Rayca Science Compute governance
The heavy work of molecular science is planned, placed, supervised and recorded by the system itself, within the budgets and policies your team sets.
Computational chemistry and molecular AI compete for the same infrastructure. Neither is governed as part of the science.
Where R&D loses time and evidence today, and how Rayca addresses each point.
Two workloads, managed apart
Physics-based simulation needs long, tightly coupled runs on HPC clusters; molecular AI needs short, high-throughput inference on GPUs. They are typically run by separate teams, schedulers and scripts, with no common view of the campaign.
How Rayca addresses it
One control layer
Every request is routed to the resource it needs, and both workloads are planned, supervised and recorded the same way.
Allocation without scientific priority
Compute is consumed in queue order rather than by scientific value. Costly methods are applied where a cheaper one would resolve the question, and budgets are reconciled after the fact.
How Rayca addresses it
Plans checked against policy
Before any job starts, methods are costed against what they can resolve, and the plan is checked against the budgets, priorities, residency and approvals your team sets.
Silent failure in long campaigns
Interrupted jobs, unstable trajectories and unconverged calculations are often discovered late, sometimes after their results have informed a decision.
How Rayca addresses it
Supervision and recovery
Running work is monitored, restarted from its last good state when it fails, and verified for convergence before results are used.
Reproducibility and data governance
Results become hard to reproduce once software, force fields and model versions change, and specialised compute can require proprietary structures to leave controlled environments.
How Rayca addresses it
Lineage, inside your institution
Each job runs within your environment and is recorded with its inputs, versions, resources and outputs, so it can be replayed and audited.
Two kinds of compute 01
Scientists ask the question; the system decides where and how it runs.
Routing the work
Control layer
One place to plan, supervise and record
HPC clusters
Simulation · long, tightly coupled
GPU accelerators
Inference · short, high throughput
Simulation
Dynamics, free energy and quantum chemistry go to HPC.
Planning against policy 02
Compute is spent where it changes a decision, and only where it is allowed.
Planning a campaign
- Read the question
- Estimate methods
- Check policy
- Commit the plan
Policy
What must be resolved, for which molecules, and to what confidence.
Placement and scheduling 03
Throughput comes from scheduling, without moving your data.
Placement
Survey resources
Clusters and accelerators available to the program.
Supervision and recovery 04
Long campaigns finish without anyone watching them overnight.
Supervision and recovery
Progress, resources and health of every running job.
Verification
Review requested
A decision is needed, not a retry.
Model inference 05
Inference fast enough to explore and careful enough to trust.
Serving molecular AI models
Requests
Grouped into batches
Accelerator
Version pinnedLoad
The right model version is loaded onto available accelerators.
Lineage 06
Computation that stands up to scientific and regulatory scrutiny.
Lineage for every job
Inputs
Structures, molecules and parameters exactly as submitted.