Query: "materials science"

title: First principles phonon calculations in materials science
abstract: Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized.  Here we demonstrate phonon properties with fundamental equations and show examples how the phonon calculations are applied in materials science.

title: A materials science perspective on tackling COVID 19
abstract: The ongoing SARS CoV 2 pandemic highlights the importance of materials science in providing tools and technologies for antiviral research and treatment development. In this Review, we discuss previous efforts in materials science in developing imaging systems and microfluidic devices for the in depth and real time investigation of viral structures and transmission, as well as material platforms for the detection of viruses and the delivery of antiviral drugs and vaccines. We highlight the contribution of materials science to the manufacturing of personal protective equipment and to the design of simple, accurate and low cost virus detection devices. We then investigate future possibilities of materials science in antiviral research and treatment development, examining the role of materials in antiviral drug design, including the importance of synthetic material platforms for organoids and organs on a chip, in drug delivery and vaccination, and for the production of medical equipment. Materials science based technologies not only contribute to the ongoing SARS CoV 2 research efforts but can also provide platforms and tools for the understanding, protection, detection and treatment of future viral diseases. Materials science provides tools and technologies for the protection against viral infections, as well as for the understanding, diagnosis, treatment and prevention of viral diseases. This Review discusses present and future directions in antiviral materials science research, with a focus on COVID 19.

title: DScribe: Library of Descriptors for Machine Learning in Materials Science
abstract: DScribe is a software package for machine learning that provides popular feature transformations "descriptors" for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user friendly, off the shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many body Tensor Representation (MBTR) Atom centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP) Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open source Apache License 2.0.

title: Quantum Algorithms for Quantum Chemistry and Quantum Materials Science.
abstract: As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus, it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynman's idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground state, dynamics, and thermal state simulation and analyze their strengths and weaknesses for future developments.

title: Unsupervised word embeddings capture latent knowledge from materials science literature
abstract: The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3 10, which requires large hand labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information dense word embeddings11 13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature. Natural language processing algorithms applied to three million materials science abstracts uncover relationships between words, material compositions and properties, and predict potential new thermoelectric materials.

title: Recent advances and applications of machine learning in solid state materials science
abstract: One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure property relationships and various approaches for the replacement of first principle methods by machine learning. We review how active learning and surrogate based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

title: From DFT to machine learning: recent approaches to materials science a review
abstract: Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning (ML) algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory (DFT) as the representative instance of electronic structure methods, to the subsequent high throughput (HT) approach, used to generate large amounts of data. Ultimately, data driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field.

title: Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
abstract: One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial and error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.

title: Superatoms in materials science
abstract: Clusters are ensembles of bound atoms intermediate in size between a molecule and a bulk solid. Some of these clusters form stable units with atomically precise structures that give rise to collective behaviours that mimic those of traditional atoms, essentially functioning as 'superatoms' Such superatoms are exciting, nanoscale building blocks for materials design. Sustained synthetic and theoretical efforts over the past 40 years have created a vast library of chemically tuneable superatoms that display unique physical and chemical properties. The use of superatoms as building blocks for materials offers opportunities to design materials with tailored functionalities; however, this potential has only begun to be realized in the past few years. The assembly of superatomic crystals presents numerous challenges, including the design of suitable building blocks, control of the self assembly process, promotion of strong inter superatom coupling and understanding the resulting collective properties. In this Review, we assess recent advances in meeting these challenges by focusing on metal chalcogenide and carbon based clusters (for example, fullerenes) as superatomic building blocks. Molecular clusters can be used as tuneable, superatomic building blocks to create new functional materials. This Review outlines the synthesis and assembly of superatoms into superatomic crystals and the resulting collective material properties.

title: Data Driven Materials Science: Status, Challenges, and Perspectives
abstract: Data driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.