Matlantis has announced the launch of Matlantis CSP (Crystal Structure Prediction), a new capability within its universal atomistic simulator that identifies stable crystal structures across large atomic and compositional search spaces. The technology is intended to support materials development for electric vehicles (EVs), energy storage, and other advanced energy systems, where performance and manufacturability are closely tied to material structure at the atomic level.
Terumi Furuta, Assistant Chief Engineer, Device Process Innovative Research Excellence, Honda R&D; Mitsumoto Kawai, Chief Engineer, Device Process, Innovative Research Excellence, Honda R&D Co., Ltd.; Haruyuki Matsuyama, Ph.D., Staff Engineer, Device Process Research, Innovative Research Excellence, Honda R&D Co., Ltd.
Materials development for EV batteries, power electronics, and related systems has traditionally relied on repeated synthesis and testing cycles, even when the probability of success is low.
Matlantis CSP introduces a computational screening step earlier in the research process, allowing teams to rule out physically implausible structures and focus experimental efforts on more promising candidates.
Honda R&D is adopting Matlantis CSP to improve exploration efficiency in materials development, including multi-component systems and metastable structures that have historically been difficult to evaluate due to computational cost. (View case study here.)
Addressing limitations of conventional crystal structure prediction
Conventional crystal structure prediction methods face several constraints, including long evaluation times using density functional theory (DFT), biased searches in variable-composition systems, and complex setup requirements for large-scale runs. Matlantis CSP is designed to address these challenges by combining Matlantis’ machine-learning interatomic potential, PFP (Preferred Potential), with proprietary search algorithms and a parallel processing framework optimized for large-scale CSP workloads.
Key capabilities include high-throughput structure evaluation in seconds to minutes per configuration, efficient exploration across full composition spaces while maintaining structural diversity, and parallel execution optimized to handle tens of thousands of trials without extensive environment configuration.
“Being able to narrow down promising crystal structures and compositions with high confidence before experiments will not only increase the probability of realizing next-generation materials but also shorten development timelines,” said Mitsumoto Kawai, chief engineer, device process, Innovative Research Excellence, Honda R&D Co., Ltd.
Across early applications, Matlantis CSP has produced results in oxide, alloy, and phosphide systems, identifying more than 10 previously unknown stable crystal structures. In the Ga–Au–Ca system, the tool identified 13 new crystals, resulting in a revised phase diagram compared with existing reference data.
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