Ryan Murdock
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View article: Generative adversarial networks and diffusion models in material discovery
Generative adversarial networks and diffusion models in material discovery Open
Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery.
View article: To err is human‐ to understand error‐processing is divine: Contributions of working memory and anxiety to error‐related brain and pupil responses
To err is human‐ to understand error‐processing is divine: Contributions of working memory and anxiety to error‐related brain and pupil responses Open
Both anxiety and working memory capacity appear to predict increased (more negative) error‐related negativity (ERN) amplitudes, despite being inversely related to one another. Until the interactive effects of these variables on the ERN are…
View article: Generative adversarial networks and diffusion models in material discovery
Generative adversarial networks and diffusion models in material discovery Open
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structur…
View article: Unintended Consequences of Trying to Help: Augmented Target Recognition Cues Bias Perception
Unintended Consequences of Trying to Help: Augmented Target Recognition Cues Bias Perception Open
Rapid advances in computer vision mean that artificial intelligence-aided systems may be able to provide helpful suggestions for a variety of complex visual tasks. One example of this approach is Augmented Target Recognition (ATR) where So…
View article: Generative adversarial networks and diffusion models in material discovery
Generative adversarial networks and diffusion models in material discovery Open
The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structur…
View article: A Fast Text-Driven Approach for Generating Artistic Content
A Fast Text-Driven Approach for Generating Artistic Content Open
In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or …
View article: A Fast Text-Driven Approach for Generating Artistic Content
A Fast Text-Driven Approach for Generating Artistic Content Open
In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or …
View article: Increasing target template precision decreases distractor recognition in hybrid search
Increasing target template precision decreases distractor recognition in hybrid search Open
Suppose you were at the grocery store with a memorized list of products to look for. After finding all of your groceries, would you remember any of the products you saw that were not on the list? Could the length of your grocery list and t…
View article: Compositionally restricted attention-based network for materials property predictions
Compositionally restricted attention-based network for materials property predictions Open
In this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (), explores the area of structure-agnostic m…
View article: Compositionally-Restricted Attention-Based Network for Materials Property Prediction
Compositionally-Restricted Attention-Based Network for Materials Property Prediction Open
In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials p…
View article: Compositionally-Restricted Attention-Based Network for Materials Property Prediction
Compositionally-Restricted Attention-Based Network for Materials Property Prediction Open
In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials p…
View article: Trained network weights for the paper "Compositionally restricted attention-based network for materials property predictions (CrabNet)"
Trained network weights for the paper "Compositionally restricted attention-based network for materials property predictions (CrabNet)" Open
Trained network weights for the paper "Compositionally restricted attention-based network for materials property predictions (CrabNet)". Download these weights for use with the code in the repository https://github.com/anthony-wang/CrabNet…
View article: Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Open
This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature en…
View article: Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices
Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices Open
This Editorial is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model trainin…
View article: Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices
Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices Open
This Editorial is intended for materials scientists interested in performing machine learning-centered research.We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training…
View article: Is Domain Knowledge Necessary for Machine Learning Materials Properties?
Is Domain Knowledge Necessary for Machine Learning Materials Properties? Open
New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This wor…
View article: Is Domain Knowledge Necessary for Machine Learning Materials Properties?
Is Domain Knowledge Necessary for Machine Learning Materials Properties? Open
New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This wor…
View article: Compositionally-Restricted Attention-Based Network for Materials Property Prediction
Compositionally-Restricted Attention-Based Network for Materials Property Prediction Open
In this paper, we evaluate an attention-based neural network architecture for the prediction of inorganic materials properties given access to nothing but each materials' chemical composition. We demonstrate that this novel application of …
View article: Compositionally-Restricted Attention-Based Network for Materials Property Prediction
Compositionally-Restricted Attention-Based Network for Materials Property Prediction Open
In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials p…
View article: Can Machine Learning Find Extraordinary Materials?
Can Machine Learning Find Extraordinary Materials? Open
One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is…
View article: Can Machine Learning Find Extraordinary Materials?
Can Machine Learning Find Extraordinary Materials? Open
One of the most common criticisms of machine learning is an assumed inability for models to extrapolate, i.e. to identify extraordinary materials with properties beyond those present in the training data set. To investigate whether this is…