The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human–machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human–machine partnerships for fast and efficient virtual materials screening, development and discovery.

Emerging materials intelligence ecosystems propelled by machine learning

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses

[This abstract is based on the author's abstract.] Improving the quality of a product/process using a computer simulator is a much less expensive option than the real physical testing. However, simulation using computationally intensive computer models ..

Space-filling designs for computer experiments: A review

pdf/least-squares-polynomial-chaos-expansion-a-review-of-5xbz5530gz.pdf

Least squares polynomial chaos expansion: A review of sampling strategies

A generative design technique for exploring shape variations

SUMMARY Space-filling properties are important in designing computer experiments. The traditional maximinandminimaxdistancedesignsconsideronlyspace-fillinginthefull-dimensionalspace; thiscanresultinpoorprojectionsontolower-dimensionalspaces,whichisundesirablewhenonly af ew factors are active. Restricting maximin distance design to the class of Latin hypercubes can improveone-dimensionalprojectionsbutcannotguaranteegoodspace-fillingpropertiesinlarger subspaces. We propose designs that maximize space-filling properties on projections to all subsets of factors. We call our designs maximum projection designs. Our design criterion can be computed at no more cost than a design criterion that ignores projection properties.

Maximum projection designs for computer experiments

Computer experiments may involve not only continuous input factors but also nominal factors, discrete numeric factors, and ordinal factors. Most existing literature in designing computer experiment...

Designing computer experiments with multiple types of factors: The MaxPro approach

Understanding the uncertainty in simulation outputs is important for careful decision-making regarding a machining process However, Monte Carlo–based methods cannot be used for evaluating the uncertainty when the simulations are computationally expensive An alternative approach is to build an easy-to-evaluate emulator to approximate the computer model and run the Monte Carlo simulations on the emulator Although this approach is very promising, it becomes inefficient when the computer model is highly nonlinear and the region of interest is large Most machining simulations are of this kind because the output is affected by several quantitative factors—such as the workpiece material properties, cutting tool parameters, and process parameters whose effects can change depending on other qualitative factors such as the type of materials, tool designs, and tool paths Because the number of levels of the qualitative factors can range from tens to thousands, building an accurate emulator is not an easy task This article proposes a new approach, called an in situ emulator, to overcome this problem The idea is to build an emulator for the user-specified levels of the qualitative factors and inside the local region defined by the input uncertainty distribution of the quantitative factors Efficient experimental design and statistical modeling techniques are used for constructing the in situ emulator The approach is illustrated using the simulations of two solid end milling processes

Uncertainty quantification of machining simulations using an in situ emulator

https://doi.org/10.1016/j.jretconser.2022.103010

How did COVID-19 change what people buy: Evidence from a supermarket chain

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Maximum projection designs for computer experiments

Designing computer experiments with multiple types of factors: The MaxPro approach

Uncertainty quantification of machining simulations using an in situ emulator

How did COVID-19 change what people buy: Evidence from a supermarket chain