Powered by the upgraded RSI engine, the platform unifies data, models, experiments and production to accelerate materials innovation
BEIJING, Oct. 26, 2026 /PRNewswire/ — Fabarta (Beijing Fengqing Technology Co., Ltd.), a leading provider of enterprise-grade AI agents and knowledge engines, today announced a major strategic upgrade to its AI for Science (AI4S) platform: the official launch of Fabarta Science Intelligent—the platform’s newly upgraded core engine, driven by RSI (Recursive Self-Improvement)—alongside the evolution of AI4S into AI4S&E (AI for Science & Engineering), which now covers the core chain of R&D and production. The upgrade marks a comprehensive enhancement of Fabarta’s AI4S platform, equipping it with frontier capabilities including multi-agent collaboration, multiscale model self-evolution and physics-informed AI hybrid modeling. The platform now connects the entire chain—from literature review and formulation design to dry- and wet-lab experimental validation, digital pilot-scale scale-up and industrial mass production—moving toward continuous self-iteration and accelerating the shift of materials R&D from traditional trial-and-error empiricism to a new paradigm driven by AI and knowledge.
Meanwhile, as an important milestone in advancing the company’s global expansion and capital market presence, Fabarta has been invited to join the first batch of “Hong Kong Top Talent Ecosystem Partners.” Founder and CEO Andy Gao and Co-founder and COO Michelle Ge will lead a delegation to attend Asia’s largest fintech event—Hong Kong FinTech Week × StartmeupHK 2026—on November 2, where they will receive official plaque at a dedicated session and showcase the breakthrough achievements of Fabarta’s AI4S platform and enterprise-grade AI Agents to over 45,000 global industry leaders.
From Single-Point Tools to Full-Chain Integration: The AI4S Platform Takes a Leap with RSI
As an AI leader deeply engaged in the fine chemicals and new materials sectors, Fabarta’s earlier AI for Science platform had already demonstrated notable results in scenarios such as intensive literature reading, data management and single-point algorithm-based prediction. Yet in the “deep-water zone” of the materials industry—moving from the laboratory to the production line—conventional AI4S tools often remain at the level of “single-point computing” or “assisting with paper publication.”
To address this bottleneck, Fabarta systematically iterated on its AI4S platform, deeply embedding its self-developed RSI intelligent architecture into the platform foundation. Through its “Four Modules” and “Three Loops,” the upgraded AI4S platform is now capable not only of literature Q&A and molecular generation, but also of adaptive model evolution, multiscale physical simulation and digital-twin calibration—accelerating the leap of Fabarta’s AI4S platform into an “industry-grade, integrated R&D-production platform with self-iteration capability.”
Breaking Two Long-Standing Bottlenecks in Materials R&D: Multi-Objective Balancing and Data Fragmentation
New materials R&D has long been constrained by two bottlenecks: balancing competing multi-objective performance targets—such as the trade-offs among heat resistance, impact toughness, processability and cost—and the severe fragmentation of R&D and production data. Heat-resistant ABS resin is a case in point: introducing heat-resistant monomers usually comes at the expense of impact strength, while increasing rubber content restores toughness but lowers heat resistance. Meanwhile, critical clues scattered through historical literature, patents and “anomalous” batches remain siloed across departments and systems, making it difficult to establish a continuously operating “question–verify–adjust” loop.
“Fabarta has always been committed to building the industrial infrastructure of AI4S. The core of this major upgrade to our AI4S platform is the introduction of the RSI intelligent engine, which deeply integrates large models with our proprietary knowledge engine. Integrating AI into industrial upgrading cannot rely on a single general-purpose large model alone—AI must be embedded in the physical mechanisms and knowledge chains of the industry. We hope to help research and industrial teams worldwide, through the upgraded AI4S platform, connect the intelligent full chain spanning dry- and wet-lab bench-scale experiments, base pilot-scale trials and enterprise mass production, so that AI can create measurable industrial value for materials innovation.” — Andy Gao, Founder and CEO of Fabarta
RSI’s Core Operating Architecture: Four Modules Powering a Three-Layer Iterative Loop
Within the RSI platform, every materials research project is carried by four tightly integrated modules—Project, Session, Experiment and Resource Center—and driven by three layers of deep iterative loops that carry R&D across the boundary between the digital and the physical world:
P0 Validation Loop (precise hypothesis validation, real-time model evolution): Within a single project session, the research agent draws on historical formulations and predictive models consolidated in the knowledge graph to rapidly generate testable hypotheses. Built on a modeling framework combining data-driven models with physical constraints, the system introduces neural operators in scenarios with sufficient data and computing power, progressively accumulating reusable and transferable domain models and operator components. Whenever new experimental data passes quality inspection and flows back to the knowledge graph, the system identifies model weaknesses through predictive deviation, physical consistency, and uncertainty evaluation, adaptively triggering incremental training, local model updates, or parameter calibration. Updated models are deployed after independent data validation, version management, and researcher review, forming the P0 self-evolving loop of “intelligent hypothesis → autonomous experiment → data feedback → operator evolution → intelligent re-exploration.”P1 Domain Loop (cross-disciplinary deep exploration, multiscale transfer): Once initial leads and base surrogate models pass validation, RSI’s cognitive agent schedules an “exploration-exploitation” coordination strategy, breaking down R&D goals into a cross-scale digital experimental chain spanning molecular design, performance prediction, and formulation optimization connected by specialized agents. Based on high-precision surrogate models, the system conducts multi-objective global optimization within physical and engineering constraint boundaries established by researchers, performing cascade pruning on candidate solutions according to confidence and robustness stability to significantly narrow the scope of high-cost wet experiments and complex simulations. Research dimensions extend step-by-step to heat-resistant monomer polar ratios, rubber phase micro-particle topological distribution, processing additive synergy windows, as well as extreme performance such as low-temperature impact toughness and long-term aging evolution. During cross-system and cross-scale transfer, the system precisely defines generalization boundaries through few-shot active learning and model residual calibration, seamlessly connecting data and knowledge reuse across formulation screening, performance validation, and process optimization.P2 Industrial Loop (from laboratory to production line, a digital-twin closed loop): Even after an excellent formulation is validated in the laboratory, performance deviations often emerge when it is scaled up in reactors and twin-screw extruders or moved into plant production, owing to factors such as shear forces and raw-material batch variability. RSI extends the second-level prediction capability of accumulated cross-scale domain models to the production environment, while real-time operational data (temperature, pressure, torque, shear history, product quality) generated by DCS/MES systems during production are continuously fed back into the R&D knowledge graph. Through industrial-data-driven operator retraining and model-deviation diagnosis, the platform achieves dynamic digital-twin calibration. By connecting real operating-condition feedback from the production side and channeling pilot- and production-scale data back to the R&D side for model correction, the P2 industrial loop bridges the “valley of death” between the laboratory and the plant floor.
Addressing LLM Hallucination: Deep Fusion of Physical AI and the Fabarta Context Graph
Unlike conventional tools that rely on the “probabilistic guessing” of general-purpose large models, the RSI platform is built on Fabarta’s core Context Graph (enterprise knowledge graph) and physics-informed neural operator (PINO) models, ensuring rigor and traceability in scientific decision-making:
Precise traceability and knowledge consolidation: RSI organizes raw monomers, formulation parameters, test results, phase-morphology images, patent literature and researchers’ reasoning hypotheses into a unified, structured knowledge network. Researchers can trace across projects (for example, “retrieve the historical rubber particle-size characteristics of formulations that introduced N-phenylmaleimide while maintaining toughness”), and every conclusion is backed by explicit source data—eliminating the “model hallucination” of general-purpose AI in rigorous scientific domains.Hybrid physics-mechanism and AI modeling: The platform integrates equation-of-state, reaction-kinetics and simulation capabilities, embedding physical laws—such as conservation laws and constitutive relations—into operator training through a combination of soft and hard constraints, so that models remain physically consistent in data-sparse regions and deliver credible extrapolation. Computation becomes conversational, reasoning steps are visualized, and intermediate results can be re-examined.Bidirectional reinforcement between knowledge and models: The knowledge graph provides PINO with structured priors and training corpora, while PINO’s predictions and discoveries are written back to the graph as “knowledge with confidence levels” and, once confirmed through experiments, consolidated into deterministic knowledge. The graph and the models feed each other and co-evolve, forming an enterprise-exclusive, non-replicable R&D intelligence asset.
Advancing the Closed-Loop Deployment of AI4S Across Industry, Academia, Research and Application
To date, Fabarta’s AI4S solutions have been successfully deployed at leading central state-owned enterprises, national-level chemical industrial parks and multiple listed new-materials companies. Through joint laboratories, integrated dry- and wet-lab experimentation, and pilot-scale bases operated by government or enterprises, the RSI platform is accelerating the commercialization of catalyst optimization, polymer design and fine-chemical process transformation.
“In real industrial scenarios, AI must answer two questions: explainability and the cost of deployment. Through ‘Graph Fine-tuning’ and ‘standardized components + scenario-specific configuration,’ the RSI platform not only lowers the trial-and-error cost for enterprises adopting AI, but also elevates AI from mere literature Q&A to an AI R&D and production team covering molecular design, digital pilot-scale trials and energy-saving, cost-reducing production.” — Andy Gao, Founder and CEO of Fabarta
Looking ahead, Fabarta will continue to center on research and production, further deepening multi-agent collaboration, physics-informed AI hybrid modeling and its matrix of vertical-domain large models, and remains committed to providing highly reliable, evolving enterprise-grade AI agent infrastructure for chemical-energy, new-materials, advanced-manufacturing and biopharmaceutical companies worldwide.
Deepening Greater Bay Area and Global Expansion: Invited as the First Batch of “Hong Kong Top Talent Ecosystem Partner” to Debut at Hong Kong FinTech Week
Leveraging its strong technological moat in enterprise-grade AI Agents and AI4S, as well as its outstanding achievements in embedding AI into industrial upgrading, Fabarta has been officially invited to become one of the first batch of “Hong Kong Top Talent Ecosystem Partners.”
From November 2 to 6, “Hong Kong FinTech Week × StartmeupHK 2026,” jointly organized by the Financial Services and the Treasury Bureau, the Commerce and Economic Development Bureau, and InvestHK of the Hong Kong SAR Government, will take place at the Hong Kong Convention and Exhibition Centre. Fabarta Founder and CEO Andy Gao and Co-founder and COO Michelle Ge will travel to Hong Kong to attend the summit, where they will accept the official certificate of appreciation for the “Hong Kong Top Talent Ecosystem Partner” at the “Top Talent Forum Special Session” on November 2.
At this flagship international event focusing on Financial AI, enterprise-grade AI Agents, and cross-border compliance, Fabarta will leverage its upgraded AI4S&E platform and Enterprise Knowledge Platform to engage in depth with representatives from government, industry, academia, research, international investment institutions, and industry leaders across more than 120 economies. Using Hong Kong as a vital strategic hub, the company will accelerate cross-border deployment and global market expansion, bridge with the Greater Bay Area and international capital markets, and drive the thriving development of the AI Agent ecosystem in global deep-tech sectors.
About Fabarta
Fabarta Co., Ltd. is an industry-leading provider of enterprise-grade AI agents and knowledge engines. Guided by its dual-engine strategy of “knowledge engine + large model,” the company leverages its proprietary Graph Fine-tuning and physics-informed AI hybrid modeling technologies to solve the hallucination and inexplicability problems of general-purpose large models in industrial domains. Fabarta is deeply engaged in trillion-yuan-scale industries including fine chemicals, new materials and advanced manufacturing, and has built a complete AI4S deployment loop spanning “laboratory bench-scale research, digital pilot-scale trials and industrial mass production.” The company is committed to helping manufacturing customers build smart factories based on four-dimensional ontology and RSI technology, closing the loop from research to production. As a strategic partner of leading value-chain anchor enterprises, Fabarta has achieved standardized, at-scale replication of more than one hundred mature agents at national-level chemical parks and among industry leaders, dedicated to embedding AI into the core value chain of the real economy and creating measurable industrial value.
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SOURCE Fabarta Co., Ltd.
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