Michał Koziarski

arxiv.org

Towards DNA-Encoded Library Generation with GFlowNets

Michał Koziarski

Mohammed Abukalam

Vedant Shah

Louis Vaillancourt

Doris Alexandra Schuetz

Moksh J. Jain

Almer M. van der Sloot

Mathieu Bourgey

Anne Marinier

Yoshua Bengio

2024-03-04

ICLR.cc/2024/Workshop/GEM (poster)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Kerstin Klaser

Josef Dean

Cas Wognum … (see 15 more)

Maciej Sypetkowski

Ioannis Koutis

Hadrien Mary

Therence Bois

Andrew William Fitzgibbon

Blazej Banaszewski

Chad Martin

Dominic Masters

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, wh… (see more)ere datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. The Graphium library is publicly available on Github and the dataset links are available in Part 1 and Part 2.

2024-01-16

ICLR.cc/2024/Conference (poster)

Crystal-GFN: sampling materials with desirable properties and constraints

Mistal

Alexandre AGM Duval

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Cheng-Hao Liu

Santiago Miret

Pablo Lemos

Luca Thiede

Zichao Yan

Alan Aspuru-Guzik

Yoshua Bengio

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (see more)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

Crystal-GFN: sampling crystals with desirable properties and constraints

Alexandre AGM Duval

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (see more)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.

2023-10-07

ArXiv (preprint)

arxiv.org

Crystal-GFN: sampling crystals with desirable properties and constraints

Alexandre AGM Duval

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (see more)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.

2023-10-07

ArXiv (preprint)