AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion

Adeesh Kolluru, John R. Kitchin
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:25042-25057, 2024.

Abstract

Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate onto the slab followed by an optimization process. Prior methodologies have relied on heuristics, problem-specific intuitions, or brute-force approaches to guide adsorbate placement. In this work, we propose a novel framework for adsorbate placement using denoising diffusion. The model is designed to predict the optimal adsorbate site and orientation corresponding to the lowest energy configuration. Further, we have an end-to-end evaluation framework where diffusion-predicted adslab configuration is optimized with a pretrained machine learning force field and finally evaluated with Density Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pretraining, model architectures, and conduct extensive experiments to underscore the significance of this approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-kolluru24a, title = {{A}dsorb{D}iff: Adsorbate Placement via Conditional Denoising Diffusion}, author = {Kolluru, Adeesh and Kitchin, John R.}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {25042--25057}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/kolluru24a/kolluru24a.pdf}, url = {https://proceedings.mlr.press/v235/kolluru24a.html}, abstract = {Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate onto the slab followed by an optimization process. Prior methodologies have relied on heuristics, problem-specific intuitions, or brute-force approaches to guide adsorbate placement. In this work, we propose a novel framework for adsorbate placement using denoising diffusion. The model is designed to predict the optimal adsorbate site and orientation corresponding to the lowest energy configuration. Further, we have an end-to-end evaluation framework where diffusion-predicted adslab configuration is optimized with a pretrained machine learning force field and finally evaluated with Density Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pretraining, model architectures, and conduct extensive experiments to underscore the significance of this approach.} }
Endnote
%0 Conference Paper %T AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion %A Adeesh Kolluru %A John R. Kitchin %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-kolluru24a %I PMLR %P 25042--25057 %U https://proceedings.mlr.press/v235/kolluru24a.html %V 235 %X Determining the optimal configuration of adsorbates on a slab (adslab) is pivotal in the exploration of novel catalysts across diverse applications. Traditionally, the quest for the lowest energy adslab configuration involves placing the adsorbate onto the slab followed by an optimization process. Prior methodologies have relied on heuristics, problem-specific intuitions, or brute-force approaches to guide adsorbate placement. In this work, we propose a novel framework for adsorbate placement using denoising diffusion. The model is designed to predict the optimal adsorbate site and orientation corresponding to the lowest energy configuration. Further, we have an end-to-end evaluation framework where diffusion-predicted adslab configuration is optimized with a pretrained machine learning force field and finally evaluated with Density Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pretraining, model architectures, and conduct extensive experiments to underscore the significance of this approach.
APA
Kolluru, A. & Kitchin, J.R.. (2024). AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:25042-25057 Available from https://proceedings.mlr.press/v235/kolluru24a.html.

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