Wet lab · Ground truth
Synthesis, characterization, and performance testing provide the closest read on real systems. We pair automation with sampling strategies so every experiment maximizes information return.
AI for Chemistry & Materials
Accelerate how the world understands and creates matter
We focus on the frontiers of chemistry and materials, uniting generative AI, first-principles simulation, and high-throughput experimentation in one decision framework—so teams can move from discovery to validation in far less time than legacy trial-and-error cycles allow.
5th paradigm · ECML
Experiment, compute, and machine learning—governed as one system
R&D has long been split between slow, precise wet labs, compute that cannot span every scale, and pure data models that struggle to generalize. ECML (Experiment–Compute–Machine Learning) uses an AI decision layer to orchestrate wet experiments, high-throughput simulation, and machine learning—trading off accuracy, speed, and cost in real time and moving the core workflow from intuition-led trial and error to predict–support with physics–validate in the lab.
High-throughput compute · Mechanism & scale
From DFT and molecular dynamics to multiscale workflows—within feasible time and budget—to expose pathways, intermediates, and property limits that constrain and explain AI models.
Machine learning · Seconds-scale prediction
Graph networks, generative diffusion, Bayesian optimization, and active learning accelerate structure generation, property prediction, and formulation search—fed by compute and lab data in a continuous loop.
ReactiveAI
End-to-end intelligent R&D—from reaction design to high-throughput validation
The platform integrates reaction generation, accurate simulation, broad screening, real-time decisions, reaction-network analytics, and model-driven experimental factories across the generate → screen → synthesize path—built on proprietary algorithms, scaled first-principles workflows, and industrial-grade data.
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ReactGen · Generative reaction AI
Generative modeling for transition states and reaction pathways—compressing searches that once took many hours of quantum chemistry into second-scale sampling for retrosynthesis and catalysis design.
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Reactify · Precision compute routing
Transfer learning and recommendation policies for DFT methods and basis sets—cutting error and wasted cycles so high-throughput results align better with experiment.
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ReactBO · Large-scale screening
Bayesian optimization and active learning for multi-objective discovery across millions of candidates—balancing spectra, stability, cost, and more.
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ReactControl · Live decisions
Real-time trade-offs between cost and information gain on compute and lab pipelines—feasibility, route choice, and resource scheduling with fewer dead ends.
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ReactNet · Automated reaction networks
Transition-state search, reaction prediction, and ML force fields at relative low cost to map deep reaction networks, key intermediates, and low-barrier paths for complex mechanisms and scale-up.
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AI Materials Factory · High-throughput lab
Model-driven automation fusing formulation, conditions, equipment parameters, and characterization—closing the loop into models and process knowledge bases.
Agent Mira
A chemistry–materials research agent—natural language as the control surface
Agent Mira (Material Innovation by Reactive Agent) couples in-house models, curated datasets, and open scientific stacks for molecular generation, property simulation, formulation modeling, and literature or database retrieval. Describe goals in plain language and receive executable workflows, verifiable compute, and structured reports—with multi-agent roles for research, coding, simulation, and documentation, lowering the barrier to specialized software and orchestration.
Cross-scale delivery
Molecular and structure design, high-throughput screening, condition and by-product prediction, synthesis and catalyst recommendations, materials performance with interpretability, and multi-objective experiment planning.
Trusted tool orchestration
Strict interfaces and modular integrations—designed for low hallucination risk and reproducibility—turn data and compute into standardized, scalable R&D output.
Unified with ReactiveAI
The agent is the front-end and task hub; the backend schedules the full ReactGen–Factory stack from problem framing to experimental feedback.
Engagement
How we work with partners
We serve materials, chemicals, energy, consumer, and pharma-adjacent R&D organizations with flexible scope—from targeted algorithm modules to the full platform and agents—including joint programs with milestone-based delivery.
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Platform rollout
From use-case discovery to toolchain deployment: environments, services, data and model components, and agent applications that move your lab toward the fifth paradigm.
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Joint R&D
Focused programs on specific materials or reactions, combining ReactiveAI, Mira, and high-throughput lab capacity for measurable outcomes in mechanism, modeling, and iteration.
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Deployment options
Public cloud for fast start and elasticity; hybrid for data residency and performance; on-premises for strict compliance and deep integration.
Industries
Where we apply the stack
The platform has reusable playbooks across fine chemicals, consumer ingredients, batteries and electrolytes, functional polymers, and C1 chemistry—covering molecular design, reaction networks, formulation, and process scale-up.
Nutrition, beauty & home care
Reaction-path and condition design for fragrance molecules; efficacy ingredient design, simulation, and formulation iteration for leading ingredient and brand pipelines.
New energy & electrochemistry
Battery materials and electrolytes: interfacial modeling, formulation screening, and performance prediction—including agent-driven R&D workbenches that shorten iteration cycles.
Advanced materials & polymers
Route optimization and exploration of high-cost feedstock alternatives; formulation development for epoxies and functional blends balancing performance and cost in demanding applications.
C1 chemistry & catalysis
Catalyst and condition optimization for paths such as CO₂ utilization—pairing mechanistic simulation with experiment to improve selectivity and stability.
About
One company. One operating entity.
Beijing Shenzhun Technology Co., Ltd. is the legal entity behind this site and our customer-facing programs in AI for chemistry and materials. Our team combines researchers and product engineers from leading universities and global technology and materials companies—bridging AI4Science, quantum chemistry, high-throughput automation, and industrial deployment.
法定名称：北京深准科技有限公司
Every experiment and every simulation should land on the hypothesis most worth validating next.
Contact
Get in touch
For partnerships, joint R&D, platform access, or vendor verification, reach us through the contacts below.
- Legal entity
- Beijing Shenzhun Technology Co., Ltd. （北京深准科技有限公司）
- Office address
- Beijing, China — replace with registered or primary business address
- Phone
- 0571-87162185
- [email protected]