# Structure Prediction from Chemical Formula Using Periodic Descriptors: Toward Inverse Design of Inorganic Materials

https://mdr.nims.go.jp/datasets/669f6bea-e9a5-4a04-8041-ccad1609ffeb

## File

- [abstract-YJWu.pdf](https://mdr.nims.go.jp/filesets/81b83b09-b929-45e7-9226-8a59e9fd435c/download) ([Detail](https://mdr.nims.go.jp/filesets/81b83b09-b929-45e7-9226-8a59e9fd435c.md))

## Id

669f6bea-e9a5-4a04-8041-ccad1609ffeb

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-15T05:03:02.665602Z

## Updated at

2025-12-19T01:12:12.362534Z

## Published at

2025-12-19T05:11:36.435069Z

## Doi

https://doi.org/10.48505/nims.6037

## First published url



## Date published



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## Resource type

conference_presentation

## Manuscript type

na

## Collection



## Title

- title: 'Structure Prediction from Chemical Formula Using Periodic Descriptors: Toward
    Inverse Design of Inorganic Materials'
  title_type: original
  lang: en

## Description

- description: "The discovery of novel functional materials increasingly relies on
    data-driven approaches, yet many formula-based candidates generated through exploration
    or generative models lack structural information. This absence of structure poses
    a significant bottleneck for both simulation and experimental planning. In this
    work, we examine the potential of periodic descriptors—compact, chemically structured
    vectors derived from the periodic table—for predicting crystal structure types
    directly from chemical formulas.\r\n  Classification models were trained on approximately
    150,000 experimentally reported inorganic compounds from the AtomWork-Adv. (AWA)
    database. The models achieved top-3 accuracies of ~0.70 for space group and ~0.76
    for Pearson symbol, and top-5 accuracies of ~0.72 and ~0.87, respectively. These
    results show that periodic descriptors capture compositional patterns linked to
    structural tendencies, enabling prototype inference even without geometry information.\r\n
    \ We further benchmarked our models against conventional classification tools
    and analyzed prediction consistency across structure families. This study demonstrates
    how composition-only models can guide structural assumptions for unexplored formulas,
    serving as a practical pre-screening step in materials discovery. Future developments
    will integrate this framework with formula-based exploration and property models,
    supporting a more complete inverse design workflow for inorganic materials."
  description_type: abstract
  lang: eng

## Creator

- name: Yen-Ju Wu
  role: author
  orcid: https://orcid.org/0000-0003-2647-3407
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Inorganic Materials Group
- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Inorganic Materials Group

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## Keyword

- subject: Structure Prediction
  schema: not_defined
- subject: Periodic Descriptor
  schema: not_defined
- subject: Inverse Design
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

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## Data origin

- data_origin_type: other

## Embargo



## Journal



## Conference

name: 2025 TwiChE (72nd Annual Meeting of the Taiwan Society of Chemical Engineer)
start_date: 2025-11-29
end_date: 2025-11-30
identifier: https://2025twiche.tw/

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## Fileset

- id: 81b83b09-b929-45e7-9226-8a59e9fd435c
  filename: abstract-YJWu.pdf
  content_type: application/pdf
  size: 144019
  md5: 1f4dbd3f6f5228690a346714a723a805

## Thumbnail

fileset_id: 81b83b09-b929-45e7-9226-8a59e9fd435c
filename: abstract-YJWu.pdf