Material Synthesis Graph Scheduling for Parallel Hardware Utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing material synthesis methods are inefficient in utilizing artificial neural networks (ANNs) to predict chemical reactions and synthesize new compounds, as they lack a systematic approach to optimize hardware device utilization and parallel execution of operations.
Innovation Solution
A material synthesis device and platform that utilizes a recipe translator to generate a graph script representing antecedent-consequent relationships between operations, and a graph scheduler to monitor hardware availability and schedule commands in parallel, optimizing the execution of chemical synthesis processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sequential execution methods are used for chemical synthesis operations, then the system is simple to implement, but the productivity is low due to inability to perform parallel operations
Solution Approach 1:
The chemical synthesis recipe is segmented into discrete operations (e.g., reagent dispensing, mixing, heating) that can be independently scheduled and executed in parallel. Each operation is represented as a separate task in the graph scheduler, allowing optimal parallelization while maintaining overall process coherence.
Solution Approach 2:
The system performs preliminary analysis of the chemical synthesis recipe to identify operations that can be executed in parallel. The graph scheduler pre-calculates the execution timeline and resource allocation, preparing the execution plan before actual hardware operation begins.
2Productivity
If hardware devices are allocated without monitoring availability, then the scheduling process is simple, but the hardware utilization efficiency deteriorates
Solution Approach 1:
The graph scheduler continuously monitors the availability status of hardware devices and uses this feedback information to dynamically adjust the execution schedule. When a device becomes available, the scheduler identifies ready operations and assigns them to the now-available hardware, optimizing resource utilization in real-time.
Solution Approach 2:
The scheduling system transitions from static hardware allocation to dynamic resource assignment. The graph scheduler adapts the execution plan based on real-time hardware availability, allowing flexible reassignment of operations to optimize throughput and reduce idle time.
3Reliability
If operations are executed without considering antecedent-consequent relationships, then the execution speed is fast, but the synthesis process reliability deteriorates
Solution Approach 1:
The synthesis process is divided into ordered operational segments where each operation has defined antecedent (prerequisite) and consequent (dependent) relationships. The graph scheduler respects these dependencies while maximizing parallel execution, ensuring reliability through proper sequencing without unnecessary sequential delays.
Solution Approach 2:
The system performs preliminary analysis of operational dependencies to construct an accurate execution graph. This pre-computed dependency structure allows the scheduler to immediately identify which operations can run in parallel and which must wait, optimizing execution time while maintaining process reliability.
Data Source
AI summary
Provided is a material synthesis device, including at least one processor configured to be implemented as a recipe translator configured to, based on analyzing a chemical synthesis recipe, generate a script in the form of a graph representing an antecedent-consequent relationship between target operations corresponding to the chemical synthesis recipe, and a graph scheduler configured to monitor availability of hardware devices of a material synthesis platform, and schedule and execute in parallel commands satisfying the antecedent-consequent relationship in the script based on the monitoring.