Reinventing Organic Redox Flow Batteries with Microsoft Discovery
Discovery Bookshelf preserves negative scientific results to guide better hypotheses. Discovery Engine navigates imprecise computational tools, adapting to real-world scientific workflows. Discovery proposed a novel organic negolyte validated through in-lab measurements. At Microsoft Build 2026, we highlighted how Microsoft Discovery designed a novel organic replacement candidate intended to reduce reliance on vanadium, a critical mineral, in grid energy storage. This demonstration integrated AI, computational machine learning models, and laboratory feedback within a closed-loop discovery system. In this blog post, we explore how Discovery Engine with CLIO mode uniquely enabled this effort. This was our first end-to-end validated scientific result with in-lab measurements, therefore worth a deeper dive into the surprising methods that Discovery used to get the complete scientific outcome. This work highlights an important shift occurring across scientific AI. Generating ideas is no longer the primary bottleneck. Generative models can propose large numbers of chemically plausible compounds, leverage computational predictors to rapidly estimate properties such as redox potential, solubility, or synthesizability, and interpret experimental results. The new bottleneck arises in alignment of these models with scientific objectives: efficiently scheduling in silico and wet-lab experiments, effectively leveraging failures to shape future exploration, and enabling accumulation of knowledge across a multitude of design decisions over time. Scientific objectives are often hard to express as clean optimization problems. The imprecision of predictive models and divergence of computational estimates from experimental reality are just two examples of why defining a complete fitness function is not possible. Results which maximize fitness are often found lacking when examined by domain experts due to design requirements and preferences that are difficult to express numerically. For this campaign, we used Discovery Engine with CLIO mode enabled, and Discovery Bookshelf, built on GraphRAG to evolve and improve a benzo[c]cinnoline scaffold. The integration of language models into the design process allows these less rigid requirements to be expressed and considered as first-class priorities alongside numerical objectives. Microsoft Discovery Engine leverages Bookshelf’s knowledge graph representations to guide the design process, enabling multiple agents to coordinate a shared understanding of what is known with what level of confidence. This shared understanding is crucial to 1) escaping local minima by considering a global view of the task and 2) learning from negative results. Empowered by the combination, we designed a novel organic negolyte in partnership with Yale Engineering, Canam Bioresearch, and Pacific Northwest National Laboratory (PNNL). Closed-loop discovery workflow using Discovery Engine to guide molecular design using computational property predictors, wet-lab feedback, and Discovery Bookshelf to preserve both positive and negative results that inform each new design cycle. Turning Scientific Exploration into Durable Knowledge Scientific publication is shaped by positive-results bias: studies reporting statistically significant or otherwise favorable findings are more likely to be published than studies reporting null or negative results. In practice, however, exploration is dominated by unsuccessful attempts, both in silico and in the laboratory, and these negative results contain valuable information about the studied system. In computational design campaigns, such negative results often arise from in silico predictions deviating from experimental reality; properly capturing experimental feedback to inform subsequent designs under a constrained search space is crucial. Knowing where not to look is as important as knowing where to look next. Hence, capturing and preserving this evidence in a temporal, thematic structure is essential to scientific progress. Negative results become more valuable when they are retained as scientific state. Instead of fragmenting across notebooks, reports, conversations, institutional memory, and compressed context, structured records allow successes and dead ends to accumulate across themes and campaign rounds. Agents for science are highly capable of collecting information by orchestrating scientific tools, iterating through many designs, and identifying candidate solutions. The challenge is preserving this knowledge in a durable form that can readily re-align given far sparser experimental results to influence future decisions. In the absence of a dedicated structure for knowledge retention, valuable information is lost to context window compression, a critical but lossy technique for maintaining agent performance on long duration tasks. The scale of explorations and hypotheses increases rapidly over the course of a design campaign. This growth compounds further when scaled beyond a single campaign and rejected hypotheses. Negative results and abandoned pathways become fragmented across notebooks, reports, conversations, and institutional memory. As agentic science expands the scale and speed at which research can be accomplished, organizations have an unprecedented opportunity to capture and transform negative results from a fragmented form into durable institutional memory. Bookshelf as a Scientific State Mechanism Bookshelf was developed to represent the arc of science as a first-class object. Rather than representing only candidate molecules that were generated, we sought to maintain relationships between hypotheses, evidence, predictions, measured outcomes, unresolved contradictions, supporting rationale, and confidence over time. As such, through the evolution of Discovery Engine’s exploration in modifying the original seed scaffold, learnings were adapted and brought forward. The prime example of such interaction is in the figure below, where Engine self-organized the molecular design task into three rounds. The first and second design rounds emphasized exploration. Discovery Engine proposed a diverse collection of molecular modifications intended to probe the behavior of the benzo[c]cinnoline scaffold. This was both to identify the best candidate and to establish an empirical map of how structural changes influenced target properties. Discovery Engine organizes an ORFB molecular campaign from broad exploration to targeted follow-up. Across three rounds, measured solubility and redox-potential outcomes—including predominantly negative results—are retained in the Discovery Bookshelf and used to guide the next set of benzo[c]cinnoline derivatives. Learning from Failure Many structures designed across the first two rounds were found to be non-viable, yielding valuable negative results. Importantly, Discovery Bookshelf captured the learnings from these design rounds, preserving the arc of reasoning constructed while exploring the candidate’s modification space. The second round provides a particularly revealing example. None of Discovery Engine’s parallel explorations using CLIO mode produced a candidate suitable for advancement. Yet the round was scientifically productive. Its negative results revealed limitations in the tooling and preserved evidence that could inform the next round. Discovery Bookshelf retained those lessons, enabling four of the six explorations in the third round to succeed. This contrast demonstrates a central principle of good science: negative evidence creates value when it changes the next decision. The seed candidate, tools, compute, and problem boundary conditions were unchanged between the two rounds; what changed was how Discovery Engine interpreted the accumulated evidence and adapted its workflow for analyzing candidate options. Had only successful explorations been retained, the third round would likely have repeated the second round’s failures. Capturing, storing, and cataloging these changes in scientific judgment transforms campaign-specific lessons into reusable organizational assets. Calibrating Imperfect Predictors The preserved results also revealed why the third-round workflow needed to change. By comparing the property predictor with the limited experimental evidence, Discovery Engine identified a systematic mismatch: the redox potential predictor was not numerically accurate, but it remained directionally useful for ranking candidates. Rather than discarding the model, Discovery Engine recalibrated its trust in the predictor, treating predicted values as relative signals instead of precise estimates. This shift from trusting the predictor’s absolute values to using it as a ranking instrument changed the design workflow and enabled Engine to extract value from an imperfect scientific tool. Thematic representation and accumulation of this knowledge through Discovery Bookshelf allowed Discovery Engine to move beyond optimizing individual candidates in isolation and instead evolve the design campaign itself. In the third round, it produced a targeted set of candidate structures informed by both the most promising identified structure and a broader understanding of the design space. In any scientific organization, the ability to preserve and reuse an entire history of positive and negative results becomes increasingly important as search spaces expand. From Scientific Memory to Organizational Memory While we demonstrate Discovery for molecular discovery, the underlying principle and combination of Bookshelf as a semantic map for Engine extends beyond chemistry. Research organizations, engineering teams, and businesses all face the same challenge. Every major initiative generates large numbers of unsuccessful paths that never become final products, published papers, or approved strategies. Yet these discarded paths frequently contain valuable information that informs future decision making. Most organizations are optimized to preserve outcomes, yet few are optimized to preserve the evolution of belief that produced them. As a result, teams revisit previously explored directions, rediscover old constraints, and relearn lessons that have already been paid for. The challenge facing organizations is remarkably similar to the challenge facing scientific discovery. Both require a way to transform trajectories of exploration into durable memory. In combination with Microsoft's broader AI stack, this becomes an immensely powerful representation for agents and humans operating and capitalizing on increasing organizational value in the reverse information paradox. Co-authors Jake Smith • Karin Strauss • William Chappell
