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With the proliferation of increasingly complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's…
Colloidal approaches allow for the synthesis of Au nanoclusters (NCs) with atomic precision and sizes ranging from a few to hundreds of atoms. In most of the cases, these processes involve a common strategy of thiol etching of initially…
In computer chip manufacturing, the study of etch patterns on silicon wafers, or metrology, occurs on the nano-scale and is therefore subject to large variation from small, yet significant, perturbations in the manufacturing environment. An…
We demonstrate patterning of metal surfaces based on lift-off of perovskite nanocrystals that enables the fabrication of nanometer-size features without the use of resist-based nanolithography. The perovskite nanocrystals act as templates…
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either…
Discrete diffusion models (DDMs) have shown powerful generation ability for discrete data modalities like text and molecules. However, their practical application is hindered by inefficient sampling, requiring a large number of sampling…
Discovering functional crystalline materials through computational methods remains a formidable challenge in materials science. Here, we introduce VQCrystal, an innovative deep learning framework that leverages discrete latent…
Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models…
Organic capping ligands can selectively bind to crystal facets to modulate growth kinetics and are important in chemical synthesis of inorganic nanocrystals. Using the capping ligands for shape-controlled growth of colloidal crystals is…
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used to predict several properties in solid-state chemistry, in…
By exploiting the exquisite selectivity of DNA hybridization, DNA-Coated Colloids (DNACCs) can be made to self-assemble in a wide variety of structures. The beauty of this system stems largely from its exceptional versatility and from the…
We present a dynamical model of crystal growth, in which it is possible to reliably achieve asymmetric products, beginning from symmetric initial conditions and growing within an isotropic environment. The asymmetric growth is the result of…
Crystallization during droplet evaporation gives rise to complex, self-organized structures, yet the mechanisms underlying the emergence of ordered functional phases remain poorly understood. In this study, we present a comprehensive,…
Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science with accelerated, high-efficiency discoveries in design, synthesis, manufacturing, characterization and application of novel…
Large-scale or high-resolution geologic models usually comprise a huge number of grid blocks, which can be computationally demanding and time-consuming to solve with numerical simulators. Therefore, it is advantageous to upscale geologic…
Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability…
A kinetic Monte Carlo approach is applied to study physical mechanisms responsible for the breakup of nanowires with the diamond cubic crystal structure into a chain of nanoparticles discovered in preceding experiments on Silicon nanowires.…
Properties of crystalline materials are closely linked to microstructure arising from the spatial arrangement, orientation, and phase of nanocrystals. Rapid characterization of crystalline microstructure can accelerate the identification of…
The scientific computation of large deformations in elastic-plastic solids is crucial in various manufacturing applications. Traditional numerical methods exhibit several inherent limitations, prompting Deep Learning (DL) as a promising…
To efficiently align the creation of defect center with photonics structure in nanoscale precision is one of the outstanding challenges for realizing high-performance photonic devices and the application in quantum technology such as…