An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

Aksöz, Zeynep; Preisinger, Clemens

doi:10.1007/978-3-030-29829-6_2

Cited by 10 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, the application of artificial intelligence and machine learning (AI/ML) to molecular simulations has increased in popularity recently due to its ability to efficiently model complex functions in data-rich domains. There have been a number of demonstrations from domain scientists for specific challenges such as reaction network elucidation, − thermochemistry prediction, − structure optimization, − accelerating individual calculations, − and integration with characterization (see recent reviews for a more thorough discussion ,− ). Most of these tasks are variations on the same fundamental problem: modeling heterogeneous catalysis.…”

Section: Introductionmentioning

confidence: 99%

Open Catalyst 2020 (OC20) Dataset and Community Challenges

et al. 2021

View full text Add to dashboard Cite

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuel synthesis, long-term energy storage, and renewable fertilizer production. Despite considerable effort by the catalysis community to apply machine learning models to the computational catalyst discovery process, it remains an open challenge to build models that can generalize across both elemental compositions of surfaces and adsorbate identity/configurations, perhaps because datasets have been smaller in catalysis than in related fields. To address this, we developed the OC20 dataset, consisting of 1,281,040 density functional theory (DFT) relaxations (∼264,890,000 single-point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We supplemented this dataset with randomly perturbed structures, short timescale molecular dynamics, and electronic structure analyses. The dataset comprises three central tasks indicative of day-to-day catalyst modeling and comes with predefined train/validation/test splits to facilitate direct comparisons with future model development efforts. We applied three state-of-the-art graph neural network models (CGCNN, SchNet, and DimeNet++) to each of these tasks as baseline demonstrations for the community to build on. In almost every task, no upper limit on model size was identified, suggesting that even larger models are likely to improve on initial results. The dataset and baseline models are both provided as open resources as well as a public leader board to encourage community contributions to solve these important tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

Open Catalyst 2020 (OC20) Dataset and Community Challenges

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Offering one substantial difference from conventional iterative processes for searching for potential solutions. Because solutions emerge from local rules, exploring new outcomes relies on the generation of new global results [11].…”

Section: Artificial Intelligence For Materials Predictionmentioning

confidence: 99%

Regression-Based Inductive Reconstruction of Shell Auxetic Structures

Vivanco

Ojeda

Yuan

2023

Computational Design and Robotic Fabrication

View full text Add to dashboard Cite

This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.

show abstract

“…Furthermore, neural networks can be used to solve the computational problem in the field of structural optimization (Aksöz and Preisinger 2019). The authors use an artificial neural network (ANN) to learn the stress conditions and coping methods in finite element analysis, then use the generated sample data to train the neural network, and finally apply the trained neural network to generate structure solutions based on the stress conditions given by the user.…”

Section: Machine Learningmentioning

confidence: 99%

Generating and Optimizing a Funicular Arch Floor Structure

Zheng,

Bernier-Lavigne,

Wang

et al. 2020

Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA)

View full text Add to dashboard Cite

An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

Cited by 10 publications

References 8 publications

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Regression-Based Inductive Reconstruction of Shell Auxetic Structures

Generating and Optimizing a Funicular Arch Floor Structure

Contact Info

Product

Resources

About