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1. Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning

   https://doi.org/10.1002/advs.201902607  

   Chen, Chun‐Teh ; Gu, Grace X. ( January 2020 , Advanced Science)

   In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general‐purpose inverse design approach is presented using generative inverse design networks. This ML‐based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order‐of‐magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient‐based topology optimization and gradient‐free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems.

    

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2. Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

   https://doi.org/10.1002/prep.202200276  

   Choi, Joseph B. ; Nguyen, Phong C. H. ; Sen, Oishik ; Udaykumar, H. S. ; Baek, Stephen ( March 2023 , Propellants, Explosives, Pyrotechnics)

   Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out “process” as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research.

    

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3. Targeted materials discovery using Bayesian algorithm execution

   https://doi.org/10.1038/s41524-024-01326-2  

   Chitturi, Sathya_R ; Ramdas, Akash ; Wu, Yue ; Rohr, Brian ; Ermon, Stefano ; Dionne, Jennifer ; Jornada, Felipe_H_da ; Dunne, Mike ; Tassone, Christopher ; Neiswanger, Willie ; et al ( July 2024 , npj Computational Materials)

   Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO₂nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

    

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4. High‐throughput computational design of halide perovskites and beyond for optoelectronics

   https://doi.org/10.1002/wcms.1500  

   Li, Yuheng ; Yang, Kesong ( August 2020 , WIREs Computational Molecular Science)

   Halide perovskites have attracted great interest as promising next‐generation materials in optoelectronics, ranging from solar cells to light‐emitting diodes. Despite their exceptional optoelectronic properties and low cost, the prototypical organic–inorganic hybrid lead halide perovskites suffer from toxicity and low stability. Therefore, it is of high demand to search for stable and nontoxic alternatives to the hybrid lead halide perovskites. Recently, high‐throughput computational materials design has emerged as a powerful approach to accelerate the discovery of new halide perovskite compositions or even novel compounds beyond perovskites. In this review, we discuss how this approach discovers halide perovskites and beyond for optoelectronics. We first overview the background of halide perovskites and methodologies in high‐throughput computational design. Then, we focus on materials properties for different optoelectronic applications, and how they are assessed with materials descriptors. Finally, we review different studies in terms of specific materials types to discuss their design principles, screening results, and experimental verification.

   This article is categorized under:

   Structure and Mechanism > Computational Materials Science

   Electronic Structure Theory > Density Functional Theory

    

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5. Systems Approaches to Materials Design: Past, Present, and Future

   https://doi.org/10.1146/annurev-matsci-070218-125955  

   Arróyave, Raymundo ; McDowell, David L. ( July 2019 , Annual Review of Materials Research)

   There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design. 

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