Related papers: Data Mining for better material synthesis: the cas…
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth…
We demonstrate a data mining approach to discover and develop new organic nonlinear optical crystals that produce intense pulses of terahertz radiation. We mine the Cambridge Structural Database for non-centrosymmetric materials and use…
The discovery and optimization of materials for specific applications is hampered by the practically infinite number of possible elemental combinations and associated properties, also known as the `combinatorial explosion'. By nature of the…
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the…
Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging…
We report a deep generative model for regression tasks in materials informatics. The model is introduced as a component of a data imputer, and predicts more than 20 diverse experimental properties of organic molecules. The imputer is…
The discovery and optimization of high-performance materials is the basis for advancing energy conversion technologies. To understand composition-property relationships, all available data sources should be leveraged: experimental results,…
Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A…
Synthesis of thin films has traditionally relied upon slow, sequential processes carried out with substantial human intervention, frequently utilizing a mix of experience and serendipity to optimize material structure and properties. With…
Identifying optimal synthesis conditions for metal-organic frameworks (MOFs) is a major challenge that can serve as a bottleneck for new materials discovery and development. Trial-and-error approach that relies on a chemist's intuition and…
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods…
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from…
The combinatorial approach to all oxide material and device research is based on the synthesis of hundreds of related materials in a single experiment. This approach requires the development of new tools to rapidly characterize these…
Pulsed-laser deposition (PLD) is one of the most promising techniques for the formation of complex-oxide heterostructures, superlattices, and well-controlled interfaces. The first part of this paper presents a review of several useful…
A novel continuous-compositional spread technique based on the non-uniformity of the deposition rate typically observed in Pulsed Laser Deposition (PLD) is introduced. The approach uses the spatial variations in the deposition-rate…
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and…
Data mining is a recognized predictive tool in a variety of areas ranging from bioinformatics and drug design to crystal structure prediction. In the present study, an electronic structure implementation has been combined with structural…
Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and…
In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery,…
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…