Robotic Labs for Streamlining Material Synthesis with Thermodynamic Strategies

In a paper published in the journal Nature Synthesis, researchers addressed the need for efficient synthesis methods for complex materials. They introduced a thermodynamic strategy to navigate phase diagrams to minimize unwanted by-products.

a, A robot-enabled inorganic materials synthesis workflow—from powder precursor preparation to ball milling, to oven firing and to X-ray diffraction (XRD) characterization of reaction products. b, Photograph of the ASTRAL laboratory. c, Robotic chemists enable large-scale exploration of synthesis hypotheses over a broad chemical space, which normally would have to be undertaken by multiple experimentalist groups. d, Human experimentalists have a trade-off between throughput and reproducibility, whereas robotic chemists can achieve both high reproducibility and throughput simultaneously. Image Credit: https://www.nature.com/articles/s44160-024-00502-y
a, A robot-enabled inorganic materials synthesis workflow—from powder precursor preparation to ball milling, to oven firing and to X-ray diffraction (XRD) characterization of reaction products. b, Photograph of the ASTRAL laboratory. c, Robotic chemists enable large-scale exploration of synthesis hypotheses over a broad chemical space, which normally would have to be undertaken by multiple experimentalist groups. d, Human experimentalists have a trade-off between throughput and reproducibility, whereas robotic chemists can achieve both high reproducibility and throughput simultaneously. Image Credit: https://www.nature.com/articles/s44160-024-00502-y

Using a robotic lab, they validated their approach with 224 reactions, achieving higher phase purity for 35 target oxides. This work highlights the potential of robotic labs in advancing experimental synthesis science. The team's predictive approach consistently outperformed traditional methods in yielding purer materials. This research marks a significant step towards accelerating the realization of theoretically predicted compounds.

Related Work

Past work has grappled with designing effective synthesis recipes for inorganic materials, posing bottlenecks in scalable manufacturing and laboratory realization of computationally predicted compounds. While density functional theory aids in estimating synthesizability, optimal synthesis recipes rely heavily on trial-and-error experimentation. The emergence of robotic laboratories offers promise for high-throughput experiments and autonomous optimization.

Yet, a fundamental understanding of how synthesis recipe changes affect solid-state reaction thermodynamics and kinetics still needs to be developed. Multicomponent oxides, crucial for various technologies, often suffer from impurity by-products due to complex phase diagrams. Previous approaches have aimed to address these issues, but a comprehensive solution integrating thermodynamics, kinetics, and precursor selection is still lacking.

Precursor Selection Methodology

Precursor selection principles represent a crucial aspect of solid-state reaction design, aiming to optimize the synthesis pathway toward the target compound. Recent findings underscore the significance of pairwise reactions between precursors, revealing that reactions typically initiate between only two precursors at a time. This insight highlights the potential formation of intermediate by-products in multistep responses, which can consume a significant portion of the total reaction energy, hampering the completion of the desired reaction.

Researchers illustrate these principles through the example of lithium barium borate (LiBaBO3) synthesis, where the traditional approach involves using three precursors—Li2CO3, boron trioxide (B2O3), and barium oxide (BaO). However, considering the decomposition of Li2CO3 to Li2O upon heating, pairwise reactions between these precursors reveal the formation of stable ternary oxides, leading to insufficient driving force for the target phase.

By contrast, initiating the reaction with LiBO2 and BaO demonstrates superior phase purity, indicating a more efficient pathway to LiBaBO3 synthesis. These findings suggest five key principles for precursor selection, emphasizing the importance of pairwise reactions, maximizing thermodynamic driving force, and ensuring the dominance of the target phase over competing phases.

Researchers applied these principles to a broader chemical space, conducting a large-scale experimental validation using the automated synthesis testing and research augmentation Lab (ASTRAL), a robotic laboratory designed for automated materials synthesis. The validation sought to evaluate how well the proposed precursor selection principles performed across various materials and under different synthesis conditions.

The synthesis process, involving 28 unique precursors applied to 35 target materials, was automated from dispensing precursors to X-ray characterization of reaction products. Researchers demonstrated the efficiency and reproducibility of the proposed approach through 224 synthesis reactions, overcoming challenges associated with traditional synthesis methods and offering a transformative platform for data-driven empirical synthesis science.

The validation effort encompassed diverse crystal chemistries, highlighting the potential of the robotic laboratory to explore synthesis hypotheses over a wide range of materials comprehensively. Unlike traditional experimental approaches, which require extensive human effort and often involve trade-offs between throughput and reproducibility, ASTRAL enables rapid, systematic investigation of synthesis pathways while ensuring high reproducibility and throughput. This approach heralds a new era in solid-state synthesis, where hypotheses can be rigorously tested and refined across diverse chemical spaces with unprecedented efficiency.

Computational Precursor Selection

The precursor selection approach yielded promising results across 35 materials, showcasing the effectiveness of computational design over traditional methods. Predicted precursors consistently outperformed traditional ones, achieving higher phase purity in 91% of cases. Notably, predicted precursors surpassed traditional ones in 15 targets, with six targets exclusively synthesized by the approach. Even when traditional precursors performed better, predicted precursors produced target materials with moderate to high purity.

Metastable compounds, albeit with low phase purity, were successfully synthesized using predicted precursors, indicating the potential for tuning thermodynamic forces for selective phase formation. Despite simplifications in DFT-calculated convex hulls, they retained predictive power due to the dominance of enthalpic contributions in oxide synthesis reactions and the skewed nature of ternary convex hulls. While thermodynamic driving forces serve as reliable proxies for kinetics, limitations arise in shallow reaction landscapes or high inverse hull energies, challenging the certainty of success based solely on thermodynamics.

These findings offer valuable feedback for refining future algorithms for solid-state precursor prediction, emphasizing the need to consider both thermodynamics and kinetics. Additionally, opportunities exist to enhance reaction outcomes through metathesis reactions, suggesting avenues for further innovation in precursor selection methodologies to optimize material synthesis.

Conclusion

To sum up, the team applied advanced precursor selection principles, exemplified through synthesizing LiBaBO3 and demonstrated the potential for enhancing the efficiency and reproducibility of materials synthesis processes.

Researchers can systematically explore diverse chemical spaces by leveraging insights into solid-state reactions and employing robotic laboratories like ASTRAL, validating and refining synthesis hypotheses with unprecedented speed and accuracy. This transformative approach overcomes traditional synthesis challenges and lays the foundation for future data-driven empirical synthesis science advancements.

Journal reference:
Silpaja Chandrasekar

Written by

Silpaja Chandrasekar

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.

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