Anthony Yu Tung Wang

Technische Universität Berlin | TUB · Institut für Werkstoffwissenschaften und -technologien / Institute of Materials Science and Technology

Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation....

Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation....

Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation....

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 (CrabNet), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that CrabNet’s...

New featurization schemes for describing materials as composition vectors in order to predict their properties using machine learning are common in the field of Materials Informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various c...

This article on Methods/Protocols 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, validation, evaluation and comparison, popular repositories for materials data and benchm...

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, validation, evaluation and comparison, popular repositories for materials data and benchmarking datasets, m...

div>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 work sets out to make clear which featurization methods should be used across various circumstances. Our findi...

div>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 property predictions when only a chemical formula is provided. Our results show that CrabNet's performance...

Interfacial phase change memory devices based on a distinct nanoscale structure called superlattice have been shown to outperform conventional phase-change devices. This improvement has been attributed to the hetero-interfaces, which play an important role for the superior device characteristics. However, the impact of grain boundaries (GBs), usual...

The laser impulse metal bonding process is a novel interconnection method for the micro joining of metallic interconnectors on metallization of sensitive substrates. The separation of the process stage melting and joining of the interconnector minimizes the energy deposition in the bottom joining partner and reduces the weld penetration depth in th...

We present a novel design for a sensitive temperature micro-probe, situated at the tip of an atomic force microscopy cantilever. The temperature-sensing element utilizes a platinum resistance thermometer, which is well-known for its measurement reproducibility and chemical inertness. The probe is fabricated using conventional clean room techniques...