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Chiral indices determine important properties of carbon nanotubes (CNTs). Unfortunately, their determination from high-resolution transmission electron microscopy (HRTEM) images, the most accurate method for assigning chirality, is a…

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles…

Convolutional Neural Networks (CNNs) have become common in many fields including computer vision, speech recognition, and natural language processing. Although CNN hardware accelerators are already included as part of many SoC…

The importance of haptic in-sensor computing devices has been increasing. In this study, we successfully fabricated a haptic sensor with a hierarchical structure via the sacrificial template method, using carbon…

The performance of carbon nanotube (CNT) cables, a contender for copper-wire replacement, is tied to its metallic and semi-conducting-like conductivity responses with temperature; the origin of the semi-conducting-like response however is…

The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches…

In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT…

Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging…

Even though thermodynamic energy-based crystal structure prediction (CSP) has revolutionized materials discovery, the energy-driven CSP approaches often struggle to identify experimentally realizable metastable materials synthesized through…

Single-walled carbon nanotubes (SWNTs) are potential materials for future nanoelectronics. Since the electronic and optical properties of SWNTs strongly depend on tube diameter and chirality, obtaining SWNTs with narrow (n,m) chirality…

Self-locking structures are often studied in macroscopic energy absorbers, but the concept of self-locking can also be effectively applied at the nanoscale. In particular, we can engineer self-locking mechanisms at the molecular level…

Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the…

Coiled Carbon Nanotubes (CCNTs) are increasingly set to become a vital factor in the new generation of nanodevices and energy-absorbing materials due to their outstanding properties. In the following work, the multi-objective optimization…

The electronic properties of carbon nanotubes are governed by their chirality, specified by the integer indices (n,m). While chirality-controlled synthesis has achieved notable successes, theoretical understanding remains predominantly…

Single-atom catalysts (SACs) have emerged as frontiers for catalyzing chemical reactions, yet the diverse combinations of active elements and support materials, the nature of coordination environments, elude traditional methodologies in…

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally…

By sequential feeding of catalyst materials, it is revealed that the active growth sites are at the bottom of the carbon nanotubes (CNTs), and that catalyst particles are constantly encapsulated into nanotubes from the bottom. This gives a…

Understanding how nanocluster cocatalysts modify the electronic structure of III-nitride surfaces is central to the rational design of efficient photocatalytic interfaces. Here, we establish design principles for nanocluster cocatalysts on…

High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly…

Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many…