Query: ML driven material science

Candidate papers:

abstract: Recently, photo degrading organic waste material is a hot topic. Many photocatalysts based on semiconductor oxide have been reported. Although they have excellent catalytic activity under ultraviolet (UV) irradiation, it is difficult to practical application because of the limited ratio of UV light in whole solar irradiation. In this work, we prepared solar driven photocatalyst based on graphene quantum dots (GQDs) and silver nanoparticles (Ag NPs) by simple photo reduction reaction. In the synthesized process, GQDs were served as reducing agent for Ag cations and capping agent for the in situ formation of Ag NPs with small size. Although the used concentration of Ag cations was very low (just 0.54 mg/ml) GQDs Ag photocatalyst exhibited a high efficiency for photo degradation Rhodamine B under sunlight. It could be ascribed to the synthetic role of Ag NPs and GQDs, and further the detailed photocatalytic mechanism was analyzed in this paper. Because this kind of photocatalyst made from water solution and can be driven by sunlight, it is a promising photocatalyst for industrial applications in the environmental remediation field.
author_names: Lifeng Wang

Yan Li

Sen Li

Ying-Min Wang

Wen-hui Kong

Wendong Xue

Cancan Li
title: Synthesis, characterization and photocatalytic activity of graphene quantum dots Ag solar driven photocatalyst
venue: Journal of Materials Science: Materials in Electronics
year: 2017

abstract: A ternary visible light driven photocatalyst, Ag/BiVO4/reduced graphene oxide (rGO) composite was manufactured by hydrothermal strategy. The optimized products were characterized by XRD, SEM, HRTEM, EDS, XPS, DRS, Raman spectra, PL, BET, photocurrent density and EIS analysis. Compared to pure BiVO4, the fabricated ternary composite showed enhanced photocatalytic ability to decompose pollutant under visible light. Triclosan was completely removed after 100 min in solution with 1 mg/mL photocatalyst under visible light irradiation. Repeated cycle tests demonstrated the photo stability and reusability of composite to decompose triclosan, indicating that this material could be utilized repeatedly. The upgraded photocatalytic ability was attributed to the addition of Ag and rGO, which enhanced the charge separation and inhibited the recombination of photogenerated electrons and holes. The EPR spin trap technique (with DMPO) was performed to identify the radicals produced in Ag/BiVO4/rGO under the visible light, and trapping experiments were conducted to determine the main active species in the photocatalytic process of decomposing triclosan. Finally, seven reaction intermediates of triclosan were detected by LC MS/MS and possible degradation routes were proposed.
author_names: Ming Li

Guanghui Xu

Zhen-Lon Guan

Yang Wang

Hongwen Yu

Yong Yu
title: Synthesis of Ag/BiVO4/rGO composite with enhanced photocatalytic degradation of triclosan.
venue: The Science of the total environment
year: 2019

abstract: Innovative paramagnetic one dimensional (1 D) core shell Fe Ag@AgCl visible light driven photocatalysts are synthesized through a template assisted electrodeposition method trailed by FeCl3 in situ oxidation. The metallic nature of Fe Ag@AgCl is confirmed through scanning electron microscopy (SEM) and crystal nature through X ray diffraction (XRD) The controllable diameter of Fe Ag is obtained through the selection of hollow size of the polycarbonate (PC) template. Electron impedance spectroscopy (EIS) confirms through the introduction of Fe to the Ag core that has prolonged the recombination of electron and hole. Escherichia coli (E. coli) are employed as the target bacteria to evaluate the photocatalytic disinfection performances. A total of 1.30 mg of Fe Ag@AgCl is proved to be able to completely inactivate 107 CFU (colony forming units)/mL after 120 min of visible light irradiation. The transition electron microscopy (TEM) confirms the stability of the material after the photo reaction. As Fe Ag@AgCl possesses magnetic properties, the material is recovered through the application of an external magnetic field. SEM images and results of 3D emission extraction matrix (EEM) depict that the bacteria cell death is caused by membrane permeability changes caused by the reduction of membrane associated proteins.
author_names: Yanping Cui

Nimesha Thathsarani

Liang Peng

Ya Gao

Lidan Lei

Zhengwei Zhou

Li-Li Liang

Xiaoyan Shi
title: Visible light activated 1 D core shell paramagnetic Fe Ag@AgCl as an innovative method for photocatalytic inactivation of E. coli
venue: Environmental Science and Pollution Research
year: 2020

abstract: Abstract Discovery of new energy materials with thermal stability and special electro optical properties has always been the goal and challenge of material science. As an important energy material, spinel has been widely used in the fields of photovoltaics, piezoelectric, catalysis, batteries, and thermoelectrics. However, there are many spinels with AB2X4 formula that have not been explored, especially for the ones with direct band gaps, which severely limit their applications. Here, we develop a target driven method that uses machine learning (ML) to accelerate the ab initio predictions of unknown spinels from the periodic table of elements. Under this strategy, eight spinels with direct band gaps and thermal stabilities at room temperature are screened out successfully from 3880 unexplored spinels (CaAl2O4, CaGa2O4, SnGa2O4, CaAl2S4, CaGa2S4, CaAl2Se4, CaGa2Se4, CaAl2Te4) The screened spinels show good optoelectronic performance in the energy systems (thin film solar cells, photocatalysts, etc. Based on the XGBoost algorithm, a semiconductor classification model with strong structure property relationship is established, with a high prediction accuracy of 91.2% and a low computational cost of a few milliseconds. The proposed target driven approach shortens the research cycle of spinel screening by approximately 3.4 years and enables the discovery and design of a wide range of energy materials. Compared with traditional high throughput material screening, the proposed method has potential applications in shortening the screening time and accelerating the development of material genomics.
author_names: Zhilong Wang

Haikuo Zhang

Jinjin Li
title: Accelerated discovery of stable spinels in energy systems via machine learning
year: 2021

abstract: Abstract Computational materials science based on data driven approach has gained increasing interest in recent years. The capability of trained machine learning (ML) models, such as an artificial neural network (ANN) to predict the material properties without repetitive calculations is an appealing idea to save computational time. Thermal conductivity in single or multilayer structure is a quintessential property that plays a pivotal role in electronic applications. In this work, we exemplified a data driven approach based on ML and high throughput computation (HTC) to investigate the cross plane thermal transport in multilayer stanene. Stanene has attracted considerable attention due to its novel electronic properties such as topological insulating features with a wide bandgap, making it an appealing candidate to ferry current in electronic devices. Classical molecular dynamics simulations are performed to extract the lattice thermal conductivities (kL) The calculated cross plane kL is orders of magnitude lower than its lateral counterparts. Impact factors such as layer number, system temperature, interlayer coupling strength, and compressive/tensile strains are explored. It is found that kL of multilayer stanene in the cross plane direction can be diminished by 86.7% with weakened coupling strength, or 66.6% with tensile strains. A total of 2700 kL data are generated using HTC, which are fed into 9 different ANN models for training and testing. The best prediction performance is given by the 2 layer ANN with 30 neurons in each layer.
author_names: Yang Hong

Dan Han

Bo Hou

Xinyu Wang

Jingchao Zhang
title: High Throughput Computations of Cross Plane Thermal Conductivity in Multilayer Stanene
year: 2021

abstract: To date, the development of organic light emitting diode (OLED) materials has been primarily based on a combination of chemical intuition and trial and error experimentation. The approach is often expensive and time consuming, let alone in most instances fails to offer new materials leading to higher efficiencies. Data driven approaches have emerged as a powerful tool to accelerate the design and discovery of novel materials with multifunctional properties for next generation OLED technologies. Virtual high throughput methods assisted by machine learning (ML) enable a broad screening of chemical space to predict material properties and suggest new candidates for OLEDs. In order to build reliable predictive ML models for OLED materials, it is required to create and manage a high volume of data which not only maintain high accuracy but also properly assess the complexity of materials chemistry in the OLED space. Active learning (AL) is among several strategies developed to face the challenge in both materials science and life science applications, where the data management in large scale becomes a main bottleneck. Here, we present a workflow that efficiently combines AL with atomic scale simulations to reliably predict optoelectronic properties of OLED materials. This study provides a robust and validated framework to account for multiple parameters that simultaneously influence OLED performance. Results of this work pave the way for a fundamental understanding of optoelectronic performance of emergent layers from a molecular perspective, and further screen candidate materials with superior efficiencies before laborious simulations, synthesis, and device fabrication.
author_names: Hadi Abroshan

Anand Chandrasekaran

Paul Winget

Shaun H Kwak

Mathew D Halls
title: Accelerated design and optimization of novel OLED materials via active learning
venue: Organic Photonics Electronics
year: 2021

abstract: Tingting Kong obtained her PhD degree from Xi'an University of Science and Technology in 2017 and is currently a lecturer at Xi'an Shiyou University. Her research interest focuses on material design for photocatalytic CO2 conversion. Jingxiang Low obtained his PhD degree from Wuhan University of Technology in 2018 and is currently a postdoctoral fellow at the University of Science and Technology of China (USTC) His research interest focuses on photocatalyst design for CO2 reduction, nitrogen fixation, and methane conversion. Yujie Xiong is the Cheung Kong Chair Professor, deputy dean of the School of Chemistry and Materials Science at USTC, and director of the Division of Nanocatalysis and Energy Conversion at Hefei National Laboratory for Physical Sciences at the Microscale. His research interests include inorganic materials and devices for CO2 reduction, nitrogen fixation, methane conversion, water splitting, and chemical production.
author_names: Tingting Kong

Jingxiang Low

Yujie Xiong
title: Catalyst: How Material Chemistry Enables Solar Driven CO2 Conversion
year: 2020

abstract: X ray diffraction (XRD) is well known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security) the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition structure property relationships between materials to enable material identification free classification. We use an expert informed, data driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user specified classes in the presence of different types and degrees of signal degradation.
author_names: Siyang Yuan

Scott D Wolter

Joel A Greenberg
title: Classification free threat detection based on material science informed clustering
venue: Defense Security
year: 2017

abstract: The investigation of superintense laser driven ion sources and their potential applications offers unique opportunities of multisciplinary research. Plasma physics can be combined with materials and nuclear science, radiation detection and advanced laser technology, leading to novel research challenges of great fundamental and applicative interest. In this paper we present interesting and comprehensive results on nanostructured low density (near critical) foam target for TW and PW class lasers, obtained in the framework of the ERC ENSURE project. Numerical simulations and experimental activities carried out at 100s TW and PW class laser facilities have shown that targets consisting in a solid foil coated with a nanostructured low density (near critical) foam can lead to an enhancement of the ion acceleration process. This stimulated a thorough numerical investigation of superintense laser interaction with nanostructured near critical plasmas. Thanks to a deep understanding of the foam growth process via Pulsed Laser Deposition technique and to the complementary capabilities of High Power Impulse Magnetron Sputtering, advanced multi layer targets based on near critical films with carefully controlled properties (e.g. density gradients over few microns length scales) can now be manufactured, with applications outreaching the field of laser driven ion acceleration. In addition, a comprehensive numerical and theoretical work has allowed to design dedicated experiments and a realistic table top apparatus for laser driven materials irradiation, Ion Beam Analysis and neutron generation, that exploits a double layer target to reduce the requirements for the laser system.
author_names: Matteo Passoni

Francesca Maria Arioli

Lorenzo Cialfi

David Dellasega

Luca Fedeli

Arianna Formenti

Anna Chiara Giovannelli

Alessandro Maffini

F Mirani

Andrea Pazzaglia

Alessandro Tentori

D Vavassori

Margherita Zavelani-Rossi

Valeria Russo
title: Advanced laser driven ion sources and their applications in materials and nuclear science
year: 2019

abstract: Data is a crucial raw material of this century, and the amount of data that has been created in materials science in recent years and is being created every new day is immense. Without a proper infrastructure that allows for collecting and sharing data (including the original data) the envisioned success of materials science and, in particular, Big Data driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture towards a comprehensive and FAIR data sharing, opening new avenues for mining Big Data of materials science. Novel data analytics concepts and tools turn data into knowledge and help the prediction of new materials or the identification of new properties of already known materials.
author_names: Claudia Draxl

Matthias Scheffler
title: NOMAD: The FAIR Concept for Big Data Driven Materials Science
year: 2018