Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Matter, Journal Year: 2021, Volume and Issue: 4(5), P. 1578 - 1597

Published: April 5, 2021

The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would optimal for a given application. High-throughput computational screening machine learning are promising routes efficiently navigate the vast space have rarely been used prediction properties that need calculated by quantum mechanical methods. Here, we introduce Quantum MOF (QMOF) database, publicly available database computed quantum-chemical more than 14,000 experimentally synthesized MOFs. Throughout this study, demonstrate how models trained on QMOF rapidly discover with targeted electronic structure using theoretically band gaps as representative example. We conclude highlighting several predicted low gaps, challenging task electronically insulating most

Language: Английский

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Autonomous experimentation systems for materials development: A community perspective

Matter, Journal Year: 2021, Volume and Issue: 4(9), P. 2702 - 2726

Published: July 26, 2021

Language: Английский

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Construction of Bio‐Piezoelectric Platforms: From Structures and Synthesis to Applications

Advanced Materials, Journal Year: 2021, Volume and Issue: 33(27)

Published: May 25, 2021

Piezoelectric materials, with their unique ability for mechanical-electrical energy conversion, have been widely applied in important fields such as sensing, harvesting, wastewater treatment, and catalysis. In recent years, advances material synthesis engineering provided new opportunities the development of bio-piezoelectric materials excellent biocompatibility piezoelectric performance. Bio-piezoelectric attracted interdisciplinary research interest due to insights on impact piezoelectricity biological systems versatile biomedical applications. This review therefore introduces platforms from a broad perspective highlights design strategies. State-of-the-art applications both biosensing disease treatment will be systematically outlined. The relationships between properties, structure, performance are examined provide deep understanding working mechanisms physiological environment. Finally, trends challenges discussed, aim construction future materials.

Language: Английский

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Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Journal of Chemical Information and Modeling, Journal Year: 2021, Volume and Issue: 61(5), P. 2131 - 2146

Published: April 29, 2021

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods quickly assess the promises these fascinating materials various applications. HTCS studies provide a massive amount structural property and performance data for MOFs, which need be further analyzed. Recent implementation machine learning (ML), is another growing field research, MOFs been very fruitful not only revealing hidden structure–performance relationships but also understanding their trends different applications, specifically gas storage separation. In this review, we highlight current state art ML-assisted separation address both opportunities challenges that are emerging by emphasizing how merging ML MOF simulations can useful.

Language: Английский

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Machine learning for advanced energy materials

Energy and AI, Journal Year: 2021, Volume and Issue: 3, P. 100049 - 100049

Published: Jan. 24, 2021

The screening of advanced materials coupled with the modeling their quantitative structural-activity relationships has recently become one hot and trending topics in energy due to diverse challenges, including low success probabilities, high time consumption, computational cost associated traditional methods developing materials. Following this, new research concepts technologies promote development necessary. latest advancements artificial intelligence machine learning have therefore increased expectation that data-driven science would revolutionize scientific discoveries towards providing paradigms for Furthermore, current advances engineering also demonstrate application technology not only significantly facilitate design but enhance discovery deployment. In this article, importance necessity contributing global carbon neutrality are presented. A comprehensive introduction fundamentals is provided, open-source databases, feature engineering, algorithms, analysis model. Afterwards, progress alkaline ion battery materials, photovoltaic catalytic dioxide capture discussed. Finally, relevant clues successful applications remaining challenges highlighted.

Language: Английский

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Ab Initio Machine Learning in Chemical Compound Space

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10001 - 10036

Published: Aug. 13, 2021

Chemical compound space (CCS), the set of all theoretically conceivable combinations chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling this space, for example, in search novel molecules or materials which exhibit desirable properties, therefore prohibitive but smallest subsets simplest properties. We review studies aimed at tackling challenge using modern machine learning techniques on (i) synthetic data, typically generated quantum mechanics methods, (ii) model architectures inspired by mechanics. Such Quantum Machine Learning (QML) approaches combine numerical efficiency statistical surrogate models with an ab initio view matter. They rigorously reflect underlying physics order to reach universality transferability across CCS. While state-of-the-art approximations problems impose severe computational bottlenecks, recent QML developments indicate possibility substantial acceleration without sacrificing predictive power

Language: Английский

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Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

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

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Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

Frontiers in Marine Science, Journal Year: 2022, Volume and Issue: 9

Published: July 28, 2022

Conservation of marine ecosystems has been highlighted as a priority to ensure sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, tools support decision-making. However, there are many roadblocks achieving adequate timely on both the effectiveness, long-term success conservation efforts, including limited funding, inadequate sampling, processing bottlenecks. These factors can result in ineffective, or even detrimental, decisions already impacted ecosystems. An automated approach facilitated by artificial intelligence (AI) provides managers with toolkit that help alleviate number these issues reducing monitoring bottlenecks costs monitoring. Automating collection, transfer, access greater information, thereby facilitating effective management. Incorporating automation big availability into decision system user-friendly interface also enables adaptive We summarise current state techniques used science use examples other disciplines identify existing potentially transferable methods enable improve predictive modelling capabilities making. discuss emerging technologies likely be useful research computer associated continues develop become more accessible. Our perspective highlights potential AI analytics for supporting decision-making, but points important knowledge gaps multiple areas processes. challenges should prioritised move toward implementing informed understanding successful outcomes managers. conclude emphasis assisted several scientific may mean future is improved implementation automation.

Language: Английский

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Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Journal of Composites Science, Journal Year: 2023, Volume and Issue: 7(9), P. 364 - 364

Published: Sept. 1, 2023

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the materials. This comprehensive review paper examines applications intelligence forecasting different types composites. begins with an overview then outlines process predicting material properties. primary focus this lies exploring various techniques employed Furthermore, highlights theoretical foundations, strengths, weaknesses each method used for Finally, based on findings, discusses key challenges suggests future research directions field prediction, offering valuable insights further exploration. is intended serve as significant reference researchers engaging studies within domain.

Language: Английский

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Machine learning in energy storage materials

Interdisciplinary materials, Journal Year: 2022, Volume and Issue: 1(2), P. 175 - 195

Published: March 29, 2022

Abstract With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution materials research paradigm. Here, taking dielectric capacitors and lithium‐ion batteries as two representative examples, we review substantial advances development energy storage materials. First, a thorough discussion framework science is presented. Then, summarize applications from three aspects, including discovering designing novel materials, enriching theoretical simulations, assisting experimentation characterization. Finally, brief outlook highlighted to spark more insights on innovative implementation science.

Language: Английский

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