Materials Acceleration Platform Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence
Bibliographic record
Abstract
aterials are an essential element of advanced energy technologies.Accelerating the discovery of new materials, and the associated research required for maturing these technologies into deployment, will require a multidisciplinary and international effort that brings together a wide variety of individuals working effectively across their specialties, as well as across sector and political boundaries.It will also require a radical departure from traditional forms of discovery.The materials discovery process involves several stages summarized as conception, synthesis, and testing or characterization.Characterization encompasses the measurement of key properties of the material, followed by its incorporation into active devices to evaluate interactions with other device components and assess overall performance metrics.These steps have usually been carried out sequentially, and therefore, only a few materials can be tested at a time.Furthermore, sophisticated tools such as artificial intelligence (AI), large computational resources, and automated robotic systems have not been widely employed yet.Recognizing the challenges and opportunities associated with materials discovery, Mission Innovation established the Clean Energy Materials Innovation Challenge and hosted its first international expert deep-dive workshop in Mexico City on Sept 11-14, 2017. 1 Leading scientists from throughout the world gathered to define the challenges, opportunities, and fundamental research needs related to materials discovery.The main recommendation coming from the workshop participants is the need to develop the materials acceleration platform(s) (MAPs), which integrate automated robotic machinery with rapid characterization and AI to accelerate the pace of discovery.The deployment of the proposed 1 Mission Innovation is a global initiative comprising 22 countries and the European Union that share the goal of accelerating clean energy innovation.M aterials discovery and development crosscut the entire energy technology portfolio, from energy generation and storage to delivery and end use.Materials are the foundation of every clean energy innovation: advanced batteries, solar cells, low-energy semiconductors, thermal storage, coatings, and catalysts for the conversion, capture, and use of CO 2 .In short, new materials constitute one of the cornerstones for the global transition to a low-carbon future.The process of discovering and developing new materials currently entails considerable time, effort, and expense.Each newly discovered Computation and design, however, are only the first step in bringing novel materials to market.Materials synthesis and characterization have yet to benefit from automation and accelerated learning on a large scale.Integrating synthesis and characterization with advanced computing, machine learning, and robotics would automate the entire materials discovery process.Finally, closing the loop to create a virtuous cycle by using AI to direct experimentation and simulation for optimal learning would result in an accelerated, comprehensive, end-to-end materials innovation platform (Figure 1.1). ABOUT MISSION INNOVATION AND THE INNOVATION CHALLENGESTransforming traditional materials discovery pipelines into an integrated platform requires commitments from governments, academic research institutions, large industries, and capital providers [5].This ambitious effort fits well in the framework of MI Innovation Challenges.MI is a global initiative of 22 countries and the European Union with the goal of accelerating clean energy innovation.Participating countries have committed to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".