Forty Years and Still No Stronger Magnet

The strongest commercially available permanent magnets — neodymium-iron-boron (Nd₂Fe₁₄B), discovered in 1984 — have not been surpassed in maximum energy product (BHmax) in over 40 years. This performance ceiling limits the power density and efficiency of electric motors, generators, and actuators across transportation, power generation, and industrial applications. Worse, Nd₂Fe₁₄B magnets depend on rare earth elements (neodymium, dysprosium) whose supply is concentrated >60% in China, creating strategic vulnerability. The compositional space of multi-element magnetic materials is vast but largely unexplored — the 1984 discovery was serendipitous, and no systematic effort has mapped the landscape of possible high-performance magnet compositions.

Permanent magnets are critical components in EV motors, wind turbine generators, industrial robots, and defense systems. The global permanent magnet market exceeds $20 billion annually and is growing rapidly with electrification trends. Every increment of magnet performance translates directly to smaller, lighter, more efficient motors — a stronger magnet enables the same torque from a smaller motor, reducing vehicle weight and energy consumption. The rare earth supply chain vulnerability has been identified as a national security concern by the U.S., EU, and Japan. ARPA-E's $20M MAGNITO program explicitly aims to reinvigorate the materials science of magnets using modern computational and experimental tools.

The ARPA-E REACT program (2011) funded alternatives to rare earth magnets, focusing on substitution strategies (iron-nitride, manganese-based compounds) that achieved respectable performance but never matched Nd₂Fe₁₄B's energy product. Iron nitride (Fe₁₆N₂) has theoretical performance exceeding Nd₂Fe₁₄B but has never been synthesized in bulk form with the required crystal structure — only thin films. Manganese-bismuth and manganese-aluminum alloys work at high temperatures but have low room-temperature performance. The fundamental challenge is that the physics of permanent magnetism requires a rare combination of properties: high saturation magnetization, high coercivity, and high anisotropy, usually achievable only with specific crystal structures that are difficult to predict and harder to synthesize in bulk. Traditional materials discovery approaches screened candidate compositions one at a time, barely scratching the surface of the multi-element composition space.

Modern computational tools (density functional theory at scale, machine learning interatomic potentials) can now screen millions of candidate compositions for magnetic properties, dramatically expanding the search space. High-throughput synthesis methods (combinatorial sputtering, rapid alloy prototyping) can produce and characterize hundreds of compositions per day. MAGNITO specifically targets three-to-five-element compositions where emergent magnetic properties might arise from complex crystal structures that were previously too difficult to predict or discover by intuition. The convergence of computational prediction, high-throughput experimentation, and advanced characterization (synchrotron, neutron diffraction) creates a window for systematic magnet discovery.

A team could use computational screening (DFT or ML-based surrogate models) to identify candidate ternary or quaternary compositions with high predicted magnetization, then compare predictions against known experimental data to validate the approach. Alternatively, a team could survey the patent and literature landscape for iron nitride synthesis methods and identify the specific thermodynamic barriers to bulk Fe₁₆N₂ production. Materials science, computational physics, and condensed matter physics skills are central.