An integrated ML-DFT methodology enables screening of inorganic halide perovskites for photovoltaic applications and thorough characterization of their surface structures. Glazer tilts make (110) the most stable surface.
Binding affinities of metal-ligand complexes are central to a multitude of applications like drug design, chelation therapy, designing reagents for solvent extraction etc. While state-of-the-art molecular modelling approaches are usually employed to gather structural and chemical insights about the metal complexation with ligands, their computational cost and the limited ability to predict metalligand stability constants with reasonable accuracy, renders them impractical to screen large chemical spaces. In this context, leveraging vast amounts of experimental data to learn the metal-binding affinities of ligands becomes a promising alternative. Here, we develop a machine learning framework for predicting binding affinities (logK 1) of lanthanide cations with several structurally diverse molecular ligands. Six supervised machine learning algorithms-Random Forest (RF), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Kernel Ridge Regression (KRR), Multi Layered Perceptrons (MLP) and Adaptive Boosting (AdaBoost)-were trained on a dataset comprising thousands of experimental values of logK 1 and validated in an external 10-folds cross-validation procedure. This was followed by a thorough feature engineering and feature importance analysis to identify the molecular, metallic and solvent features most relevant to binding affinity prediction, along with an evaluation of performance metrics against the dimensionality of feature space. Having demonstrated the excellent predictive ability of our framework, we utilized the best performing AdaBoost model to predict the logK 1 values of lanthanide cations with nearly 71 million compounds present in the PubChem database. Our methodology opens up an opportunity for significantly accelerating screening and design of ligands for various targeted applications, from vast chemical spaces. Rare Earth Elements (REEs), that constitute the lanthanide block of the periodic table, together with Yttrium and Scandium, lie at the heart of many modern technologies in diverse fields ranging from health care to clean energy applications 1. With increasing adoption of clean and energy efficient technologies, the demand for REEs is expected to grow manifold in the coming years 2. Although conventional mining remains the primary source of global REE supply currently 3 , owing to the huge quantities of electronic waste (e-waste) generated, REE recovery from e-wastes becomes a promising secondary source of these critical elements 4. Much of the metal processing industry relies upon hydrometallurgical operations such as liquid-liquid extraction (LLE) to recover the target element 5. The success of an LLE operation depends critically on the choice of ligands that can selectively bind to one or more target metal ions and transport them into an oil phase in contact with an aqueous phase which originally contained the metal ions. Thus, successful recovery of REEs from e-wastes calls for the design of ligands with a high affinity for one or more target lanthanide ions. The binding strength of a ...
Solidification castings can exhibit a columnar or an equiaxed morphology or a combination of both. Since the relative proportions of these two components strongly influence the internal quality of cast product, the study of morphological transition from columnar to equiaxed structure (CET) becomes important. The transition also affects quality parameters like inclusion distribution in castings which has a significant bearing on the properties of cast products. In this work, a combined model for CET and inclusion distribution in continuously cast steel billets is presented. A conduction based transient thermal solidification model is employed in conjunction with Hunt's criterion for CET to predict the evolution of melt temperature, the location of transition and area-fractions of columnar and equiaxed zones across the billet cross-section. A correlation between melt temperature and equiaxed nuclei density is proposed and incorporated in the model to account for a more realistic variation of CET with melt superheat. The model is compared with available experimental data and is used to explore the effect of process parameters on CET and determine the spatial distribution of non-metallic inclusions in the solidified billet.
In this work, the columnar-to-equiaxed transition (CET) and spot segregation phenomena in continuously-cast (CC) high carbon steel billets are investigated. Casting process is simulated for a 125 mm × 125 mm billet using a macroscopic thermal model. An empirical correlation, depicting the effect of melt temperature on heterogeneous nuclei density, is incorporated in the CET model to account for a more realistic variation of CET with melt superheat. A parametric study is also performed to see the influence of casting speed on CET and explore its theoretical limit for plant operation. The model is sufficiently general and can be utilised for different caster designs and processing conditions. Additionally, an attempt is made to estimate the degree of solute segregation in small spots along the billet centreline using a simple microsegregation model. The model predictions are compared with experimental observations on CC high-carbon steel billets and the results are encouraging.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.