4.8 Article

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

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。
Editorial Material Chemistry, Physical

The value of negative results in data-driven catalysis research

Toshiaki Taniike et al.

Summary: Data science and machine learning have the potential to greatly improve catalyst discovery. However, the issue of negative results is currently hindering progress. This Comment highlights the value of negative data and proposes a vision for overcoming the challenges in data-driven catalysis research.

NATURE CATALYSIS (2023)

Article Chemistry, Multidisciplinary

A semi-supervised deep-learning approach for automatic crystal structure classification

Satvik Lolla et al.

Summary: In this work, a novel approach of semi-supervised learning is applied to identify the Bravais lattice and space group of inorganic crystals. The reported models utilize labeled and unlabeled data to train a generative deep-learning model, which can more accurately generalize to real data. The models outperform current deep-learning approaches for space group and Bravais lattice classification using fewer training data.

JOURNAL OF APPLIED CRYSTALLOGRAPHY (2022)

Article Chemistry, Multidisciplinary

Machine-Learning Spectral Indicators of Topology

Nina Andrejevic et al.

Summary: Topological materials discovery is significant in condensed matter physics, but the experimental determination of materials' topology is challenging. Researchers have used X-ray absorption spectroscopy and neural networks to predict the topological class of materials, potentially leading to the discovery of new topological materials and further understanding of field-driven phenomena.

ADVANCED MATERIALS (2022)

Article Chemistry, Multidisciplinary

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

Keisuke Takahashi et al.

Summary: This study proposes the combination of high-throughput calculations and catalyst informatics as an alternative method for designing heterogeneous catalysts. By performing calculations on 1972 catalyst surface planes for the oxidative coupling of methane (OCM) reaction, key elements were unveiled and several catalysts for the OCM reaction were designed. The results suggest that designing catalysts using high-throughput calculations is achievable if appropriate trends and patterns within the generated data are identified.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2022)

Article Physics, Multidisciplinary

From atomically resolved imaging to generative and causal models

Sergei Kalinin et al.

Summary: The development of high-resolution imaging methods has provided rich information on the atomic structure and functionalities of solids, necessitating the adaptation of classical macroscopic definitions to understand local imaging data. This data can be used to construct statistical and physical models that generate observed structures. The availability of observational data opens pathways for exploring causal mechanisms underlying solid structure and functionality.

NATURE PHYSICS (2022)

Article Chemistry, Multidisciplinary

Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine

Davide Angelone et al.

Summary: The study demonstrates that the Chemputer synthesis robot can be programmed to perform a variety of reactions, including solid-phase peptide synthesis, iterative cross-coupling, and diazirine chemistry. Developing universal and modular hardware that can be automated makes various batch chemistry accessible. The research also showcases a complex convergent robotic synthesis, highlighting the utility of the Chemputer system.

NATURE CHEMISTRY (2021)

Review Chemistry, Multidisciplinary

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence

Tetiana Zubatiuk et al.

Summary: In this paper, we introduce out-of-the-box approaches to developing transferable MLIPs for diverse chemical tasks, utilizing large training data sets and advanced methods to improve accuracy and flexibility. Different models such as ANI, AIMNet, and ML-EHM are discussed for their ability to encode various interactions and improve generalization of ML models.

ACCOUNTS OF CHEMICAL RESEARCH (2021)

Article Chemistry, Physical

Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms

A. A. Guda et al.

Summary: X-ray absorption near-edge structure (XANES) spectra can be analyzed using machine learning algorithms to establish the relationship between intuitive descriptors of spectra and local atomic and electronic structures, providing a simple and fast tool for extracting structural parameters, successfully demonstrated high prediction quality on experimental data.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Chemistry, Physical

Atomistic Line Graph Neural Network for improved materials property predictions

Kamal Choudhary et al.

Summary: ALIGNN is a graph neural network architecture that explicitly and efficiently includes bond angle information in atomistic prediction tasks, leading to improved performance. The model performs well on multiple atomistic prediction tasks and has a competitive advantage in terms of speed.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Materials Science, Multidisciplinary

Recent progress of the computational 2D materials database (C2DB)

Morten Niklas Gjerding et al.

Summary: The Computational 2D Materials Database (C2DB) is a curated open database with properties for over 4000 2D materials. New materials and properties have been added, including monolayers, bilayers, defects, and various characteristics. With open access, detailed documentation, and rich data, C2DB is a unique resource for advancing the science of atomically thin materials.

2D MATERIALS (2021)

Article Multidisciplinary Sciences

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Vishu Gupta et al.

Summary: Artificial intelligence and machine learning are increasingly used in materials science, but limited availability of large datasets for most properties hinders their widespread application. Researchers propose a cross-property deep-transfer-learning framework to build accurate models with limited data.

NATURE COMMUNICATIONS (2021)

Article Chemistry, Multidisciplinary

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

Lauren Takahashi et al.

Summary: Research on designing high-performance catalysts for the oxidative coupling of methane is often hindered by inconsistent data, but high throughput experiments provide a systematic way to produce catalyst-related data. By applying graph theory to visualize trends in data transformation, new catalysts can be designed to achieve high C-2 yields, resulting in the successful design of numerous efficient catalysts.

CHEMICAL SCIENCE (2021)

Article Multidisciplinary Sciences

Ultrafast machine vision with 2D material neural network image sensors

Lukas Mennel et al.

NATURE (2020)

Article Chemistry, Physical

High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

Thanh Nhat Nguyen et al.

ACS CATALYSIS (2020)

Article Chemistry, Physical

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

Anthony Yu-Tung Wang et al.

CHEMISTRY OF MATERIALS (2020)

Article Chemistry, Multidisciplinary

Toward On-Demand Materials Synthesis and Scientific Discovery through Intelligent Robots

Jiagen Li et al.

ADVANCED SCIENCE (2020)

Article Chemistry, Physical

Material Descriptors for the Discovery of Efficient Thermoelectrics

Patrizio Graziosi et al.

ACS APPLIED ENERGY MATERIALS (2020)

Article Chemistry, Multidisciplinary

Catalyst Acquisition by Data Science (CADS): a web-based catalyst informatics platform for discovering catalysts

Jun Fujima et al.

REACTION CHEMISTRY & ENGINEERING (2020)

Article Physics, Fluids & Plasmas

Learning to grow: Control of material self-assembly using evolutionary reinforcement learning

Stephen Whitelam et al.

PHYSICAL REVIEW E (2020)

Review Nanoscience & Nanotechnology

Deep learning analysis on microscopic imaging in materials science

M. Ge et al.

MATERIALS TODAY NANO (2020)

Review Chemistry, Multidisciplinary

Universal Chemical Synthesis and Discovery with 'The Chemputer'

Piotr S. Gromski et al.

TRENDS IN CHEMISTRY (2020)

Article Chemistry, Multidisciplinary

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

Hironao Yamada et al.

ACS CENTRAL SCIENCE (2019)

Article Chemistry, Physical

Visualizing Scientists' Cognitive Representation of Materials Data through the Application of Ontology

Lauren Takahashi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

Article Chemistry, Physical

Automatic oxidation threshold recognition of XAFS data using supervised machine learning

Itsuki Miyazato et al.

MOLECULAR SYSTEMS DESIGN & ENGINEERING (2019)

Article Materials Science, Multidisciplinary

Matminer: An open source toolkit for materials data mining

Logan Ward et al.

COMPUTATIONAL MATERIALS SCIENCE (2018)

Article Nanoscience & Nanotechnology

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Nicolas Mounet et al.

NATURE NANOTECHNOLOGY (2018)

Article Instruments & Instrumentation

The challenge of constructing an international XAFSdatabase

Kiyotaka Asakura et al.

JOURNAL OF SYNCHROTRON RADIATION (2018)

Article Chemistry, Physical

A strategy to apply machine learning to small datasets in materials science

Ying Zhang et al.

NPJ COMPUTATIONAL MATERIALS (2018)

Article Chemistry, Medicinal

Redesigning the Materials and Catalysts Database Construction Process Using Ontologies

Lauren Takahashi et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Article Materials Science, Multidisciplinary

NOMAD: The FAIR concept for big data-driven materials science

Claudia Draxl et al.

MRS BULLETIN (2018)

Article Multidisciplinary Sciences

Controlling an organic synthesis robot with machine learning to search for new reactivity

Jaroslaw M. Granda et al.

NATURE (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Chemistry, Physical

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Janis Timoshenko et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)

Article Multidisciplinary Sciences

Density functional theory is straying from the path toward the exact functional

Michael G. Medvedev et al.

SCIENCE (2017)

Article Multidisciplinary Sciences

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Zachary W. Ulissi et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition

Mohammad Hadi Modarres et al.

SCIENTIFIC REPORTS (2017)

Article Chemistry, Inorganic & Nuclear

Materials informatics: a journey towards material design and synthesis

Keisuke Takahashi et al.

DALTON TRANSACTIONS (2016)

Article Chemistry, Physical

Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

Lei Cheng et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Physics, Multidisciplinary

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli et al.

PHYSICAL REVIEW LETTERS (2015)

Article Materials Science, Multidisciplinary

AFLOW: An automatic framework for high-throughput materials discovery

Stefano Curtarolo et al.

COMPUTATIONAL MATERIALS SCIENCE (2012)

Article Computer Science, Interdisciplinary Applications

The Computational Materials Repository

David D. Landis et al.

COMPUTING IN SCIENCE & ENGINEERING (2012)

Article Materials Science, Multidisciplinary

A high-throughput infrastructure for density functional theory calculations

Anubhav Jain et al.

COMPUTATIONAL MATERIALS SCIENCE (2011)

Article Physics, Applied

Inorganic Materials Database for Exploring the Nature of Material

Yibin Xu et al.

JAPANESE JOURNAL OF APPLIED PHYSICS (2011)

Review Chemistry, Applied

Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the Art

Radislav Potyrailo et al.

ACS COMBINATORIAL SCIENCE (2011)

Article Materials Science, Multidisciplinary

Materials informatics

Krishna Rajan

MATERIALS TODAY (2005)