High-Entropy Materials Patents: Who Leads, Where the Gaps Are 2026
- One filer holds 17 of 32 records in scope and the top five combined account for 90.6% of the field — this is a landscape with a dominant leader, not a crowded one.
- Filing peaked in 2020 at 8 records then fell; the 2021→2024 span shows a -100% change, though 2025 onward is still filling in as publications lag filing.
- Computing overlaps materials science more than metallurgy does G06F and G16C classes cover 40.6% and 37.5% of records respectively, ahead of C22C alloys at 12.5%.
Filing growth compares 2021 (3 records) with 2024 (0) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 32 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This review covers 32 published patent records matching high-entropy and multi-principal-element alloy claims combined with material-property, synthesis-condition, or structure-property terms. The scope is deliberately narrow: it captures filings where alloy composition work is paired with characterization, prediction, or interface data rather than alloy chemistry alone. That intersection is why the dataset skews toward computational classes as much as metallurgical ones.
Coverage runs from 2015 through the 2026-08-31 cut-off. Because publication typically lags filing by around 18 months, records from 2025 and 2026 understate actual filing activity for those years and should not be read as a slowdown.
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Filing trend and technology composition
Two views of the same 32 records: how filing activity has moved year over year, and which IPC subclasses the claims actually sit in.
A single peak year, then a drop-off that predates the publication lag
Filing rose to a peak of 8 records in 2020, then declined; the 2021-to-2024 span — the most recent window that can be treated as complete — shows a -100% change. Years after 2024 are still filling in as publications catch up to filing dates, so they should not be read as confirming or reversing that decline.
Computation and prediction classes outweigh alloy-composition classes
G06F (electric digital data processing) appears in 40.6% of the 32 records and G16C (computational chemistry) in 37.5%, both ahead of C22C (alloys) at 12.5% and B22F (powder metallurgy) at 9.4%. Because records can carry multiple IPC classes, these shares add up to more than 100% of the record total — the pattern to read is that most filings pair an alloy system with a modelling or prediction layer, not that alloy chemistry itself is under-represented.
Shares are the percentage of the 32 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on High-Entropy Materials Patent Landscape with Eureka
This page is one run against one query. Ask Eureka your own question about high-entropy materials patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records in this dataset
Systems and methods for predicting structure and properties of atomic elements and alloy materials (US20200066376A1)
Metallic alloy development has traditionally relied on experimental or theoretical equilibrium phase diagrams. Synthesis, processing and mechanical testing of samples demand heavy investment in time, money and equipment, and conventional Calphad-type calculations alone do not resolve local structure and related property prediction well. This filing describes simulation systems combining molecular dynamics with accelerated Monte Carlo techniques to predict structure evolution and material properties.Filed by Tata Consultancy Services Limited, published 2020-02-27.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200257933A1 | Machine Learning to Accelerate Alloy Design | 187 |
| 2 | US20220067249A1 | Machine Learning to Accelerate Design of Energetic Materials | 80 |
| 3 | US20200066376A1 | Systems and methods for predicting structure and properties of atomic elements and alloy materials | 29 |
| 4 | EP3614389A2 | Systems and methods for predicting structure and properties of atomic elements and alloy materials thereof | 13 |
| 5 | US20210202116A1 | Nuclear fuel elements including protective structures, and related methods | 12 |
| 6 | US11915105B2 | Machine learning to accelerate alloy design | 8 |
| 7 | US20220374721A1 | Systems and methods for design of application specific functional materials | 5 |
| 8 | US11562807B2 | Systems and methods for predicting structure and properties of atomic elements and alloy materials | 5 |
| 9 | WO2021051078A1 | Methods for and devices prepared from shape material alloy welding | 4 |
| 10 | EP4071657A1 | Systems and methods for design of application specific functional materials | 3 |
Citation counts inside a searched corpus favour older filings; read this as a signal of influence on later work, not of current commercial relevance.
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Three read-outs from the ranking, the trend, and the citation table.
This is a leader-and-tail field, not a crowded one
With 8 assignees accounting for all 32 records in scope, and one filer alone holding 17, most of the claim space that exists has already been staked by a small group. A new entrant is negotiating around a handful of portfolios, not hundreds of scattered filers.
Activity has not sustained its 2020 peak
Filing reached 8 records in 2020 and then fell; the 2021-to-2024 window, the most recent period that can be treated as complete, shows a full reversal. Filings from 2025 onward are still arriving as the publication lag closes, so this should not yet be read as the field's final trajectory.
Prediction and computation classes lead alloy-composition classes
G06F and G16C together outpace C22C and B22F, meaning the densest claim activity is in modelling, machine learning and computational chemistry applied to alloy design rather than in alloy composition itself. That leaves composition-only claims comparatively less crowded.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to high-entropy materials patent landscape, with the prior art for and against each one.
Who holds the claim space
Eight assignees account for the entire dataset, with filing weight concentrated at the top rather than spread evenly.
One filer dominates the ranked set
The leading assignee holds 17 of the 32 records in scope — more than half the field by itself. That scale suggests a portfolio built around a core method (prediction and simulation approaches feature heavily in this filer's most-cited records) rather than a single alloy composition.
No assignee is currently accelerating
Recent-year momentum data shows the tracked assignees, including the leading filer, at 0 new records in the latest year, with a -100% year-on-year change recorded for one filer. Given the publication lag, this reads as a gap in visible activity rather than confirmed withdrawal from the field.
The tail is thin, not absent
Beyond the top five, the remaining assignees in the ranked set hold small single-digit counts each. That leaves room for a new filer to establish a position without displacing an incumbent outright, provided the claims target an under-served branch rather than the leader's core method.
| Assignee | Recent year | YoY |
|---|---|---|
| Tata Consultancy Services Limited | 0 | — |
| Ohio State Innovation Foundation | 0 | — |
| Khalifa University of Science and Technology | 0 | — |
| STEINGRIMSSON BALDUR ANDREW | 0 | — |
| Battelle Energy Alliance, LLC | 0 | — |
| Panasonic Intellectual Property Management Co., Ltd. | 0 | -100% |
| University of Virginia Patent Foundation | 0 | — |
| Harbin Engineering University | 0 | — |
Where to take this next
The dataset points to a field with a dominant filer, a stalled filing curve, and claim density that sits in computation more than in alloy chemistry.
Check freedom-to-operate against the top filer's method claims
With one assignee holding 17 of 32 records, most concentrated in prediction and simulation approaches, a new filing in that method space needs a clearance check before drafting rather than after.
Run a claims comparison in EurekaWatch for the 2025–2026 records still arriving
Because publication lags filing by roughly 18 months, the apparent drop after 2021 is not yet the final picture. Re-check the trend once later years finish publishing before treating the field as inactive.
Set a filing alert in EurekaQuestions practitioners ask about this landscape
The ranked set for this scope covers 8 assignees across 32 records, with one filer holding 17 records and the top five combined accounting for 90.6% of the field. That is a heavily concentrated landscape: most of the claim space that exists is already held by a small group rather than spread across many independent filers. A new entrant should expect to negotiate around a handful of portfolios, not dozens of scattered ones.
Filing peaked at 8 records in 2020 and then declined; the 2021-to-2024 window, the most recent period treatable as complete, shows a -100% change. However, publication typically lags filing by around 18 months, so records from 2025 and 2026 are still incomplete and should not be read as confirming a continued slowdown. The honest read is that activity has not sustained its 2020 peak, with the trajectory since 2024 still unresolved.
Computational classes lead: G06F (electric digital data processing) appears in 40.6% of the 32 records in scope and G16C (computational chemistry) in 37.5%, both ahead of traditional metallurgy classes like C22C (alloys) at 12.5% and B22F (powder metallurgy) at 9.4%. This means most filings pair an alloy system with a modelling, prediction or machine-learning layer rather than claiming alloy composition alone. Because a single record can carry multiple IPC codes, these percentages add up to more than 100% of the record total, which is expected.
US20200066376A1, filed by Tata Consultancy Services Limited and published 2020-02-27, covers simulation systems and methods that combine molecular dynamics with accelerated Monte Carlo techniques to predict structure evolution and material properties of atomic elements and alloy materials. It is the most-cited computational-prediction filing in this dataset at 29 citations, with a related EP filing (EP3614389A2) also present. Anyone building alloy-property prediction tools using a similar MD-plus-Monte-Carlo combination should review its claim scope closely before finalizing an approach.
The technology composition data shows lighter filing density in classes like B01J (catalysis, 9.4%), A61Q (cosmetics use, 6.3%), and B22F (powder metallurgy, 9.4%) compared to the dominant computational classes. Combined with a thin long tail beyond the top five assignees, this suggests processing-route and application-specific claims — rather than core prediction methods — carry lower filing density right now. That said, low density is not the same as confirmed freedom to operate, and any drafting decision should be checked against the specific claims already on file.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.