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Foundation models for vision have transformed visual recognition with powerful pretrained representations and strong zero-shot capabilities, yet their potential for data-efficient learning remains largely untapped. Active Learning (AL) aims…
High entropy alloys are finding significant scientific interest due to their exotic microstructures and exceptional properties resulting thereof. These alloys have excellent corrosion resistance and may find broad range of applications from…
Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and complex phase formation mechanisms. Efficient exploration of this space requires a strategic approach that…
The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant…
The solution and diffusion of interstitial non-metallic solutes (INSs) like H, He, O, C, N, P, and S is common in refractory high-entropy alloys (RHEAs) and essentially controls the RHEAs properties. However, the disorder local chemical…
Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that are carefully designated to maintain their identities so…
Annotating datasets for object detection is an expensive and time-consuming endeavor. To minimize this burden, active learning (AL) techniques are employed to select the most informative samples for annotation within a constrained…
High entropy alloys (HEAs) are a new class of metals that exhibit unique mechanical performance. Among HEAs, additively manufactured eutectic high entropy alloys (AM-EHEAs) have recently emerged as candidate materials for use in extreme…
High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on…
The development of alloys that are hydrogenated and dehydrogenated quickly and actively at room temperature is a challenge for the safe and compact storage of hydrogen. In this study, a new high-entropy alloy (HEA) with AB-type…
Refractory high-entropy alloys are under consideration for applications where materials are subjected to high temperatures and levels of radiation, such as in the fusion power sector. However, at present, their scope is limited because they…
Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of…
The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is especially challenging with high-dimensional data (e.g.…
Although spectrally selective materials play a key role in existing and emerging solar thermal technologies, temperature-related degradation currently limits their use to below 700C in vacuum, and even lower temperatures in air. Here we…
Refractory metals exhibit high strength at high temperature, but often lack ductility. Multiprinciple element alloys such as high entropy alloys offer the potential to improve ductility while maintaining strength, but we don't know…
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…
Exploring the vast composition space of multi-component alloys presents a challenging task for both \textit{ab initio} (first principles) and experimental methods due to the time-consuming procedures involved. This ultimately impedes the…
Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In this paper, we propose a novel active learning framework,…
Increasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain…
We design, build, and thoroughly test a new type of actuator dubbed viscoelastic liquid cooled actuator (VLCA) for robotic applications. VLCAs excel in the following five critical axes of performance: energy efficiency, torque density,…