相关论文: Machine-Learning-Optimized Perovskite Nanoplatelet…
Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable…
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…
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase…
Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational…
The chalcogenide perovskite material BaZrS$_{3}$ is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's amorphous precursor as well as…
Copper nanoparticles (Cu NPs) have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with the time- and resource-intensive nature of experimental optimization, poses…
The properties of silver nanoparticles (AgNPs) are affected by various parameters, making optimisation of their synthesis a laborious task. This optimisation is facilitated in this work by concurrent use of a T-junction microfluidic system…
The anisotropy of perovskite nanoplatelets (PeNPLs) opens up many opportunities in optoelectronics, including enabling the emission of linearly polarized light. But the limited stability of PeNPLs is a pressing challenge, especially for…
The optimization of the electrodes manufacturing process constitutes one of the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because LIB electrode manufacturing is a…
This chapter is a preprint from our book by , focusing on leveraging machine learning (ML) in chemical and polyolefin manufacturing optimization. It's crafted for both novices and seasoned professionals keen on the latest ML applications in…
Cesium based halide perovskites, such as CsPbI3 and CsSnI3, have emerged as exceptional candidates for next generation photovoltaic and optoelectronic technologies, but their practical application is limited by temperature dependent phase…
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not…
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become…
Due to their inherent variabilities,nanomaterial-based sensors are challenging to translate into real-world applications,where reliability/reproducibility is key.Recently we showed Bayesian inference can be employed on engineered…
Laser material processing has emerged as a versatile and indispensable tool in various industries, including manufacturing, healthcare, and materials science. However, the interaction of a lasers with surfaces is highly dependent on a large…
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model…
When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored. For example, the materials' composition of elements will affect properties such as strength or…
Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments…
Halide perovskites exhibit unpredictable properties in response to environmental stressors, due to several composition-dependent degradation mechanisms. In this work, we apply data visualization and machine learning (ML) techniques to…
Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can…