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AI can reduce rare-earth use in two different ways: it can help scientists identify magnetic materials that need little or no rare earth, and it can help engineers design motors that meet their targets with less magnet material. In both cases, AI narrows the search; physics, manufacturing, magnetic testing and full-motor validation still determine whether a design works.
What AI actually changes
Rare-earth elements such as neodymium can produce magnets with very high strength and resistance to demagnetization. Those properties are valuable in compact, efficient motors, but they expose manufacturers to material cost, supply and processing risks. Replacing rare earths is therefore not a simple substitution exercise: a candidate must deliver the required magnetic strength, coercivity, temperature behavior, efficiency, power density, service life and manufacturability for a particular application.
AI is useful because the design spaces are too large for exhaustive testing. A model can rank compositions, estimate likely magnetic properties, identify promising microstructures or search motor geometries against objectives such as torque, mass and cooling. The output is a shortlist or design direction—not a finished magnet or a production-ready motor.
The Ames National Laboratory AI-driven roadmap combines fundamental physics with machine learning to advance rare-earth-free permanent-magnet discovery. Similarly, the National Research Council of Canada (NRC) describes AI and machine-learning design alongside topological optimization and cold-spray additive manufacturing. Neither source presents AI as independently eliminating rare earths.
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Path one: using AI to find different magnet materials
1. Define the application’s real requirements
Researchers first specify the operating envelope: target remanence and energy density, coercivity, maximum and minimum temperatures, exposure to opposing magnetic fields, mechanical stresses, allowable mass and volume, and the manufacturing process. A material that looks excellent on one metric can fail in a motor because it demagnetizes when hot, cannot be formed economically or requires too much of another constrained element.
2. Build physics-informed training data
Training data can combine measured magnetic properties, crystal structures, composition and processing conditions with calculations from fundamental physics. Physics constraints help prevent a model from recommending compositions that look plausible statistically but are unstable or impossible to synthesize.
3. Rank candidates and microstructures
Machine learning can estimate an upper limit for properties such as coercivity or energy-density product for a phase and help optimize microstructure. This is a computational research method, not evidence that every highly ranked candidate can be made as a dense, durable magnet. Predictions must be checked against uncertainty and then tested experimentally.
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4. Synthesize and characterize samples
Scientists still have to make the material, measure hysteresis and temperature dependence, test resistance to demagnetization, and examine defects, oxidation and mechanical integrity. Results from those tests feed back into the model, improving the next round of candidate selection.
5. Validate the magnet in its intended machine
A material-level result does not establish motor-level performance. The magnet must be evaluated in the rotor and thermal environment where it will operate, including manufacturing tolerances, control strategy, fault conditions and expected service life.
Examples of alternative magnetic materials
MnBi: a reported rare-earth-free result
An Ames Laboratory report dated April 30, 2025 describes a bonded manganese-bismuth (MnBi) magnet developed for industrial-motor use without rare earths. The laboratory reports that its coercivity—resistance to demagnetization—nearly doubles when temperature rises 100°C (212°F) from room temperature. That is a result for the reported bonded magnet property. It does not establish whole-motor efficiency, production volume, cost, or commercial deployment.
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Ames scientist and team leader Jun Cui said, “You cannot use rare earths to solve everything. That’s an awful waste of a critical natural resource.” That statement expresses a resource-efficiency viewpoint; it is not a quantitative comparison of lifecycle impacts.
Samarium-containing options
The NRC says researchers are also investigating samarium-iron-nitrogen (SmFeN) and samarium-cobalt (SmCo). These may offer different price, availability or temperature characteristics from neodymium-based magnets, but they still contain rare-earth elements. They should therefore be described as reduced-dependence or alternative options, not rare-earth-free solutions.
Ferrite and iron-nitride research
A Ghent University project record covers ferrite and iron-nitride materials, with or without electric excitation, as part of motor redesign. It also examines aluminum stator windings and recyclability. The record lists a September 2025 to September 2026 duration; it does not provide outcomes that can be treated as proof of a deployed design after that period.
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Path two: using AI to need less magnet material
The second pathway keeps a permanent magnet but changes the motor so the required rare-earth volume is smaller. The NRC combines topological optimization, AI-based design and additive manufacturing to search for lighter, more compact geometries. Additive processes can make shapes that are difficult to produce conventionally, while optimization evaluates many configurations against selected constraints.
NRC research officers Jean-Michel Lamarre and Fabrice Bernier describe the potential this way: “The potential of topological optimization combined with additive manufacturing is enormous. With these approaches, we can create new motor geometries that can significantly reduce—or even eliminate—the need for rare earth elements, cut manufacturing costs, and enhance both the power‑to‑weight ratio and overall motor efficiency.” Their wording is a statement of potential from a development program, not a universal measured outcome.
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Hybrid rare-earth and ferrite magnets
A high-energy rare-earth magnet can be combined with lower-energy ferrite so the rare-earth portion supplies only the performance that ferrite cannot. The design must account for different temperature coefficients, magnetic circuits, mechanical retention and demagnetization margins. The result reduces rare-earth mass but does not remove it.
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Consequent-pole arrangements
In a consequent-pole motor, part of the physical rare-earth magnet is replaced by an induced magnetic pole created by the iron structure. A 2021 review in IEEE Transactions on Magnetics identifies this and hybrid ferrite structures as two distinct ways to reduce rare-earth magnet volume. Consequent-pole designs can introduce torque ripple, local saturation or demagnetization concerns, so the magnetic circuit and controls must be optimized together.
Non-permanent-magnet motors
Another option is to avoid permanent magnets entirely. The U.S. Department of Energy’s motor R&D overview includes non-permanent-magnet designs, reduced-rare-earth magnets, and new motor materials among its research pathways. Induction, wound-field and reluctance architectures can remove rare-earth magnets, but they may trade away some combination of inverter complexity, rotor losses, acoustic behavior, efficiency, power density or control simplicity.
The same DOE overview notes that internal permanent-magnet motors provide high power density and maintain high efficiency over a large part of their operating range. That is why the choice is application-specific rather than a contest with one universal winner.
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- Set objectives and limits. Specify torque-speed points, peak and continuous power, efficiency map, maximum temperature, mass, envelope, noise, cooling capacity, allowable rare-earth mass and target cost.
- Choose design variables. Variables may include magnet volume and grade, pole count, rotor barriers, air-gap shape, winding layout, conductor material, lamination geometry and cooling channels.
- Run electromagnetic and thermal simulations. Finite-element or reduced-order models generate training data and expose saturation, losses and hot spots. AI can act as a surrogate model to evaluate more candidates quickly.
- Optimize under constraints. An algorithm searches for Pareto-optimal designs: for example, lower rare-earth mass without violating torque, demagnetization or temperature limits. Engineers then inspect whether the proposed geometry can actually be manufactured.
- Prototype and test. Build the rotor, stator and power electronics; measure torque, efficiency, temperature rise, vibration, demagnetization margin and fault behavior over the complete operating range.
- Check production and life-cycle factors. Evaluate yield, joining and coating processes, material availability, repairability, recycling and end-of-life separation before approving a design.
What to compare before calling a design “better”
| Criterion | Questions to answer |
|---|---|
| Rare-earth requirement | How many grams are used per motor? Is the reduction partial, or are permanent magnets eliminated? |
| Performance | Does the design meet torque, power density and efficiency targets across the complete duty cycle, not just at one operating point? |
| Magnetic robustness | What are coercivity, temperature limits and demagnetization margins under overload and fault fields? |
| Size, mass and cooling | Does a lower-rare-earth design require more iron, copper, active cooling or inverter capacity? |
| Manufacturing | Can the geometry be produced repeatedly at acceptable yield, tolerance and cycle time? |
| Economics and supply | Do substitute materials, additives or specialized processing offset the saving in rare earths? |
| Service and recycling | Can the motor be repaired, remanufactured and disassembled so its materials are recovered? |
The available sources do not provide one consistent head-to-head dataset covering all of these criteria. A claim that a design is “more sustainable” or “cheaper” therefore needs its own boundary conditions and measurements.
What AI cannot replace
- Materials synthesis: a predicted composition may form unwanted phases, oxidize or require an impractical process.
- Magnetic characterization: room-temperature magnetization is not enough; coercivity and irreversible losses must be measured at operating temperatures.
- Motor validation: rotor stress, thermal cycling, controls, vibration and faults can expose problems absent from a material coupon or simulation.
- Scale-up: laboratory samples do not prove powder supply, batch consistency, production yield or recyclability.
- Responsible comparison: eliminating rare earths does not automatically reduce total environmental impact if the substitute requires more energy, toxic processing or difficult disposal.
Where the field stands
Research programs now pursue both rare-earth-free materials and designs that use smaller quantities of rare-earth magnet. AI makes candidate selection and geometry exploration faster, especially when paired with physics models and additive manufacturing. The evidence currently supports describing these as active engineering pathways, not as proof that AI-designed rare-earth-free motors are broadly deployed.
For a specific product, the decisive evidence remains a tested motor specification: rare-earth mass, measured efficiency map, thermal and demagnetization margins, production process, cost basis and service-life data. Until those are published for the intended duty cycle, treat AI outputs as promising design guidance rather than a guarantee.
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