Alex Hernandez-Garcia

Sun Sun

Alain Tchagang

Jiang Su

Samuel Huberman

Hongyu Guo

Homin Shin

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (voir plus) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of

2025-02-20

ArXiv (prépublication)

OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes

F'elix Therrien

Jamal Abou Haibeh

Divya Sharma

Rhiannon Hendley

Alex Hern'andez-Garc'ia

Sun Sun

Alain Tchagang

Jiang Su

Samuel Huberman

Hongyu Guo

Homin Shin

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (voir plus) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a domain-expert-curated database of

2025-02-20

ArXiv (prépublication)

OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes

Félix Therrien

Jamal Abou Haibeh

Divya Sharma

Rhiannon Hendley

Sun Sun

Alain Tchagang

Jiang Su

Samuel Huberman

Hongyu Guo

Homin Shin

Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher… (voir plus) theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a database of

2025-02-20

ArXiv (prépublication)

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Alex Hern'andez-Garc'ia

Jinkyoo Park

Designing biological sequences with desired properties is a significant challenge due to the combinatorially vast search space and the high … (voir plus)cost of evaluating each candidate sequence. To address these challenges, reinforcement learning (RL) methods, such as GFlowNets, utilize proxy models for rapid reward evaluation and annotated data for policy training. Although these approaches have shown promise in generating diverse and novel sequences, the limited training data relative to the vast search space often leads to the misspecification of proxy for out-of-distribution inputs. We introduce

2024-10-06

ArXiv (prépublication)

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

David Rolnick

Crystal-GFN: sampling materials with desirable properties and constraints

Mistal

Alexandre AGM Duval

2023-10-27

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

Multi-Fidelity Active Learning with GFlowNets

Nikita Saxena

Moksh J. Jain

Cheng-Hao Liu

In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanw… (voir plus)hile, the progress in machine learning has turned it into a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, high-dimensional spaces, where querying a high fidelity, black-box objective function is very expensive. Progress in machine learning methods that can efficiently tackle such problems would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose the use of GFlowNets for multi-fidelity active learning, where multiple approximations of the black-box function are available at lower fidelity and cost. GFlowNets are recently proposed methods for amortised probabilistic inference that have proven efficient for exploring large, high-dimensional spaces and can hence be practical in the multi-fidelity setting too. Here, we describe our algorithm for multi-fidelity active learning with GFlowNets and evaluate its performance in both well-studied synthetic tasks and practically relevant applications of molecular discovery. Our results show that multi-fidelity active learning with GFlowNets can efficiently leverage the availability of multiple oracles with different costs and fidelities to accelerate scientific discovery and engineering design.

2023-10-27

NeurIPS.cc/2023/Workshop/ReALML (published)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

David Rolnick

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (voir plus)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-27

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

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

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (voir plus)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 … (voir plus)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 (prépublication)

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 … (voir plus)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 (prépublication)