4.6 Article

Artificial intelligence for materials research at extremes

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Electrical & Electronic

Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development

A. R. Will-Cole et al.

Summary: Bayesian optimization is a well-established machine learning approach for optimizing black-box functions. In this study, it was applied to optimize the structure-property relationships of known ferromagnetic thin-film materials. The results demonstrated that Bayesian optimization can effectively reduce the required number of samples and save time and resources in optimizing ferromagnetic films.

IEEE TRANSACTIONS ON MAGNETICS (2022)

Article Multidisciplinary Sciences

A self-driving laboratory advances the Pareto front for material properties

Benjamin P. MacLeod et al.

Summary: This study uses a self-driving laboratory to map out the Pareto front for making highly conductive coatings at low temperatures. It discovers new synthesis conditions that yield metallic films at lower processing temperatures, enabling the coating of different plastic materials. The results demonstrate the potential of using a self-driving laboratory to find materials that provide optimal trade-offs between conflicting objectives.

NATURE COMMUNICATIONS (2022)

Article Multidisciplinary Sciences

Decoding reactive structures in dilute alloy catalysts

Nicholas Marcella et al.

Summary: Rational catalyst design is crucial for energy-efficient and sustainable catalytic processes. This study combines X-ray absorption spectroscopy, activity studies, and kinetic modeling to understand the mechanism of catalytic reactions in dilute bimetallic catalysts. The results show that surface Pd ensembles containing a few Pd atoms are the active species, and the catalytic activity can be tuned by controlling the ensemble size.

NATURE COMMUNICATIONS (2022)

Article Multidisciplinary Sciences

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner et al.

Summary: This paper introduces an E(3)-equivariant deep learning method for accelerating molecular dynamics simulations. The method shows state-of-the-art accuracy and remarkable sample efficiency in faithfully describing the dynamics of complex systems. The Neural Equivariant Interatomic Potentials (NequIP) approach employs E(3)-equivariant convolutions to interact with geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. NequIP outperforms existing models with significantly fewer training data, challenging the commonly held belief about the necessity of massive training sets for deep neural networks.

NATURE COMMUNICATIONS (2022)

Article Physics, Applied

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

Austin McDannald et al.

Summary: This paper demonstrates the first live, autonomous control over neutron diffraction experiments using ANDiE, which significantly reduces the experimental time required and improves measurement efficiency. It shows the ability of ANDiE to determine the magnetic ordering transition of MnO and Fe1.09Te, and emphasizes the broad applicability of its active learning approach to neutron-based experiments.

APPLIED PHYSICS REVIEWS (2022)

Review Chemistry, Physical

Recent advances and applications of deep learning methods in materials science

Kamal Choudhary et al.

Summary: This article provides an overview of the recent developments in deep learning methods for atomistic simulation, materials imaging, spectral analysis, and natural language processing. It discusses the applications, modeling approaches, and available software and datasets for each modality.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Physics, Applied

Integrating machine learning with mechanistic models for predicting the yield strength of high entropy alloys

Shunshun Liu et al.

Summary: This article presents a computational approach that combines mechanistic models with phenomenological and machine learning models to rapidly predict the temperature-dependent properties of high entropy alloys.

JOURNAL OF APPLIED PHYSICS (2022)

Article Microscopy

Event-based hyperspectral EELS: towards nanosecond temporal resolution

Yves Auad et al.

Summary: The study developed an event-based hyperspectral EELS technique using a specific integrated circuit detector and embedded time-to-digital lines, allowing for hyperspectral image acquisition without additional costs, as well as enabling the study of chemical reactions in samples under electron beam irradiation.

ULTRAMICROSCOPY (2022)

Article Chemistry, Multidisciplinary

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Tatiana Konstantinova et al.

Summary: Imaging, scattering, and spectroscopy are fundamental for understanding and discovering new functional materials. Machine learning methods are being used to process and interpret large datasets in real-time. However, there are barriers for general users who lack expertise in machine learning and technical difficulties in deploying ML models. This paper presents various ML models and demonstrates how they can be integrated into experimental workflows at specific facilities.

DIGITAL DISCOVERY (2022)

Article Automation & Control Systems

Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro-Processors

Fazel Bateni et al.

Summary: Lead halide perovskite nanocrystals are considered advanced functional materials with outstanding optoelectronic characteristics, but their precise synthesis and fundamental studies remain challenging. An autonomous fluidic micro-processor has been developed to accelerate complex synthesis and processing parameters, demonstrating efficient and intelligent navigation through halide exchange and cation doping reactions. This strategy can be further applied for autonomous discovery and development of novel impurity-doped nanocrystals for next-generation energy technologies.

ADVANCED INTELLIGENT SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Towards automating structural discovery in scanning transmission electron microscopy

Nicole Creange et al.

Summary: Researchers have developed automated experiment workflows to explore the atomic-level structure of functional materials using different combinations, discussing the advantages and disadvantages of different methods, and applying them to materials systems with different feature sizes.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2022)

Article Materials Science, Multidisciplinary

Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys

Ankit Roy et al.

Summary: The study uses machine-learning to down select corrosion-resistant alloys and optimize descriptors to predict the corrosion resistance of MPEAs. Findings demonstrate the potential and challenges of ML when applied to complex chemical phenomena like alloy corrosion.

NPJ MATERIALS DEGRADATION (2022)

Article Materials Science, Multidisciplinary

Benchmarking active learning strategies for materials optimization and discovery

Alex Wang et al.

Summary: Autonomous physical science is revolutionizing materials science by using machine learning to optimize experiment design and analysis. Incorporating prior knowledge of physical laws into algorithms further improves system performance. A reference dataset is provided to benchmark and compare different active learning strategies in materials optimization, and Expected Improvement showed the best overall performance.

OXFORD OPEN MATERIALS SCIENCE (2022)

Editorial Material Chemistry, Physical

Towards data-driven next-generation transmission electron microscopy

Steven R. Spurgeon et al.

Summary: Electron microscopy plays a crucial role in various aspects of modern life, including materials development for quantum computing, energy, and medicine. In order to achieve transformative discoveries in the next decade, an open, highly integrated, and data-driven microscopy architecture is essential.

NATURE MATERIALS (2021)

Article Materials Science, Multidisciplinary

High-throughput rapid experimental alloy development (HT-READ)

Kenneth S. Vecchio et al.

Summary: This article presents a high-throughput rapid experimental alloy development method that integrates a closed-loop material screening process and artificial intelligence agent technology. It achieves a unified framework for computational identification, experimental preparation, and high-throughput analysis, preventing institutional knowledge loss and enabling the use of new experimental data in new design objectives.

ACTA MATERIALIA (2021)

Article Engineering, Multidisciplinary

Bayesian neural networks for uncertainty quantification in data-driven materials modeling

Audrey Olivier et al.

Summary: Modern machine learning techniques, in conjunction with simulation-based methods, show great potential for scientific and engineering applications. However, a critical shortcoming is the lack of reliable uncertainty estimates. This paper presents methods for Bayesian learning of neural networks, achieving a balance between accuracy of uncertainty estimates and computational cost.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)

Article Computer Science, Hardware & Architecture

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Francis J. Alexander et al.

Summary: The rapid growth in data, computational methods, and computing power is driving a revolution in machine learning, statistical learning, computational learning, and artificial intelligence. These new technologies have important implications for scientific discovery and the design and use of computing systems.

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS (2021)

Article Chemistry, Multidisciplinary

Multimodal Spectroscopic Study of Surface Termination Evolution in Cr2TiC2Tx MXene

James L. Hart et al.

Summary: The study reports vacuum annealing experiments on Cr2TiC2Tx with in situ electron energy loss spectroscopy and Cr K-edge extended energy loss fine structure analysis, tracking the evolution of the MXene surface coordination environment. The results demonstrate thermal control of -F termination in Cr2TiC2Tx and the thermal stability of O termination.

ADVANCED MATERIALS INTERFACES (2021)

Article Computer Science, Artificial Intelligence

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Eyke Huellermeier et al.

Summary: Uncertainty plays a major role in machine learning, with aleatoric and epistemic uncertainty being two key types. As machine learning becomes increasingly relevant for practical applications, handling uncertainty becomes more important.

MACHINE LEARNING (2021)

Article Multidisciplinary Sciences

RapidEELS: machine learning for denoising and classification in rapid acquisition electron energy loss spectroscopy

Cassandra M. Pate et al.

Summary: This study demonstrates the use of machine learning techniques to denoise high frame rate spectra, providing a foundation for reliable analysis of noisy data acquired in rapid in-situ spectroscopy experiments. The results pave the way for automation in microscopy and the exploration of transformations that are otherwise poorly understood.

SCIENTIFIC REPORTS (2021)

Article Multidisciplinary Sciences

Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams

Sebastian Ament et al.

Summary: Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis, exemplifying complex and resource-intensive experimentation, can be accelerated through hierarchical autonomous experimentation guided by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and a range of AI methods to efficiently explore multidimensional parameter space and map processing phase diagrams.

SCIENCE ADVANCES (2021)

Article Materials Science, Multidisciplinary

Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer

James R. Deneault et al.

Summary: The research developed a low-cost and accessible research robot, AM ARES, that utilized online machine learning planners and their soon-to-be open-sourced ARES OS software to rapidly and effectively optimize the complex high-dimensional parameter sets associated with 3D printing.

MRS BULLETIN (2021)

Review Materials Science, Multidisciplinary

Emerging Capabilities for the High-Throughput Characterization of Structural Materials

Daniel B. Miracle et al.

Summary: This review evaluates recent progress in CHT evaluations for structural materials, highlighting the application of high-throughput computations and new synthesis methods. It also points out challenges in measurement and production, and proposes a strategy to enhance efficiency by conducting two layers of evaluations.

ANNUAL REVIEW OF MATERIALS RESEARCH, VOL 51, 2021 (2021)

Article Chemistry, Physical

First-Principles-Based Prediction of Electrochemical Oxidation and Corrosion of Copper under Multiple Environmental Factors

Lauren N. Walters et al.

Summary: This study investigates the immunity, passivation, and corrosion behavior of copper under various environmental factors using density functional theory-calculated electrochemical Pourbaix diagrams. The discrepancies between thermodynamic predictions and electrochemical observations in aqueous conditions are discussed. It is found that corrosion phases compete with solid copper precipitates near neutral pHs, and elevated temperatures favor corrosion products over passivating oxides. Pressure is shown to alter the predominance of specific ionic species in the Pourbaix diagrams.

JOURNAL OF PHYSICAL CHEMISTRY C (2021)

Article Materials Science, Multidisciplinary

EELSpecNet: Deep Convolutional Neural Network Solution for Electron Energy Loss Spectroscopy Deconvolution

S. Shayan Mousavi M et al.

MICROSCOPY AND MICROANALYSIS (2021)

Article Chemistry, Physical

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Ayana Ghosh et al.

Summary: The article explores the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduces workflows based on ensemble learning and iterative training to greatly improve feature detection.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Chemistry, Physical

Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity

Koushik Pal et al.

Summary: This study presents the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides with low lattice thermal conductivity, which may have high energy conversion efficiency. The research suggests experimental opportunities in synthesizing and characterizing these stable compounds.

NPJ COMPUTATIONAL MATERIALS (2021)

Review Materials Science, Multidisciplinary

Autonomous experimentation systems for materials development: A community perspective

Eric Stach et al.

Summary: Materials research and development are crucial for solving world problems, and the partnership between humans and robots can accelerate technological advancements. The new paradigm brings both challenges and opportunities, requiring collaborative efforts across academia, industry, government, and funding agencies.

MATTER (2021)

Article Chemistry, Medicinal

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Edward Kim et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Article Materials Science, Multidisciplinary

High-throughput synthesis of Mo-Nb-Ta-W high-entropy alloys via additive manufacturing

Michael Moorehead et al.

MATERIALS & DESIGN (2020)

Article Materials Science, Multidisciplinary

Understanding Micromechanical Material Behavior Using Synchrotron X-rays andIn SituLoading

Matthew P. Miller et al.

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE (2020)

Article Multidisciplinary Sciences

Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization

Jorge Chang et al.

SCIENTIFIC REPORTS (2020)

Article Multidisciplinary Sciences

On-the-fly closed-loop materials discovery via Bayesian active learning

A. Gilad Kusne et al.

NATURE COMMUNICATIONS (2020)

Article Materials Science, Multidisciplinary

propnet: A Knowledge Graph for Materials Science

David Mrdjenovich et al.

MATTER (2020)

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

Review Materials Science, Multidisciplinary

Exploring new links between crystal plasticity models and high-energy X-ray diffraction microscopy

Paul A. Shadea et al.

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2019)

Article Materials Science, Multidisciplinary

The machine learning revolution in materials?

Kristofer G. Reyes et al.

MRS BULLETIN (2019)

Article Chemistry, Physical

Semi-supervised machine-learning classification of materials synthesis procedures

Haoyan Huo et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Chemistry, Multidisciplinary

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

ACS CENTRAL SCIENCE (2018)

Article Thermodynamics

Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods

Han Wei et al.

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2018)

Article Chemistry, Physical

Experimental assessment of thin film high pressure metal hydride material properties

Claudio Corgnale et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2018)

Article Engineering, Manufacturing

Machine Learning Prediction of Heat Capacity for Solid Inorganics

Steven K. Kauwe et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2018)

Review Engineering, Chemical

Indentation Hardness Measurements at Macro-, Micro-, and Nanoscale: A Critical Overview

Esteban Broitman

TRIBOLOGY LETTERS (2017)

Article Materials Science, Multidisciplinary

Representation of compounds for machine-learning prediction of physical properties

Atsuto Seko et al.

PHYSICAL REVIEW B (2017)

Article Chemistry, Physical

Combining Simulations and Solution Experiments as a Paradigm for RNA Force Field Refinement

Andrea Cesari et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)

Article Materials Science, Multidisciplinary

High Dynamic Range Pixel Array Detector for Scanning Transmission Electron Microscopy

Mark W. Tate et al.

MICROSCOPY AND MICROANALYSIS (2016)

Article Chemistry, Physical

Autonomy in materials research: a case study in carbon nanotube growth

Pavel Nikolaev et al.

NPJ COMPUTATIONAL MATERIALS (2016)

Article Nanoscience & Nanotechnology

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

Michael W. Gaultois et al.

APL MATERIALS (2016)

Review Materials Science, Multidisciplinary

High temperature nanoindentation: The state of the art and future challenges

J. M. Wheeler et al.

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2015)

Review Chemistry, Physical

Constructing high-dimensional neural network potentials: A tutorial review

Joerg Behler

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Review Materials Science, Multidisciplinary

The Materials Genome Initiative, the interplay of experiment, theory and computation

Juan J. de Pablo et al.

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2014)

Article Materials Science, Multidisciplinary

Compositionally graded metals: A new frontier of additive manufacturing

Douglas C. Hofmann et al.

JOURNAL OF MATERIALS RESEARCH (2014)

Article Nanoscience & Nanotechnology

Discovery of a meta-stable Al-Sm phase with unknown stoichiometry using a genetic algorithm

Feng Zhang et al.

SCRIPTA MATERIALIA (2014)

Article Engineering, Manufacturing

Three-dimensional sampling of material structure for property modeling and design

McLean P. Echlin et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2014)

Article Computer Science, Artificial Intelligence

A unifying view on dataset shift in classification

Jose G. Moreno-Torres et al.

PATTERN RECOGNITION (2012)

Article Materials Science, Multidisciplinary

Plasticity of Micrometer-Scale Single Crystals in Compression

Michael D. Uchic et al.

Annual Review of Materials Research (2009)

Article Computer Science, Artificial Intelligence

Principal component analysis and dimensional analysis as materials informatics tools to reduce dimensionality in materials science and engineering

Krishna Rajan et al.

Statistical Analysis and Data Mining (2009)

Article Electrochemistry

In situ Raman spectroscopic investigation of aqueous iron corrosion at elevated temperatures and pressures

JE Maslar et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2000)