arXiv subjects

Christopher Karpovich

Publications and source records attributed to Christopher Karpovich.

4 recordsLinked to original sources

arXiv2024

Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data

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 machine learning (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.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Learning

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the complexity of underlying mechanisms, and the challenges of experimental validation. Here, we present a physics-informed machine learning framework that accelerates the discovery of thermally driven MIT materials. Using a trained classifier, we screen a crystal structure database to identify promising candidates for higher fidelity simulations. We focus on Ca$_2$Fe$_3$O$_8$, CaCo$_2$O$_4$, and CaMn$_2$O$_4$, and use density functional theory (DFT) to determine their electronic and magnetic ground states and assess their microscopic MIT mechanisms. We further apply machine learning regression models to estimate their transition temperatures and employ synthesis prediction tools to identify likely precursors and reaction routes. This integrated approach reduces the time and effort required to identify, understand, and synthesize new MIT materials, providing a generalizable pathway for accelerating correlated quantum materials discovery.

cond-mat.mtrl-sci↗

arXiv2022

Deep Reinforcement Learning for Inverse Inorganic Materials Design

A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this work, we propose a reinforcement learning (RL) approach to inverse inorganic materials design, which can identify promising compounds with specified properties and synthesizability constraints. Our model learns chemical guidelines such as charge and electronegativity neutrality while maintaining chemical diversity and uniqueness. We demonstrate a multi-objective RL approach, which can generate novel compounds with targeted materials properties including formation energy and bulk/shear modulus alongside a lower sintering temperature synthesis objectives. Using this approach, the model can predict promising compounds of interest, while suggesting an optimized chemical design space for inorganic materials discovery.

cond-mat.mtrl-sci↗

arXiv2021

Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning

Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by chemists' prior knowledge and experience, built upon experimental trial-and-error that is both time and resource consuming. Recent developments in natural language processing (NLP) have enabled large-scale text mining of scientific literature, providing open source databases of synthesis information of synthesized compounds, material precursors, and reaction conditions (temperatures, times). In this work, we employ a conditional variational autoencoder (CVAE) to predict suitable inorganic reaction conditions for the crucial inorganic synthesis steps of calcination and sintering. We find that the CVAE model is capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route (solid-state, sol-gel) that are present in the inorganic synthesis space. Moreover, the CVAE can generalize well to unseen chemical entities and shows promise for predicting reaction conditions for previously unsynthesized compounds of interest.

cond-mat.mtrl-sci↗