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The reliability with Machine Learning (ML) techniques in novel materials discovery often depend on the quality of the dataset, in addition to the relevant features used in describing the material. In this regard, the current study presents…
Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-and-error methods. However, a major challenge in current…
Theoretical studies on the experimental feasibility of hypothetical Zeolitic Imidazolate Frameworks (ZIF) have focused so far on relative energy of various polymorphs, by energy minimization at the quantum chemical level. We present here a…
Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert…
Zn(imidazolate)$_2$ metal-organic frameworks (MOFs) exhibit a remarkable degree of polymorphism. Because of their promising industrial applications, many research groups have investigated phase transitions, phase diagram and relative…
Recently, there have been significant advancements in the study of Metal-Organic Frameworks (MOFs), particularly in the discovery of glassy states in zeolitic imidazolate frameworks (ZIFs), a subset of MOFs. However, the correlation between…
By treating idealized zeolite frameworks as periodic mechanical trusses, we show that the number of flexible folding mechanisms in zeolite frameworks is strongly peaked at the minimum density end of their flexibility window. 25 of the 197…
Metal-organic frameworks (MOFs) are porous materials formed by interconnected metal atoms via organic linkers, resulting in high surface area and tuneable porosity, making them exceptional candidates for CO2 capture. However, their…
Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for materials science and condensed matter physics. In this work,…
The use of inorganic crystals technology has been widely date. Since quartz crystals for watches in the nineteenth century, and common way radio in the early twentieth century, to computer chips with new semiconductor materials. Chemical…
Crystal structures connected by continuous phase transitions are linked through mathematical relations between crystallographic groups and their subgroups. In the present study, we introduce group-subgroup machine learning (GS-ML) and show…
Harnessing the recent advance in data science and materials science, it is feasible today to build predictive models for materials properties. In this study, we employ the data of high-throughput quantum mechanics calculations based on…
How best to design and redesign high-throughput experiments for zeolite synthesis is addressed. A model that relates materials function to chemical composition of the zeolite and the structure directing agent is introduced. Using this…
Organic-inorganic hybrids may offer material properties not available from their inorganic components. However, they are typically less stable and disordered. Long-term stability study of the hybrid materials, over the anticipated lifespan…
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and…
Metal--organic frameworks (MOFs) are a family of chemically diverse materials, with applications in a wide range of fields covering engineering, physics, chemistry, biology and medicine. Research so far has focused almost entirely on…
Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular for the application of ML to small data sets often found in…
The synthesis of metal-organic frameworks (MOFs) in a monolithic morphology is a promising way to achieve the transition of this class of materials from academia to industrial applications. The sol-gel process has been widely employed to…
The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of hypothetical zeolites that could be targeted experimentally…
Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel unseen classes. First, we propose to cast the problem of ZSL as…