At NeurIPS 2022, Intel Labs researchers, along with academic and corporate co-organizers hosted the 1st workshop on AI for Accelerated Design (AI4Mat). As outlined in a previous post, the goals, technical themes, and Intel Labs' contributions centered around leveraging novel AI techniques to advance the complex challenge of materials design and bringing advanced materials into the real world to address some of today’s most pressing technological challenges.
Figure 1. Schedule Overview of the AI4Mat Workshop on December 2nd, 2022.
The schematic schedule above shows the general outline of the workshop and its breakdown along the three major themes: 1. AI-Guided Design; 2. Automated Chemical Synthesis; 3. Automated Material Characterization. The opening remarks provided a general introduction to the workshop themes, schedule, and its speaker and highlighted some statistics related to the scientific contributions:
Figure 2. Workshop Contributions along Scientific Categories (left) and Institutions with Largest Number of Accepted Submissions (right).
As shown in the figures above, the contributions skewed heavily toward AI-Guided Design, which more closely aligns with paper submissions to the primary NeurIPS conference. One of the main goals of the workshop is to further promote, encourage and connect researchers who are working on currently underrepresented themes of Automated Chemical Synthesis and Automated Material Characterization. Increased research activity in these areas will drive the community closer to the realization of closed-loop AI-infused automated materials design with a meaningful impact on real-world applications. Automated Chemical Synthesis and Automated Material Characterization often require real-world tools and equipment, which can often create a greater barrier to entry for researchers to make meaningful progress. The contribution statistics also show the strong representation of Intel Labs research in the workshop with 8 accepted works, including one spotlight presentation, followed closely by MIT with 7 accepted works and the University of Toronto and Harvard with 3 accepted works.
The workshop began with a cross-disciplinary panel spanning the technical themes of the workshop, as well as representation from academic, governmental, and industrial research organizations.
The panel discussion initially focused on providing thoughts and advice for active and interested researchers building their careers in the highly interdisciplinary and complex field of AI and materials science, followed by an extensive discussion of some of the ongoing challenges in the field. Some of the major takeaways from the panel include:
- AI is already being applied in targeted, consequential ways in real-world materials design workflows and applications. However, major breakthroughs that fulfill the ambitious goals outlined in the workshop are still many years away. Thus creating opportunities for meaningful research work.
- The fragmented nature of data collection and dataset usage is a major challenge in scaling AI for automated materials design. As systems and technical ambitions become more and more complex, the ability to manage different kinds of data at larger scales becomes more and more important.
The workshop then continued with keynotes, spotlights from the three thematic tracks, an interactive discussion, and a poster session showcasing all contributions.
The keynotes showcased real-world use cases of advanced AI in materials design, including natural language processing for determining fabrication paths of new materials systems, building AI-based tools to enable robotic synthesis of molecular materials, and applying deep learning for advanced electron microscopy at state-of-the-art atomic scales. Some major takeaways from the various keynotes and spotlights include:
- With some human action in the loop, AI can already significantly accelerate materials discovery in real-world use cases, including conductive thin films and 3D-printed bone replacements.
- AI can significantly speed up the discovery process by effectively using compute in approximating simulations and effectively searching in large design spaces.
Automated Chemical Synthesis:
- Designing AI systems with actionable chemical steps in mind can significantly improve the synthesizability of generated structures and thereby help optimize and automate various materials fabrication procedures.
- Sample efficiency in experimental materials fabrication is a key challenge that AI can help with early closed-loop systems already showing promising results.
Automated Material Characterization:
- Data scarcity remains challenging, given the cost of obtaining high-quality characterization data and associated labels in characterization settings. This creates a prominent opportunity for generating high-quality synthetic data using advanced techniques and data augmentation techniques.
- Interpretability is a major consideration in characterization to continue to enable researchers to make quicker progress in materials analysis that will help inform subsequent materials design decisions.
Overall, the 1st AI for Accelerated Materials Design workshop at NeurIPS 2022 achieved its primary objective of showcasing research and bringing together domain experts from both AI and materials science. In doing this, several insights and future directions emerged through diverse sets of discussions that aim to drive research in the community forward. Intel Labs and its collaborators look forward to continuing along this exciting research direction and pushing the state-of-the-art along the various efforts related to building AI-infused materials design systems with real-world impact.
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