Data‐Driven Materials Innovation and Applications

Advanced Materials, Год журнала: 2022, Номер 34(36)

Опубликована: Апрель 22, 2022

Abstract Owing to the rapid developments improve accuracy and efficiency of both experimental computational investigative methodologies, massive amounts data generated have led field materials science into fourth paradigm data‐driven scientific research. This transition requires development authoritative up‐to‐date frameworks for approaches material innovation. A critical discussion on current advances in discovery with a focus frameworks, machine‐learning algorithms, material‐specific databases, descriptors, targeted applications inorganic is presented. Frameworks rationalizing innovation are described, review essential subdisciplines presented, including: i) advanced data‐intensive strategies algorithms; ii) databases related tools platforms generation management; iii) commonly used molecular descriptors processes. Furthermore, an in‐depth broad innovation, such as energy conversion storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, magnetic materials, provided. Finally, how these (with insights synergy science, tools, mathematics) support paradigms outlined, opportunities challenges highlighted.

Язык: Английский

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Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention

JACS Au, Год журнала: 2021, Номер 1(10), С. 1612 - 1620

Опубликована: Авг. 5, 2021

As a fundamental problem in chemistry, retrosynthesis aims at designing reaction pathways and intermediates for target compound. The goal of artificial intelligence (AI)-aided is to automate this process by learning from the previous chemical reactions make new predictions. Although several models have demonstrated their potentials automated retrosynthesis, there still significant need further enhance prediction accuracy more practical level. Here we propose local framework called LocalRetro, motivated intuition that molecular changes occur mostly locally during reactions. This differs nearly all existing methods suggest reactants based on global structures molecules, often containing fine details not directly relevant concept yields templates involving atom bond edits. Because remote functional groups can also affect overall path as secondary aspect, proposed encoded model then refined account nonlocal effects through attention mechanism. Our shows promising 89.5 99.2% round-trip top-1 top-5 predictions USPTO-50K dataset 50 016 We demonstrate validity LocalRetro large 479 035 (UTPTO-MIT) with comparable 87.0 97.4%, respectively. application correctly predicting synthesis five drug candidate molecules various literature.

Язык: Английский

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Evaluation guidelines for machine learning tools in the chemical sciences

Nature Reviews Chemistry, Год журнала: 2022, Номер 6(6), С. 428 - 442

Опубликована: Май 24, 2022

Язык: Английский

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Quantum machine learning for chemistry and physics

Chemical Society Reviews, Год журнала: 2022, Номер 51(15), С. 6475 - 6573

Опубликована: Янв. 1, 2022

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin deep (DL) have ushered unprecedented developments in all areas physical sciences especially chemistry. Not only classical variants , even those trainable on near-term quantum hardwares been developed promising outcomes. Such algorithms revolutionzed material design performance photo-voltaics, electronic structure calculations ground excited states correlated matter, computation force-fields potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies drug designing classification phases matter accurate identification emergent criticality. this review we shall explicate subset such topics delineate contributions made by both computing enhanced machine over past few years. We not present brief overview well-known techniques also highlight their using statistical insight. The foster exposition aforesaid empower promote cross-pollination among future-research chemistry which can benefit from turn potentially accelerate growth algorithms.

Язык: Английский

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Artificial intelligence to bring nanomedicine to life

Advanced Drug Delivery Reviews, Год журнала: 2022, Номер 184, С. 114194 - 114194

Опубликована: Март 10, 2022

Язык: Английский

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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Chemical Science, Год журнала: 2022, Номер 14(2), С. 226 - 244

Опубликована: Ноя. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Язык: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Год журнала: 2023, Номер 15(1)

Опубликована: Окт. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Язык: Английский

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How to train a neural network potential

The Journal of Chemical Physics, Год журнала: 2023, Номер 159(12)

Опубликована: Сен. 27, 2023

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm change in the development potential energy surfaces for atomistic simulations. By providing efficient access energies and forces, they allow us perform large-scale simulations extended systems, which are not directly accessible by demanding first-principles methods. In these simulations, MLPs can reach accuracy electronic structure calculations, provided that have been properly trained validated using suitable set reference data. Due their highly flexible functional form, construction be done with great care. this Tutorial, we describe necessary key steps training reliable MLPs, from data generation via final validation. procedure, is illustrated example high-dimensional neural network potential, general applicable many types MLPs.

Язык: Английский

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Accelerated chemical science with AI

Digital Discovery, Год журнала: 2023, Номер 3(1), С. 23 - 33

Опубликована: Дек. 6, 2023

The ASLLA Symposium focused on accelerating chemical science with AI. Discussions data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, academic bodies provided.

Язык: Английский

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Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Advanced Intelligent Systems, Год журнала: 2024, Номер 6(4)

Опубликована: Фев. 14, 2024

The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors underscores the need for advanced risk assessment methods to safeguard human health environment. Traditional labor‐intensive approaches have given way computational methods. This review integrates recent developments in chemical nano‐quantitative structure‐activity relationship (QSAR) with machine learning modeling, offering a comprehensive predictive NMs chemicals. It explores nanodescriptors, their role predicting toxicity, amalgamation algorithms nano‐QSAR improved accuracy. article also investigates modeling techniques like molecular dynamics simulations, docking, mechanics/quantum mechanics physical properties. By consolidating these approaches, advocates more accurate efficient means assessing risks associated NMs/chemicals, promoting safe utilization minimizing adverse effects on A valuable resource researchers practitioners, informed decision‐making, advancing our understanding potential risks, is facilitated. Beyond studying systems at various scales, data from sources, enhancing accuracy fostering NMs/chemicals while impact

Язык: Английский

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