218 research outputs found
Advances of Machine Learning in Materials Science: Ideas and Techniques
In this big data era, the use of large dataset in conjunction with machine
learning (ML) has been increasingly popular in both industry and academia. In
recent times, the field of materials science is also undergoing a big data
revolution, with large database and repositories appearing everywhere.
Traditionally, materials science is a trial-and-error field, in both the
computational and experimental departments. With the advent of machine
learning-based techniques, there has been a paradigm shift: materials can now
be screened quickly using ML models and even generated based on materials with
similar properties; ML has also quietly infiltrated many sub-disciplinary under
materials science. However, ML remains relatively new to the field and is
expanding its wing quickly. There are a plethora of readily-available big data
architectures and abundance of ML models and software; The call to integrate
all these elements in a comprehensive research procedure is becoming an
important direction of material science research. In this review, we attempt to
provide an introduction and reference of ML to materials scientists, covering
as much as possible the commonly used methods and applications, and discussing
the future possibilities.Comment: 80 pages; 22 figures. To be published in Frontiers of Physics, 18,
xxxxx, (2023
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
Recently, there has been extensive use of artificial Intelligence (AI) in the
field of material engineering. This can be attributed to the development of
high performance computing and thereby feasibility to test deep learning models
with large parameters. In this article we tried to review some of the latest
developments in the applications of AI in material engineering.Comment: V
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods
Machine learning (ML) is increasingly becoming a helpful tool in the search
for novel functional compounds. Here we use classification via random forests
to predict the stability of half-Heusler (HH) compounds, using only
experimentally reported compounds as a training set. Cross-validation yields an
excellent agreement between the fraction of compounds classified as stable and
the actual fraction of truly stable compounds in the ICSD. The ML model is then
employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely
stable candidates. The predicted stability of HH compounds from three previous
high throughput ab initio studies is critically analyzed from the perspective
of the alternative ML approach. The incomplete consistency among the three
separate ab initio studies and between them and the ML predictions suggests
that additional factors beyond those considered by ab initio phase stability
calculations might be determinant to the stability of the compounds. Such
factors can include configurational entropies and quasiharmonic contributions.Comment: 11 pages, 5 figures, 2 table
Leveraging Language Representation for Material Recommendation, Ranking, and Exploration
Data-driven approaches for material discovery and design have been
accelerated by emerging efforts in machine learning. While there is enormous
progress towards learning the structure to property relationship of materials,
methods that allow for general representations of crystals to effectively
explore the vast material search space and identify high-performance candidates
remain limited. In this work, we introduce a material discovery framework that
uses natural language embeddings derived from material science-specific
language models as representations of compositional and structural features.
The discovery framework consists of a joint scheme that, given a query
material, first recalls candidates based on representational similarity, and
ranks the candidates based on target properties through multi-task learning.
The contextual knowledge encoded in language representations is found to convey
information about material properties and structures, enabling both similarity
analysis for recall, and multi-task learning to share information for related
properties. By applying the discovery framework to thermoelectric materials, we
demonstrate diversified recommendations of prototype structures and identify
under-studied high-performance material spaces, including halide perovskite,
delafossite-like, and spinel-like structures. By leveraging material language
representations, our framework provides a generalized means for effective
material recommendation, which is task-agnostic and can be applied to various
material systems
Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning
Thermoelectric materials can be used to construct devices which recycle waste
heat into electricity. However, the best known thermoelectrics are based on
rare, expensive or even toxic elements, which limits their widespread adoption.
To enable deployment on global scales, new classes of effective thermoelectrics
are thus required. models of transport properties can help
in the design of new thermoelectrics, but they are still too computationally
expensive to be solely relied upon for high-throughput screening in the vast
chemical space of all possible candidates. Here, we use models constructed with
modern machine learning techniques to scan very large areas of inorganic
materials space for novel thermoelectrics, using composition as an input. We
employ an attention-based deep learning model, trained on data derived from
calculations, to predict a material's Seebeck coefficient,
electrical conductivity, and power factor over a range of temperatures and
- or -type doping levels, with surprisingly good
performance given the simplicity of the input, and with significantly lower
computational cost. The results of applying the model to a space of known and
hypothetical binary and ternary selenides reveal several materials that may
represent promising thermoelectrics. Our study establishes a protocol for
composition-based prediction of thermoelectric behaviour that can be easily
enhanced as more accurate theoretical or experimental databases become
available
The 2019 materials by design roadmap
Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design
Atomistic Line Graph Neural Network for Improved Materials Property Predictions
Graph neural networks (GNN) have been shown to provide substantial
performance improvements for representing and modeling atomistic materials
compared with descriptor-based machine-learning models. While most existing GNN
models for atomistic predictions are based on atomic distance information, they
do not explicitly incorporate bond angles, which are critical for
distinguishing many atomic structures. Furthermore, many material properties
are known to be sensitive to slight changes in bond angles. We present an
Atomistic Line Graph Neural Network (ALIGNN), a GNN architecture that performs
message passing on both the interatomic bond graph and its line graph
corresponding to bond angles. We demonstrate that angle information can be
explicitly and efficiently included, leading to improved performance on
multiple atomistic prediction tasks. We use ALIGNN models for predicting 52
solid-state and molecular properties available in the JARVIS-DFT, Materials
project, and QM9 databases. ALIGNN can outperform some previously reported GNN
models on atomistic prediction tasks by up to 85 % in accuracy with better or
comparable model training speed
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS)
infrastructure at the National Institute of Standards and Technology (NIST) is
a large-scale collection of curated datasets and tools with more than 80000
materials and millions of properties. JARVIS uses a combination of electronic
structure, artificial intelligence (AI), advanced computation and experimental
methods to accelerate materials design. Here we report some of the new features
that were recently included in the infrastructure such as: 1) doubling the
number of materials in the database since its first release, 2) including more
accurate electronic structure methods such as Quantum Monte Carlo, 3) including
graph neural network-based materials design, 4) development of unified
force-field, 5) development of a universal tight-binding model, 6) addition of
computer-vision tools for advanced microscopy applications, 7) development of a
natural language processing tool for text-generation and analysis, 8) debuting
a large-scale benchmarking endeavor, 9) including quantum computing algorithms
for solids, 10) integrating several experimental datasets and 11) staging
several community engagement and outreach events. New classes of materials,
properties, and workflows added to the database include superconductors,
two-dimensional (2D) magnets, magnetic topological materials, metal-organic
frameworks, defects, and interface systems. The rich and reliable datasets,
tools, documentation, and tutorials make JARVIS a unique platform for modern
materials design. JARVIS ensures openness of data and tools to enhance
reproducibility and transparency and to promote a healthy and collaborative
scientific environment
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