tree |
IJDSMS 2024 |
Code |
| Machine learning assisted exploration for affine Deligne-Lusztig varieties |
Number Theory, Representation Theory |
Peking Math J 2024 |
Code |
| Machine learning BPS spectra and the gap conjecture |
Mathematical Physics, PCA |
Physical Review D 2024 |
|
| Machine learning Calabi-Yau hypersurfaces |
Mathematical Physics |
Physical Review D 2022 |
|
| Machine learning class numbers of real quadratic fields |
Number Theory, Interpretability |
IJDSMS 2023 |
|
| Machine learning for complete intersection Calabi-Yau manifolds: a methodological study |
Mathematical Physics |
Physical Review D 2021 |
|
| Machine Learning in the String Landscape |
Mathematical Physics |
JHEP 2017 |
|
| Machine learning invariants of arithmetic curves |
Number Theory, Logistic Regression, Random Forest |
Journal of Symbolic Computation 2023 |
|
| Machine Learning Kreuzer–Skarke Calabi–Yau Threefolds |
Mathematical Physics, Algebraic Geometry, Neural Network |
arXiv 2025 |
|
| Machine learning Kronecker coefficients |
Representation Theory, CNN, Decision Tree |
IJDSMS 2023 |
|
| Machine learning line bundle cohomologies of hypersurfaces in toric varieties |
Algebraic Geometry |
Physics Letters B 2019 |
|
| Machine Learning Number Fields |
Number Theory, Neural Network, Random Forest |
MCGD 2022 |
|
| Machine learning of Calabi-Yau volumes |
Mathematical Physics, CNN, Linear Regression |
Physical Review D 2017 |
|
| Machine learning Sasakian G2 topology on contact Calabi-Yau 7-manifolds |
Mathematical Physics, Neural Network |
Physics Letters B 2024 |
Code |
| Machine Learning the vanishing order of rational L-functions |
Number Theory, LDA, Neural Network |
arXiv 2025 |
|
| Machine-learning dessins d'enfants: explorations via modular and Seiberg–Witten curves |
Algebraic Geometry, Mathematical Physics |
Journal of Physics A 2021 |
|
| Machine-learning Sato-Tate conjecture |
Number Theory |
Journal of Symbolic Computation 2022 |
|
| Machines Learn Number Fields, But How? The Case of Galois Groups |
Number Theory, Logistic Regression, Decision Tree, Interpretability |
arXiv 2025 |
Code |
| Mathematical discoveries from program search with large language models |
Combinatorics, LLM |
Nature 2024 |
Code |
| Mathematical discovery in the age of artificial intelligence |
Survey |
Nature Physics 2025 |
|
| Murmurations of Elliptic Curves |
Number Theory, PCA |
Experimental Mathematics 2024 |
Quanta |
| Neural network approximations for Calabi-Yau metrics |
Mathematical Physics, Neural Network |
JHEP 2022 |
|
| New Calabi–Yau manifolds from genetic algorithms |
Algebraic Geometry, Mathematical Physics, Genetic Algorithm |
Physics Letters B 2024 |
|
| PatternBoost: Constructions in Mathematics with a Little Help from AI |
Discrete Geometry, Combinatorics, Transformer, RL |
arXiv 2024 |
Code |
| Predicting root numbers with neural networks |
Number Theory, RNN, CNN |
IJDSMS 2024 |
|
| Ranks of elliptic curves and deep neural networks |
Number Theory, CNN |
Research in Number Theory 2023 |
Code |
| Rigor with Machine Learning from Field Theory to the Poincaré Conjecture |
Geometry, Mathematical Physics |
Nature Reviews Physics 2024 |
|
| Searching for ribbons with machine learning |
Geometry, Bayesian Optimization, RL, Neural Network |
Machine Learning Science and Technology 2025 |
Code |
| Studying number theory with deep learning: a case study with the Möbius and squarefree indicator functions |
Number Theory, Transformer |
arXiv 2025 |
Code |
| Unsupervised Discovery of Formulas for Mathematical Constants |
Number Theory |
NeurIPS 2024 |
Code |
| What makes math problems hard for reinforcement learning: a case study |
Group Theory, RL, Transformer |
arXiv 2024 |
Code |
