scholarly journals High-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches

2021 ◽  
Author(s):  
Kate Higgins ◽  
Maxim Ziatdinov ◽  
Sergei V. Kalinin ◽  
Mahshid Ahmadi
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Metal Halide ◽  
Learning Approaches ◽  
Halide Perovskites ◽  
The Stability ◽  
High Throughput Study

Machine learning for high-throughput experimental exploration of metal halide perovskites

Joule ◽  
2021 ◽  
Author(s):  
Mahshid Ahmadi ◽  
Maxim Ziatdinov ◽  
Yuanyuan Zhou ◽  
Eric A. Lass ◽  
Sergei V. Kalinin
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Metal Halide ◽  
Halide Perovskites

Diagnostics of the 3D printing process on a CNC machine using machine learning approaches

2021 ◽  
pp. 55-59
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
P.V. Kolchin ◽  
A.V. Kiselev
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
3D Printing ◽  
Electric Arc ◽  
Cyber Physical System ◽  
Learning Approaches ◽  
Cnc Machine ◽  
Printing Process ◽  
Optimization Of Process Parameters ◽  
The Stability

Diagnostics and optimization of the dynamics of an electric arc during 3D printing on a CNC machine are considered. The application of nonlinear dynamics methods in assessing the stability of the 3D printing process and the use of artificial neural networks in the classification and optimization of process parameters are shown. Keywords: 3D printing, cyber physical system, machine learning, hybrid processing, neuroform controller, diagnostics, digital twin. [email protected]

Metal halide perovskites: stability and sensing-ability

2018 ◽  
Vol 6 (38) ◽  
pp. 10121-10137 ◽  
Author(s):  
Zhaohua Zhu ◽  
Qian Sun ◽  
Zhipeng Zhang ◽  
Jie Dai ◽  
Guichuan Xing ◽  
...  
Keyword(s):  
Metal Halide ◽  
Mechanistic Studies ◽  
External Perturbations ◽  
Halide Perovskites ◽  
The Stability

We review the investigations and mechanistic studies on the stability of metal-halide perovskites under external perturbations, and highlight recent attempts to apply them as sensors.

scholarly journals Robot-Accelerated Perovskite Investigation and Discovery (RAPID): 1. Inverse Temperature Crystallization

2019 ◽  
Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Crystal ◽  
High Throughput ◽  
Metal Halide ◽  
Crystal Formation ◽  
Inverse Temperature ◽  
High Quality ◽  
Metal Halide Perovskite ◽  
Halide Perovskite ◽  
Temperature Crystallization

Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal X-ray diffraction studies. We present the first automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 1928 metal halide perovskite synthesis reactions were conducted using six organic ammonium cations (methylammonium, ethylammonium, n-butylammonium, formamidinium, guanidinium, and acetamidinium), increasing the number of metal halide perovskite materials accessible by ITC syntheses by three and resulting in the formation of a new phase, [C<sub>2</sub>H<sub>7</sub>N<sub>2</sub>][PbI<sub>3</sub>]. This comprehensive dataset allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this dataset enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.

scholarly journals PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Daniel Griffith ◽  
Alex S Holehouse
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Recurrent Neural Network ◽  
Transcriptional Activation ◽  
Network Architecture ◽  
Learning Approaches ◽  
Large Protein ◽  
Protein Datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

scholarly journals Dimethylammonium iodide stabilized bismuth halide perovskite photocatalyst for hydrogen evolution

Nano Research ◽  
2020 ◽  
Author(s):  
He Zhao ◽  
Kalyani Chordiya ◽  
Petri Leukkunen ◽  
Alexey Popov ◽  
Mousumi Upadhyay Kahaly ◽  
...  
Keyword(s):  
Hydrogen Generation ◽  
Research Question ◽  
Metal Halide ◽  
Recrystallization Process ◽  
Co Catalyst ◽  
Novel Approach ◽  
Apparent Quantum Efficiency ◽  
Halide Perovskites ◽  
Halide Perovskite ◽  
The Stability

AbstractMetal halide perovskites have emerged as novel and promising photocatalysts for hydrogen generation. Currently, their stability in water is a vital and urgent research question. In this paper a novel approach to stabilize a bismuth halide perovskite [(CH3)2NH2]3[BiI6] (DA3BiI6) in water using dimethylammonium iodide (DAI) without the assistance of acids or coatings is reported. The DA3BiI6 powder exhibits good stability in DAI solutions for at least two weeks. The concentration of DAI is found as a critical parameter, where the I- ions play the key role in the stabilization. The stability of DA3BiI6 in water is realized via a surface dissolution–recrystallization process. Stabilized DA3BiI6 demonstrates constant photocatalytic properties for visible light-induced photo-oxidation of I- ions and with PtCl4 as a co-catalyst (Pt-DA3BiI6), photocatalytic H2 evolution with a rate of 5.7 μmol⋅h-1 from HI in DAI solution, obtaining an apparent quantum efficiency of 0.83% at 535 nm. This study provides new insights on the stabilization of metal halide perovskites for photocatalysis in aqueous solution.

scholarly journals Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1136 ◽  
Author(s):  
Sereina Riniker ◽  
Gregory A. Landrum ◽  
Floriane Montanari ◽  
Santiago D. Villalba ◽  
Julie Maier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Learning Approaches ◽  
High Throughput Screen ◽  
Hit Rate

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

scholarly journals Deep Learning-Based Election Results Prediction Using Twitter Activity

2021 ◽  
Author(s):  
Haider Ali ◽  
Haleem Farman ◽  
Hikmat Yar ◽  
Zahid Khan ◽  
Shabana Habib ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
General Election ◽  
Learning Approaches ◽  
Social Media Platforms ◽  
General Elections ◽  
Election Results ◽  
Twitter Activity ◽  
The Stability

Abstract Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results and a discussion on the outcomes of sentiment analysis for real-world forecasting and approval for general elections in Pakistan.

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