Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing (1803.11246v1)

Abstract: Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment.

• The paper presents an integration of automation, machine learning, and high-performance computing that can slash materials development timelines from decades to years.
• The methodology employs rapid testing, data-driven feedback loops, and high-throughput simulations to identify the most promising material candidates.
• The approach envisions a 'Hardware Cloud' to democratize access to advanced synthesis and characterization tools, enabling faster innovations in critical fields.

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

This paper by Correa-Baena et al. presents a sophisticated approach to accelerate the traditionally protracted process of novel materials development by leveraging a triad of modern technologies: automation, ML, and high-performance computing (HPC). The authors argue that by integrating these technologies, the timeline for bringing new materials from the laboratory to market can be shortened significantly, potentially by an order of magnitude. This shift aligns with investor and market expectations and environmental imperatives by offering enhanced rates of return and timely technological advancements in critical fields.

The research underscores the striking time mismatch in traditional materials development, which spans 15-25 years. It contrasts starkly with the desired shorter timeframes of 2-5 years imposed by funding and leadership cycles in organizations. This delineation elucidates the suboptimal return on investment in energy materials compared to software or medical advancements. By advocating for a paradigm shift towards rapid, data-rich experimental methodologies, this work aims to close this gap and enhance the productivity of materials research.

The authors highlight several key technological advancements and methodologies contributing to this accelerated framework:

• Tool Automation: Rapid testing is achieved through automated tools that streamline the experimental process and focus on viable material candidates.
• Machine Learning and Data Integration: ML facilitates an automated feedback loop that continuously refines experimental design supporting faster progression through predictive insights from historical data.
• High-Throughput Computing: HPC in simulation and data analysis prioritizes resource allocation toward the most promising material properties.

Significant advances in specific areas of materials research are addressed, including:

1. Theoretical Predictions: With computational advancements, the current pace of theoretical outputs has surpassed experimental validation capabilities. Research is leaning towards predicting materials' behaviors and properties under various practical conditions.
2. Synthesis Techniques: High-throughput methods, such as modern vacuum techniques and solution processing, present higher growth rates and a flexible platform for diverse material deposition.
3. Defect Engineering: Understanding and exploiting defect and impurity characteristics are associated with swift performance improvements, exemplified by lead-halide perovskites in optoelectronic applications, notable for their defect tolerance.
4. Diagnostics and Characterization: Enhanced by ML and automation, current diagnostic tools offer significantly faster and, in some cases, more precise results than traditional characterization methods.

A crucial component of the authors’ proposal is the "Hardware Cloud", analogous to the Software Cloud, which envisions centralized, remotely operable facilities for synthesis and characterization. This infrastructure could democratize access to complex, expensive equipment, supported by robust data storage and processing protocols.

The paper also addresses the infrastructural and human-capital needs critical to realizing these advancements. Machine learning must adapt to exercises with sparse data sets while maintaining scientific integrity. There is a call for standardized data formats and robust data-management systems to ensure seamless integration into research practices. Furthermore, evolving the educational landscape to place greater emphasis on interdisciplinary training will prepare researchers to take full advantage of these new technologies.

In conclusion, this work by Correa-Baena et al. presents a detailed, forward-looking vision for the rapid acceleration of materials discovery and development. While acknowledging existing challenges, the paper highlights the potential convergence of automation, machine learning, and high-performance computing as pivotal enablers. These tools promise to augment the scientific process, aligning it more with market needs and societal goals, thereby transforming the landscape of materials science in the coming decade.