Crystal Group Prediction for Lithiated Manganese Oxides Using Machine Learning DOI Creative Commons
Pier Paolo Prosini

Batteries, Journal Year: 2023, Volume and Issue: 9(2), P. 112 - 112

Published: Feb. 5, 2023

This work aimed to predict the crystal structure of a compound starting only from knowledge its chemical composition. The method was developed select new materials in field lithium-ion batteries and tested on Li-Fe-O compounds. For each testing compound, correspondence with respect training compounds evaluated simply by calculating Euclidean distance existing between stoichiometric coefficients elements constituting two At under test assigned for which value minimum. results showed that model can crystalline group an accuracy higher than 80% precision 90%, cut-off four. then used manganese-based (Li-Mn-O). analysis conducted twenty randomly selected 70%. Out ten valid predictions, nine were true positives, 90%.

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
Uranium and lithium extraction from seawater: challenges and opportunities for a sustainable energy future DOI
Yu Jie Lim, Kunli Goh, Atsushi Goto

et al.

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 11(42), P. 22551 - 22589

Published: Jan. 1, 2023

Our analysis of the current literature shows that advances in extractive technologies for U/Li recovery lie at intersection between molecular simulation, nanotechnology and materials science, electrochemistry, membrane engineering.

Language: Английский

Citations

52
Application of machine learning for advanced material prediction and design DOI Creative Commons

Cheuk Hei Chan,

Mingzi Sun, Bolong Huang

et al.

EcoMat, Journal Year: 2022, Volume and Issue: 4(4)

Published: March 7, 2022

Abstract In material science, traditional experimental and computational approaches require investing enormous time resources, the conditions limit experiments. Sometimes, may not yield satisfactory results for desired purpose. Therefore, it is essential to develop a new approach accelerate progress avoid unnecessary wasting of resources. As data‐driven method, machine learning provides reliable accurate performance solve problems in science. This review first outlines fundamental information learning. It continues with research concerning prediction various properties materials by Then discusses methods discovery their structural information. Finally, we summarize other applications will be beneficial future application more science research. image

Language: Английский

Citations

65
Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning DOI
N. Shirokii,

Y. Din,

Ilya Petrov

et al.

Small, Journal Year: 2023, Volume and Issue: 19(19)

Published: Feb. 11, 2023

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity a critical parameter that describes their interaction with living organisms screened in every bio-related research. To prevent excessive experiments, such properties have be pre-evaluated. Several existing ML models partially fulfill gap by predicting whether nanomaterial toxic or not. Yet, this binary categorization neglects concentration dependencies crucial for experimental scientists. Here, an ML-based approach proposed quantitative inorganic cytotoxicity achieving precision expressed 10-fold cross-validation (CV) Q2 = 0.86 root mean squared error (RMSE) 12.2% obtained correlation-based feature selection and grid search-based model hyperparameters optimization. provide further flexibility, atom property-based descriptors are introduced allowing extrapolate on unseen samples. Feature importance calculated find interpretable optimal decision-making. These findings allow scientists perform primary silico candidate screening minimize number excessive, labor-intensive experiments enabling rapid development nanomaterials medicinal purposes.

Language: Английский

Citations

29
Phase diagrams—Why they matter and how to predict them DOI Creative Commons
Pin Yu Chew,

Aleks Reinhardt

The Journal of Chemical Physics, Journal Year: 2022, Volume and Issue: 158(3)

Published: Dec. 15, 2022

Understanding the thermodynamic stability and metastability of materials can help us to, for example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to be durable. It also design experimental routes novel phases with potentially interesting properties. In this Perspective, we provide an overview how phase behavior quantified both computer simulations machine-learning approaches determine diagrams, as well combinations two. We review basic workflow free-energy computations condensed phases, including some practical implementation advice, ranging from Frenkel–Ladd approach integration direct-coexistence simulations. illustrate applications such methods on a range systems chemistry biological separation. Finally, outline challenges, questions, phase-diagram determination which believe possible address near future using state-of-the-art calculations, may fundamental insight into separation processes multicomponent solvents.

Language: Английский

Citations

31
Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control DOI
Meijin Lu, Silin Rao, Hong Yue

et al.

Crystal Growth & Design, Journal Year: 2024, Volume and Issue: 24(12), P. 5374 - 5396

Published: June 6, 2024

Crystals are integral to a variety of industrial applications, such as the development pharmaceuticals and advancements in material science. To anticipate crystal behavior pinpoint effective crystallization techniques, thorough investigation structures, properties, associated processes is essential. However, conventional methods like experimental procedures quantum mechanics calculations, while crucial, can be expensive time-consuming. In response, machine learning has risen an alternative, complementing traditional approaches based on classical force fields. recent years, deployment realm yielded notable progress. This review offers concise overview application techniques crystallization, focusing past five years. Our analysis literature indicates that accelerated prediction structures by streamlining generation evaluation structures. Additionally, it facilitated key properties solubility, melting point, habit. The further explores role refining control optimization processes, highlighting restrictions algorithms sensing technologies. advantages end-to-end processing for enhancing accuracy predictions combination data-driven with mechanism-based models robustness also considered. summary, this provides insights into current state field intelligent suggests pathways future research development.

Language: Английский

Citations

8
Toward the Golden Age of Materials Informatics: Perspective and Opportunities DOI
Keisuke Takahashi, Lauren Takahashi

The Journal of Physical Chemistry Letters, Journal Year: 2023, Volume and Issue: 14(20), P. 4726 - 4733

Published: May 12, 2023

Materials informatics is reaching the transition point and evolving from its early stages of adoption development moving toward golden age. Here, transformation stage materials next level explored. In particular, it has become crucial to be able manipulate synthesis data, properties characterization data. Through use ontology, material design understanding can carried out simultaneously in a whitebox manner. perspective on ultimate goal along with potential key components discussed.

Language: Английский

Citations

15
Accelerating Materials Discovery through Machine Learning: Predicting Crystallographic Symmetry Groups DOI Creative Commons

Yousef A. Alghofaili,

Mohammed Alghadeer, Abdulmohsen Alsaui

et al.

The Journal of Physical Chemistry C, Journal Year: 2023, Volume and Issue: 127(33), P. 16645 - 16653

Published: Aug. 11, 2023

Predicting crystal structure from the chemical composition is one of most challenging and long-standing problems in condensed matter physics. This problem resides at interface between materials sciences With reliable data proper physics-guided modeling, machine learning (ML) can provide an alternative venue to undertake reduce problem's complexity. In this work, very robust ML classifiers for crystallographic symmetry groups were developed applied ternary (AlBmCn) binary (AlBm) starting only formula. first essential step toward predicting full geometry. Such a highly multi-label multi-class perspective requires careful preprocessing due size imbalance data. The resulting predictive models are accurate all groups, including systems, point Bravais lattices, space with weighted balanced accuracies exceeding 95%. small set ionic compositional features, namely, stoichiometry, radii, ionization energies, oxidation states each element compounds. Considering such minimal feature space, obtained high ascertain that physics well captured. even further confirmed as we demonstrate accuracy our approach limited by comparing models. presented work could effectively contribute accelerating new discovery development.

Language: Английский

Citations

15
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors DOI
Yuxin Li, Rongzhi Dong, Wenhui Yang

et al.

Computational Materials Science, Journal Year: 2021, Volume and Issue: 198, P. 110686 - 110686

Published: July 6, 2021

Language: Английский

Citations

29
TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery DOI
Lai Wei, Nihang Fu, Edirisuriya M. Dilanga Siriwardane

et al.

Inorganic Chemistry, Journal Year: 2022, Volume and Issue: 61(22), P. 8431 - 8439

Published: April 14, 2022

Fast and accurate crystal structure prediction (CSP) algorithms web servers are highly desirable for the exploration discovery of new materials out infinite chemical design space. However, currently, computationally expensive first-principles calculation-based CSP applicable to relatively small systems reach most researchers. Several teams have used an element substitution approach generating or predicting structures, but usually in ad hoc way. Here we develop a template-based (TCSP) algorithm its companion server, which makes this tool accessible all Our uses elemental/chemical similarity oxidation states guide selection template structures then rank them based on compatibility can return multiple predictions with ranking scores few minutes. A benchmark study 98290 formulas Materials Project database using leave-one-out evaluation shows that our achieve high accuracy (for 13145 target TCSP predicted their root-mean-square deviation < 0.1) large portion formulas. We also discover Ga-B-N system, showing potential high-throughput discovery. user-friendly app be accessed freely at www.materialsatlas.org/crystalstructure MaterialsAtlas.org platform.

Language: Английский

Citations

21