Physics-Inspired Structural Representations for Molecules and Materials

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 9759 - 9815

Published: July 26, 2021

The first step in the construction of a regression model or data-driven analysis, aiming to predict elucidate relationship between atomic-scale structure matter and its properties, involves transforming Cartesian coordinates atoms into suitable representation. development representations has played, continues play, central role success machine-learning methods for chemistry materials science. This review summarizes current understanding nature characteristics most commonly used structural chemical descriptions atomistic structures, highlighting deep underlying connections different frameworks ideas that lead computationally efficient universally applicable models. It emphasizes link their physical chemistry, mathematical description, provides examples recent applications diverse set science problems, outlines open questions promising research directions field.

Language: Английский

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Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(12), P. 10970 - 11021

Published: May 16, 2022

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems the future. The liquid electrolyte is one of most important parts battery extremely critical stabilizing electrode–electrolyte interfaces constructing safe long-life-span batteries. Tremendous efforts been devoted developing new solvents, salts, additives, recipes, where molecular dynamics (MD) simulations play an increasingly role exploring structures, physicochemical properties such as ionic conductivity, interfacial reaction mechanisms. This review affords overview applying MD study electrolytes for rechargeable First, fundamentals recent theoretical progress three-class summarized, including classical, ab initio, machine-learning (section 2). Next, application exploration electrolytes, probing bulk structures 3), deriving macroscopic conductivity dielectric constant 4), revealing mechanisms 5), sequentially presented. Finally, general conclusion insightful perspective on current challenges future directions provided. Machine-learning technologies highlighted figure out these challenging issues facing research promote rational design advanced next-generation

Language: Английский

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Small data machine learning in materials science

npj Computational Materials, Journal Year: 2023, Volume and Issue: 9(1)

Published: March 25, 2023

Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed limitations brought data. Then, workflow learning has been introduced. Next, methods dealing with were introduced, including extraction from publications, database construction, high-throughput computations and experiments source level; modeling algorithms for imbalanced algorithm active transfer strategy level. Finally, future directions in science proposed.

Language: Английский

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Implicit Solvation Methods for Catalysis at Electrified Interfaces

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(12), P. 10777 - 10820

Published: Dec. 20, 2021

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of continuous polarizable medium. Originating molecular chemistry with finite solutes, implicit techniques are now increasingly used context first-principles modeling electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz model latter electrodes reactive surface them through slabs periodic boundary condition supercells brings its specific challenges. Foremost this concerns difficulty describing entire double layer forming electrified solid-liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review methodology from application angle, highlighting particular use widespread ab initio thermodynamics catalysis. Notably, can be mimic polarization electrode's electronic under applied potential concomitant capacitive charging beyond limitations DFT supercell. Most critical continuing advances effective SLI lack pertinent (experimental or high-level theoretical) reference data needed parametrization.

Language: Английский

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Unified representation of molecules and crystals for machine learning

Machine Learning Science and Technology, Journal Year: 2022, Volume and Issue: 3(4), P. 045017 - 045017

Published: Nov. 3, 2022

Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly accurately interpolating between reference calculations. For this, kernel approaches crucially require a representation that accommodates arbitrary systems. We introduce many-body tensor is invariant to translations, rotations, and nuclear permutations same elements, unique, differentiable, represent molecules crystals, fast compute. Empirical evidence for competitive energy force prediction errors presented changes in molecular structure, crystal chemistry, dynamics using regression symmetric gradient-domain as models. Applicability demonstrated phase diagrams Pt-group/transition-metal binary

Language: Английский

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SELFIES and the future of molecular string representations

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

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Deep potentials for materials science

Materials Futures, Journal Year: 2022, Volume and Issue: 1(2), P. 022601 - 022601

Published: April 19, 2022

Abstract To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions atomic interactions has emerged been widely applied; i.e. machine learning potentials (MLPs). One recently developed type MLP is deep potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying method presented along with step-by-step their development use. We also review applications DPs wide range systems. Library provides platform for database extant DPs. discuss accuracy efficiency compared potentials.

Language: Английский

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Data‐Driven Materials Innovation and Applications

Advanced Materials, Journal Year: 2022, Volume and Issue: 34(36)

Published: April 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.

Language: Английский

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Quantum machine learning for chemistry and physics

Chemical Society Reviews, Journal Year: 2022, Volume and Issue: 51(15), P. 6475 - 6573

Published: Jan. 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.

Language: Английский

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Extending machine learning beyond interatomic potentials for predicting molecular properties

Nature Reviews Chemistry, Journal Year: 2022, Volume and Issue: 6(9), P. 653 - 672

Published: Aug. 25, 2022

Language: Английский

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