Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation DOI

Cem Oran,

Riccarda Caputo,

P. Villars

et al.

Inorganic Chemistry, Journal Year: 2024, Volume and Issue: 63(43), P. 20521 - 20530

Published: Oct. 15, 2024

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers fundamental principle similarity in a straightforward way. In this framework, rows correspond to principal quantum numbers elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for deeper understanding relationships and patterns among elements. study, we propose novel strategy structure type (prototype) prediction utilizing PN concept identify possible modifications phase stability unexplored systems. Our PN-based crystal (PNcsp) program, which evaluates through neighboring map, provides most probable prototypes unknown unreported given phases binary higher order We applied PNcsp 59 distinct systems whose equimolar are indicated respective diagrams but lack accurate experimental determination. methodology identified 93 these equiatomic phases, 47 exhibit mechanical dynamic stability. Notably, approach discovered 19 entirely novel, fully stable polymorphic thereby expanding known landscape potential materials. Furthermore, demonstrated that method is also effective nonequimolar

Language: Английский

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization DOI
Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló

et al.

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(15), P. 13006 - 13042

Published: June 27, 2022

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific cutting-edge technologies, where they have transformative impact. Such an assembly statistical linear algebra methods making use large data sets is becoming more integrated into chemistry crystallization research workflows. This review aims to present, for the first time, holistic overview cheminformatics as novel, powerful means accelerate discovery new crystal structures, predict key properties organic crystalline materials, simulate, understand, control dynamics complex process systems, well contribute high throughput automation chemical development involving materials. We critically advances these new, rapidly emerging areas, raising awareness issues such bridging models with first-principles mechanistic models, set size, structure, quality, selection appropriate descriptors. At same we propose future at interface applied mathematics, chemistry, crystallography. Overall, this increase adoption tools by chemists scientists across industry academia.

Language: Английский

Citations

76
Crystal structure generation with autoregressive large language modeling DOI Creative Commons
Luis M. Antunes, Keith T. Butler, Ricardo Grau‐Crespo

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Dec. 6, 2024

Abstract The generation of plausible crystal structures is often the first step in predicting structure and properties a material from its chemical composition. However, most current methods for prediction are computationally expensive, slowing pace innovation. Seeding algorithms with quality generated candidates can overcome major bottleneck. Here, we introduce CrystaLLM, methodology versatile structures, based on autoregressive large language modeling (LLM) Crystallographic Information File (CIF) format. Trained millions CIF files, CrystaLLM focuses through text. produce wide range inorganic compounds unseen training, as demonstrated by ab initio simulations. Our approach challenges conventional representations crystals, demonstrates potential LLMs learning effective models chemistry, which will lead to accelerated discovery innovation materials science.

Language: Английский

Citations

18
First-principles calculations: Structural stability, electronic structure, optical properties and thermodynamic properties of AlBN2, Al3BN4 and AlB3N4 nitrides DOI
Bo Li, Huarong Qi,

Yonghua Duan

et al.

Materials Science in Semiconductor Processing, Journal Year: 2023, Volume and Issue: 160, P. 107400 - 107400

Published: Feb. 22, 2023

Language: Английский

Citations

27
Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials DOI Creative Commons
Lai Wei,

Qinyang Li,

Yuqi Song

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 5, 2024

Abstract Self‐supervised neural language models have recently achieved unprecedented success from natural processing to learning the languages of biological sequences and organic molecules. These demonstrated superior performance in generation, structure classification, functional predictions for proteins molecules with learned representations. However, most masking‐based pre‐trained are not designed generative design, their black‐box nature makes it difficult interpret design logic. Here a Blank‐filling Language Model Materials (BLMM) Crystal Transformer is proposed, network‐based probabilistic model tinkering inorganic materials. The built on blank‐filling text generation has unique advantages “materials grammars” together high‐quality interpretability, data efficiency. It can generate chemically valid materials compositions as high 89.7% charge neutrality 84.8% balanced electronegativity, which more than four eight times higher compared pseudo‐random sampling baseline. process BLMM allows recommend operations based chemistry, useful doping. applied discover set new validated using Density Functional Theory (DFT) calculations. This work thus brings unsupervised transformer artificial intelligence A user‐friendly web app been developed be accessed freely at www.materialsatlas.org/blmtinker .

Language: Английский

Citations

10
Towards quantitative evaluation of crystal structure prediction performance DOI Creative Commons
Lai Wei, Q. Li, Sadman Sadeed Omee

et al.

Computational Materials Science, Journal Year: 2024, Volume and Issue: 235, P. 112802 - 112802

Published: Jan. 23, 2024

Language: Английский

Citations

7
Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies DOI
Jiahao Xie, Yansong Zhou, Muhammad Faizan

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(5), P. 322 - 333

Published: May 23, 2024

Language: Английский

Citations

7
Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models DOI Creative Commons
Nihang Fu,

Jeffrey Hu,

Ying Feng

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(28)

Published: Aug. 7, 2023

Oxidation states (OS) are the charges on atoms due to electrons gained or lost upon applying an ionic approximation their bonds. As a fundamental property, OS has been widely used in charge-neutrality verification, crystal structure determination, and reaction estimation. Currently, only heuristic rules exist for guessing oxidation of given compound with many exceptions. Recent work developed machine learning models based structural features predicting metal ions. However, composition-based state prediction still remains elusive so far, which significant implications discovery new materials structures have not determined. This proposes novel deep learning-based BERT transformer language model BERTOS all elements inorganic compounds chemical composition. achieves 96.82% accuracy all-element benchmarked cleaned ICSD dataset 97.61% oxide materials. It is also demonstrated how it can be conduct large-scale screening hypothetical material compositions discovery.

Language: Английский

Citations

14
Review on automated 2D material design DOI Creative Commons
Abdalaziz Rashid Al-Maeeni, Mikhail Lazarev, N. Kazeev

et al.

2D Materials, Journal Year: 2024, Volume and Issue: 11(3), P. 032002 - 032002

Published: May 2, 2024

Abstract Deep learning (DL) methodologies have led to significant advancements in various domains, facilitating intricate data analysis and enhancing predictive accuracy generation quality through complex algorithms. In materials science, the extensive computational demands associated with high-throughput screening techniques such as density functional theory, coupled limitations laboratory production, present substantial challenges for material research. DL are poised alleviate these by reducing costs of simulating properties generating novel desired attributes. This comprehensive review document explores current state applications design, a particular emphasis on two-dimensional materials. The article encompasses an in-depth exploration data-driven approaches both forward inverse design within realm science.

Language: Английский

Citations

4
Deep learning-assisted methods for accelerating the intelligent screening of novel 2D materials: New perspectives focusing on data collection and description DOI

Yuandong Lin,

Ji Ma, Yong‐Guang Jia

et al.

Coordination Chemistry Reviews, Journal Year: 2025, Volume and Issue: 529, P. 216436 - 216436

Published: Jan. 16, 2025

Language: Английский

Citations

0
Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules DOI
Dmitriy M. Makarov, Pavel S. Bocharov,

Michail M. Lukanov

et al.

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 273 - 292

Published: Jan. 1, 2025

Language: Английский

Citations

0