Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data (2412.17283v2)

Abstract: The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular interest are emerging materials that display exceptional physical properties, making them promising candidates in energy-efficient microelectronic devices. As the conventional Edisonian approach becomes significantly outpaced by growing societal needs, emerging computational modeling and ML methods are employed for the rational design of materials. However, the complex physical mechanisms, cost of first-principles calculations, and the dispersity and scarcity of data pose challenges to both physics-based and data-driven materials modeling. Moreover, the combinatorial composition-structure design space is high-dimensional and often disjoint, making design optimization nontrivial. In this Account, we review a team effort toward establishing a framework that integrates data-driven and physics-based methods to address these challenges and accelerate materials design. We begin by presenting our integrated materials design framework and its three components in a general context. We then provide an example of applying this materials design framework to metal-insulator transition (MIT) materials, a specific type of emerging materials with practical importance in next-generation memory technologies. We identify multiple new materials which may display this property and propose pathways for their synthesis. Finally, we identify some outstanding challenges in data-driven materials design, such as materials data quality issues and property-performance mismatch. We seek to raise awareness of these overlooked issues hindering materials design, thus stimulating efforts toward developing methods to mitigate the gaps.

• The paper presents a comprehensive framework combining literature-based data acquisition, ML predictive modeling, and Bayesian optimization for materials discovery.
• The paper demonstrates the use of interpretable machine learning classifiers to predict metal–insulator transitions, effectively narrowing down candidate materials.
• The paper introduces Latent Variable Gaussian Processes to handle mixed-variable data, enhancing exploration of high-dimensional design spaces while reducing computational costs.

Integrating Data-Driven and Physics-Based Approaches in Materials Design

The paper "Emerging microelectronic materials by design: Navigating combinatorial design space with scarce and dispersed data" addresses the challenging task of designing next-generation microelectronic materials with unique properties that are critical for advancements in sustainable energy, electronics, and biomedical applications. This paper presents a comprehensive framework that integrates both data-driven and physics-based methods to facilitate the systematic design of these materials, focusing particularly on overcoming issues posed by the high dimensionality and disjoint characteristics of the design space.

The authors propose a multi-faceted framework that consists of three primary components: (1) literature-based data acquisition through text mining and NLP, (2) predictive modeling and virtual screening using ML, and (3) adaptive discovery through Bayesian optimization (BO). This integrated approach aims to efficiently narrow down candidate materials by rapidly screening vast chemical-compositional spaces and then optimizing promising candidates' properties.

A key demonstration of the framework is its application in the discovery of metal--insulator transition (MIT) materials. These materials are pivotal in developing energy-efficient microelectronic devices due to their rapid phase-changing properties between conductive and insulating states. The paper details ML classifiers that predict MIT behaviors based on data compiled from both high-throughput computational databases and literature-driven databases. These classifiers utilize interpretable modeling techniques to uncover underlying features that could inform predictive theories about MIT behaviors.

Furthermore, the paper discusses the development of mixed-variable sensitivity models using Latent Variable Gaussian Processes (LVGPs), a novel addition extending Gaussian processes' capability to handle both numerical and categorical data. This advancement significantly enhances the applicability of Bayesian optimization in complex, high-dimensional material design scenarios. The prediction models use uncertainty quantification effectively, guiding experimentation and simulation efforts towards promising regions of the materials space while minimizing computational costs.

Significant numerical results include the efficient discovery of new candidate materials, particularly within the lacunar spinel and Ruddlesden-Popper perovskite families. These materials were identified and optimized for MIT conditions through adaptive frameworks, dramatically reducing the search space and accelerating discovery processes.

The authors acknowledge existing challenges in data-driven material design, such as inconsistencies in material data quality and the mismatch between intrinsic material properties and device-oriented performance metrics. They advocate for developing robust data acquisition methods and enhancing multi-modal data integration to address these issues effectively.

In terms of implications, the framework outlined in the paper has considerable potential. It offers a pragmatic pathway towards a more systematic exploration of enormous design spaces characteristic of materials science, thereby pushing the frontiers beyond the traditional trial-and-error methods. Although primarily applied to MIT materials, the methodology holds promise for a broader spectrum of emerging materials across various technological domains.

In conclusion, the presented work underscores significant advancements in the interface between computational materials science and machine learning, proposing tangible steps towards overcoming current limitations in materials design. Future developments in automating synthesis and extending co-design strategies at the materials-device level may further streamline and enhance the discovery and deployment of transformative new materials.