Aditi Krishnapriyan
Assistant Professor
Develops machine learning methods for molecular and materials modeling and scientific simulation.
Aditi Krishnapriyan's lab at UC Berkeley works on proteins, drugs & materials, physics, climate & scientific computing and physics-based simulation & animation. 28 papers since 2023, 1 at top venues, mostly Journal of Chemical Information and Modeling and Nature Machine Intelligence. 2 master's students. 7 alumni, most often to Google and NVIDIA. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 4%
- PhD students
- 0
- MS students
- 2
- alumni listed
- 7
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 1 · collecting
- 6 papers2025 · T1 0
- 9 papers2024 · T1 0
- 4 papers2023 · T1 0
T1 venuesOtherVenues: Journal of Chemical Information and Modeling, Nature Machine Intelligence
Research focuspaper titles that use each topic's words
- Physics-based simulation & animation8 papers
- Physics, climate & scientific computing3 papers
- Proteins, drugs & materials2 papers
- molecular1 paper
- materials1 paper
Paper list
From the lab homepage.
20269 papers
- International Conference on Machine Learning (ICML)A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
- Discover ChemistryBenchmarking Machine-Learned Potentials for Water-Splitting Catalysts: Validation on Pt and IrO₂ Surfaces Using OC20 and OMat24
- Neural Information Processing Systems (NeurIPS)Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
- Nature Machine Intelligence (T1)Flow matching for generative modelling in bioinformatics and computational biology
- International Conference on Machine Learning (ICML)From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
- Journal of Chemical Information and ModelingGeneral Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design
- International Conference on Machine Learning (ICML)Parallel Stochastic Gradient-Based Planning for World Models
- Transactions on Machine Learning Research (TMLR)PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
- Digital DiscoveryUnderstanding and Mitigating Distribution Shifts in Universal Machine Learning Interatomic Potentials
20256 papers
- International Conference on Machine Learning (ICML)Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
- Neural Information Processing Systems (NeurIPS)EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
- Nature Reviews ChemistryFoundation Models for Atomistic Simulation of Chemistry and Materials
- Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, Spotlight (top 3%)MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform
- Transactions on Machine Learning Research (TMLR)Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
- International Conference on Learning Representations (ICLR)Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
20249 papers
- Journal of Chemical Information and ModelingCoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation
- International Conference on Learning Representations (ICLR), Spotlight (top 3%)Enabling efficient equivariant operations in the Fourier basis via Gaunt Tensor Products
- International Conference on Artificial Intelligence and Statistics (AISTATS)Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
- Chemical ScienceInvestigating the Behavior of Diffusion Models for Accelerating Electronic Structure Theory Calculations
- International Conference on Learning Representations (ICLR)Neural Spectral Methods: Self-supervised learning in the spectral domain
- Electric Power Systems ResearchPhysics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
- International Conference on Learning Representations (ICLR)Scaling physics-informed hard constraints with mixture-of-experts
- Neural Information Processing Systems (NeurIPS)The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
- Computational GeometryTopological regularization via persistence-sensitive optimization
20234 papers
- ACS Central ScienceAn ecosystem for digital reticular chemistry
- Nature Computational ScienceChemical reaction networks and opportunities for machine learning
- Communications PhysicsLearning continuous models for continuous physics
- International Conference on Learning Representations (ICLR)Learning differentiable solvers for systems with hard constraints
Where alumni went7 listed, 67% to industry
Industry 4Academia 2
Most often to
- NVIDIA
Also to
- Southern Institute of Science and Technology
- University of Maryland
Apply to UC BerkeleyFall 2027
UC Berkeley EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS (non-English-country degrees)
- Fee
- See program page
Confirmed on the program page.
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- The lab homepage
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