Publications

This paper introduces a method to compute accurate energy Hessians using pretrained GNNs, enabling faster free energy corrections and transition state searches.

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Authors: Brook Wander, Joseph Musielewicz, Raffaele Cheula, John R. Kitchin

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This paper builds the largest validated dataset of gold nanoparticle syntheses using a hybrid LLM-based approach, uncovering how shape depends on precursor and protocol choices.

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Authors: Sanghoon LeeKevin CruseSamuel P. GleasonA. Paul AlivisatosGerbrand Ceder, Anubhav Jain

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This paper presents a physics-based tool that extracts size and shape distributions of gold nanorods directly from UV-Vis spectra, enabling automated, high-throughput synthesis analysis and predictive modeling without relying on electron microscopy.

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Authors: Samuel P. Gleason, Jakob C. Dahl, Mahmoud Elzouka, Xingzhi Wang, Dana O. Byrne, Hannah Cho, Mumtaz Gababa, Ravi S. Prasher, Sean Lubner, Emory M. Chan, A. Paul Alivisatos

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This paper presents CuXASNet, a neural network trained on FEFF9 simulations that rapidly predicts Cu L-edge X-ray absorption spectra from atomic structures with near-DFT accuracy, enabling high-throughput screening and experimental analysis across diverse materials.

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Authors: Samuel P. Gleason, Matthew R. Carbone, Deyu Lu, Jim Ciston

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This paper benchmarks uncertainty quantification methods for GNN-predicted relaxed energies and shows that latent space distances—especially when engineered for rotational invariance—offer the most reliable and efficient uncertainty estimates.

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Authors: Joseph Musielewicz, Janice Lan, Matt Uyttendaele, John R. Kitchin

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This paper presents a random forest-based model trained on simulated dynamical electron diffraction patterns to predict crystal systems, space groups, and lattice constants, enabling fast, uncertainty-aware structure identification from 2D data in both simulated and experimental 4D-STEM settings.

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Authors: Samuel P. Gleason, Alexander Rakowski, Stephanie M. Ribet, Steven E. Zeltmann, Benjamin H. Savitzky, Matthew Henderson, Jim Ciston, Colin Ophus

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This paper presents a random forest model trained on simulated Cu L-edge XAS spectra to predict oxidation states directly from XAS and EELS data, enabling accurate, high-throughput analysis of mixed-valence copper materials across experimental and in situ settings.

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Authors: Samuel P. Gleason, Deyu Lu, Jim Ciston

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This paper offers a structured and in-depth guide to the field of Geometric Graph Neural Networks (GNNs) for 3D atomic systems, introducing a taxonomy of invariant, equivariant (Cartesian and spherical), and unconstrained models to help newcomers and practitioners better understand and navigate the landscape of geometric GNN architectures, applications, and future directions.

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Authors: Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein

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This paper introduces PhAST, a physics-aware, scalable, and task-specific GNN framework that significantly boosts accuracy and efficiency for catalyst discovery on OC20, enabling up to 40× speedups and CPU-based training.

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Authors: Alexandre DuvalVictor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hérnandez-Garcia, David Rolnick

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This paper presents a new way to generate stable bulk crystal structures using generative AI, building them step-by-step under chemical and physical constraints.

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Authors: Mila AI4Science, Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, Victor Schmidt

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This paper presents a fine-tuned GPT-3 model that extracts complete, structured seed-mediated gold nanorod synthesis procedures from unstructured literature, enabling the creation of a high-quality, reusable dataset for downstream synthesis modeling and analysis.

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Authors: Nicholas WalkerSanghoon LeeJohn DagdelenKevin CruseSamuel GleasonAlexander DunnGerbrand CederA. Paul AlivisatosKristin A. Persson, Anubhav Jain

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This paper presents GFlowNets as a general framework for AI-driven scientific discovery, enabling diverse, uncertainty-aware hypothesis generation, experimental design, and causal inference in domains with large, complex search spaces.

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Authors: Moksh Jain, Tristan Deleu, Jason Hartford, Cheng-Hao Liu, Alex Hernandez-Garcia, Yoshua Bengio

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This paper presents a high-throughput DFT workflow for modeling amorphous material surface reactions, using site clustering and automated NEB generation to predict etching barriers in systems like a-Si and a-C with minimal computation.

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Authors: Martin Siron, Nita Chandrasekhar, Kristin A. Persson

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This paper introduces FAENet, a lightweight and highly expressive GNN that achieves symmetry preservation through stochastic frame averaging rather than architectural constraints, enabling state-of-the-art performance and scalability in 3D materials modeling.

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Authors: Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick

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This paper introduces GFlowNets as a framework for sampling complex structured objects proportionally to reward, enabling diverse generation in tasks like molecule design and probabilistic inference.

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Authors: Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

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This paper presents a large-scale DFT study of tellurium-containing semiconductors, uncovering chemisorption trends and unique scaling relationships that suggest their potential as selective photocatalysts for CO₂ reduction.

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Authors: Martin Siron, Oxana Andriuc, Kristin A. Persson

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This paper presents an automated DFT workflow for systematically evaluating adsorption on semiconductor surfaces, demonstrated on zinc telluride to enable high-throughput screening for photocatalytic CO₂ reduction.

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Authors: Oxana Andriuc, Martin Siron, Joseph H. Montoya, Matthew Horton, Kristin A. Persson

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