Automations
This pillar focuses on R&D workflows that simulate material properties, battery chemistry, fluid behavior, and aerospace alloys more efficiently than traditional testing pipelines. Content should show how custom modeling automation accelerates candidate ranking, reduces lab iteration cost, and supports higher-confidence engineering decisions in advanced industries.
This foundational workflow orchestrates multi-physics simulation, candidate screening, and result synthesis to compress R&D cycles. The page details a custom architecture that connects HPC job schedulers, simulation software APIs, and data lakes, enabling engineering teams to evaluate thousands of material candidates virtually before committing to costly lab tests, directly improving innovation velocity and resource allocation.
Automates the initial filtering of vast chemical and alloy spaces using property prediction models and rule-based agents. The page explains how a custom workflow integrates generative design tools, databases like Materials Project, and simulation runners to rank candidates by target properties, reducing manual screening effort by over 80% and focusing experimental budgets on the most promising leads.
Deploys specialized agents to scrape, parse, and cross-validate material property data from fragmented literature, lab reports, and simulation outputs. This page covers the architecture for ensuring data quality, normalizing units, and populating a centralized knowledge graph, which eliminates manual data wrangling and creates a trusted single source of truth for downstream modeling.
Implements autonomous agents that monitor running FEA or CFD jobs, detect divergence or instability, and adjust solver parameters in real-time. The page details how this workflow reduces failed runs and HPC waste by integrating with tools like ANSYS or COMSOL, using ML to diagnose issues, and triggering corrective actions without engineer intervention.
Automates the creation and iterative refinement of simulation meshes based on geometry complexity and required accuracy. This page explains the custom logic that analyzes CAD models, applies domain-specific meshing rules, and validates mesh quality, significantly reducing setup time for complex multi-physics problems and improving result reliability.
Orchestrates large-scale parameter sweeps to model corrosion, fatigue, or thermal aging over time. The page outlines a system that manages input file generation, distributes jobs across HPC clusters, and aggregates results, enabling comprehensive lifespan prediction studies that would be impractical to run manually, thus de-risking material selection for long-lifecycle products.
Transforms raw simulation outputs into actionable insights, stress reports, and visualization dashboards automatically. This page details agents that extract key metrics, generate comparative plots, and flag areas of concern, freeing analysts from repetitive data manipulation and accelerating the decision-to-report cycle for design reviews.
Creates a closed-loop system where simulation models are automatically updated based on incoming data from physical tests or sensors. The page covers the data pipeline, discrepancy analysis, and parameter optimization agents that keep digital twins accurate, reducing the predictive error and improving the trustworthiness of simulation-led design.
Systematically explores how input uncertainties affect simulation outcomes to identify critical design variables. This page explains the orchestration of design-of-experiments, parallel execution, and global sensitivity analysis, providing engineers with robust design guidance and reducing the risk of overlooking key failure modes.
Dynamically allocates compute nodes and prioritizes simulation jobs based on project urgency, resource requirements, and cost constraints. The page details the agentic scheduler that interfaces with Slurm or Kubernetes, optimizing cluster utilization and ensuring critical R&D milestones are met without manual resource wrestling.
Automates the discovery of novel battery materials by simulating ionic conductivity, stability, and interfacial properties. This page outlines a workflow that combines DFT calculations, molecular dynamics, and ranking agents to identify high-potential electrolytes, accelerating development cycles for next-generation solid-state batteries.
Translates particle-level degradation models into full-cell lifespan predictions through automated multi-scale simulation chaining. The page explains how this workflow correlates SEI growth, stress evolution, and capacity fade, providing battery designers with rapid, simulation-based durability assessments to guide formulation and architecture choices.
Coordinates agents to simulate phase transitions, oxygen release, and crack propagation in NMC, LFP, or other cathode chemistries. This page details the integration of chemomechanical models and the automated analysis of failure pathways, helping cathode developers improve thermal safety and longevity through computational insight.
Automatically iterates through cooling channel designs, material choices, and operational setpoints using coupled thermal-electrochemical simulations. The page covers the optimization loop and CFD integration that identifies configurations minimizing hot spots, thereby enhancing fast-charge capability and safety without protracted manual trial-and-error.
Screens for high-capacity, low-expansion anode materials like silicon composites or lithium metal alternatives using generative models and property prediction. This page explains the end-to-end workflow from candidate generation to stability simulation, drastically reducing the experimental search space for next-generation energy storage.
Automates the exploration of alloy compositions for turbine blades or hypersonic skins that balance creep resistance, oxidation, and weight. The page details the CALPHAD-based simulation agents and multi-objective optimization that propose novel alloys, compressing a traditionally years-long discovery process into months.
Orchestrates cyclic loading simulations across entire assemblies to predict fatigue life and inspect critical crack paths. This page explains the automated workflow for importing CAD/PLM data, setting up damage tolerance analyses, and generating maintenance interval recommendations, reducing manual setup and improving aircraft certification efficiency.
Deploys agents to simulate and rank thousands of fiber orientation and ply sequence combinations for strength, stiffness, and weight. This page covers the integration with composite analysis software and the AI-driven search that finds optimal layups, enabling faster design of lightweight aerospace and automotive structures.
Automates electrochemical and environmental exposure simulations to evaluate coating performance on substrates like steel or aluminum. The page details the workflow that models pit initiation, coating delamination, and sacrificial anode behavior, providing rapid, virtual qualification of corrosion protection systems for marine, automotive, and infrastructure applications.
Evaluates powder flowability, melt pool dynamics, and resultant microstructure to predict printability and final part properties. This page explains the simulation chain and decision agents that recommend optimal powder compositions and process parameters, reducing costly print failures and material waste in metal AM.
Automates the virtual testing of polymer blends for target properties like tensile strength, glass transition, or chemical resistance. The page outlines the coarse-grained molecular dynamics and agentic analysis workflow that allows chemical companies to rapidly formulate new plastics, elastomers, or coatings with desired performance characteristics.
Drastically reduces the expert time required to prepare, run, and refine complex fluid flow simulations. This page details agents that automate geometry cleanup, boundary condition assignment, turbulence model selection, and result validation, making high-fidelity CFD more accessible and repeatable for design engineers.
Coordinates quantum chemistry calculations and microkinetic modeling to screen catalyst surfaces and simulate reaction networks. This page explains the workflow that identifies promising catalysts for chemical synthesis or emission control, accelerating R&D for the chemical and energy sectors by prioritizing lab experiments.
Predicts how polymers will behave in injection molding or extrusion processes by automating viscoelastic and flow simulations. The page covers the integration with process simulation software and the agents that recommend processing conditions to avoid defects, reducing setup time for new material introductions in manufacturing.
Uses COSMO-RS or other solubility simulations to automatically rank solvents for extraction, reaction, or purification steps. This page details the workflow that evaluates safety, cost, and environmental impact alongside performance, helping process chemists design greener and more efficient separation processes.
Automates the simulation of dopant incorporation, activation energy, and electronic impact in silicon, GaN, or other substrates. This page explains the DFT-based workflow and ranking agents that identify dopants for precise conductivity control, accelerating the development of advanced power electronics and semiconductor devices.
Orchestrates coupled thermal-stress simulations to analyze heat dissipation and warpage in complex chip packages. The page details the automated workflow that imports package layouts, runs parametric studies on underfill and heat spreader materials, and ensures reliability in high-power computing and automotive electronics.
Screens for low-k and high-k dielectric materials with optimal band gaps, breakdown strength, and interface stability. This page covers the simulation pipeline and analysis agents that support the semiconductor industry's push beyond silicon dioxide, enabling faster innovation for next-generation nodes.
Predicts atomic migration and void formation in copper interconnects under high current density using automated simulation suites. This page explains the workflow that assesses chip reliability, allowing designers to proactively adjust layouts and materials to prevent circuit failure in advanced semiconductor packages.
Screens oxide compositions for applications in displays, photovoltaics, and touch sensors by simulating optical and electrical properties. This page details the high-throughput DFT workflow and filtering agents that identify alternatives to indium tin oxide (ITO), reducing cost and supply chain risk for optoelectronics manufacturers.
Automates the exploration of alloy microstructures and compositions to maximize energy absorption in crash simulations. The page outlines the multi-agent system that links material models to full-vehicle crash FEA, enabling the co-design of materials and vehicle safety structures for lighter, safer automotive bodies.
Systematically evaluates viscoelastic materials and constrained layer designs to dampen vehicle noise and vibration. This page explains the automated workflow that runs frequency-response simulations, ranks material-performance trade-offs, and integrates results into full-vehicle NVH models, streamlining acoustic package development.
Coordinates thermal, structural, and wear simulations to design composite or novel alloy brake rotors that are lighter and more durable. This page details the agents that manage multi-physics constraints, helping automotive engineers reduce unsprung weight and extend component life without compromising safety.
Simulates the complex abrasion and heat generation of tire tread compounds under different road and driving conditions. The page covers the automated workflow that links material models to vehicle dynamics, enabling tire manufacturers to virtually prototype compounds for improved longevity and wet grip performance.
Orchestrates coupled thermal-structural simulations to evaluate how battery enclosures contain and withstand thermal runaway events. This page explains the automated setup and analysis that helps EV manufacturers certify safety systems faster and with greater confidence, reducing physical testing costs and time.
Automates the simulation of barrier properties, mechanical strength, and biodegradability for bio-based or recycled packaging films. This page outlines the multi-property assessment workflow that helps consumer goods companies rapidly identify and qualify sustainable alternatives to conventional plastics.
Predicts oxygen and moisture transmission rates through multi-layer polymer structures using automated molecular dynamics and diffusion simulations. This page details the workflow that ensures shelf-life compliance, allowing packaging engineers to design effective barriers with less material and fewer lab trials.
Coordinates simulations to predict the mechanical performance and environmental degradation timeline of PLA, PHA, and other biopolymers. This page explains the workflow that helps material scientists tailor compostability rates and in-use durability, accelerating the development of truly circular material solutions.
Automates the exploration of hardfacing materials and bulk alloy compositions to withstand extreme abrasion in mining and excavation. The page details the simulation agents that model wear mechanisms and propose microstructures, extending equipment service life and reducing downtime in heavy industry.
Predicts material loss in compressor and turbine blades due to sand, ash, or droplet impact using automated CFD-DEM coupling. This page explains the workflow that helps power generation and aviation engineers select coatings and materials to maintain efficiency and avoid unplanned outages in harsh environments.
Coordinates simulations for stress corrosion cracking, hydrogen embrittlement, and fatigue in valves for oil & gas, chemical, or hydrogen service. This page details the agents that evaluate material performance against complex operational envelopes, ensuring safety and reliability in critical fluid handling infrastructure.
Automates long-term creep deformation and rupture simulations for superheater tubes, turbine casings, and other high-temperature components. The page covers the workflow that integrates material databases and lifing models, enabling predictive maintenance and life-extension strategies for aging thermal power assets.
Orchestrates simulations to assess susceptibility to sulfide stress cracking (SSC) and hydrogen-induced cracking (HIC) in pipeline steels. This page explains the automated electrochemical and fracture mechanics workflow that de-risks material selection for sour oil and gas fields, preventing catastrophic failures.
Predicts spalling and subsurface fatigue life in rolling element bearings by automating contact mechanics and damage accumulation simulations. This page details the workflow that helps bearing manufacturers and heavy machinery designers select optimal steel grades and heat treatments for maximum reliability under dynamic loads.
Systematically simulates the electronic, thermal, and mechanical properties of novel 2D materials and their heterostructures. This page explains the high-throughput DFT workflow and analysis agents that accelerate the discovery of 2D materials for next-generation sensors, transistors, and composite reinforcements.
Automates the co-optimization of thermal conductivity, coefficient of thermal expansion (CTE), and density for advanced heat sink applications in electronics. The page outlines the multi-agent system that explores metal matrix composites and novel alloys, enabling the design of lighter, more efficient thermal management solutions.
Simulates fatigue failure in solder interconnects due to CTE mismatch between chips, substrates, and boards. This page details the automated workflow that runs accelerated life tests virtually, helping electronics manufacturers improve product longevity and reduce field returns in automotive, aerospace, and consumer electronics.
Automatically iterates through alloy compositions for thin-film deposition targets to maximize film uniformity, purity, and target lifetime. The page covers the simulation agents that model erosion profiles and sputtering yields, reducing the trial-and-error in developing targets for semiconductor fabrication and display manufacturing.
Predicts the optical absorption, emission, and stability of quantum dot materials through automated quantum mechanical simulations. This page explains the high-throughput workflow that helps display companies screen cadmium-free QD candidates for brighter, more color-accurate, and longer-lasting screens.
Coordinates simulations to evaluate coercivity, remanence, and thermal stability of novel hard and soft magnetic materials. This page details the agents that manage multi-scale modeling, assisting in the development of next-generation permanent magnets for motors and advanced materials for high-density data storage media.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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