4.6 Article

Heterogeneous Catalysis in Grammar School

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 Multidisciplinary Sciences

Rapid discovery of stable materials by coordinate-free coarse graining

Rhys E. A. Goodall et al.

Summary: A fundamental challenge in materials science is to understand the relationship between stoichiometry, stability, structure, and property. Recent advances in machine learning have shown that it can be used to accurately predict the stability and functional properties of materials. This study solves the bottleneck of crystal structure identification for previously unidentified materials by coarse-graining the search space of atomic coordinates. The use of Wyckoff representations as inputs to a machine learning model achieves high precision in finding unknown theoretically stable materials.

SCIENCE ADVANCES (2022)

Article Chemistry, Physical

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot et al.

Summary: The OC20 dataset provides rich information on catalysts, offering more data support for building machine learning models. By demonstrating the baseline with three graph neural network models, it provides a direction for further research in the catalysis community. The dataset and baseline models are provided as open resources, encouraging the community to work together to solve these important tasks.

ACS CATALYSIS (2021)

Article Chemistry, Multidisciplinary

Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

AkshatKumar Nigam et al.

Summary: The study introduces a new algorithm called STONED, which achieves performance comparable to deep generative models in the chemical space through interpolation and exploration without the need for large amounts of data and training time.

CHEMICAL SCIENCE (2021)

Article Computer Science, Artificial Intelligence

Mapping the space of chemical reactions using attention-based neural networks

Philippe Schwaller et al.

Summary: This study demonstrates how transformer-based models can infer reaction classes from non-annotated text-based representations of chemical reactions with an accuracy of 98.2%. The learned representations can be used as reaction fingerprints to capture fine-grained differences between reaction classes better than traditional fingerprints. Insights into chemical reaction space are illustrated through an interactive reaction atlas providing visual clustering and similarity searching.

NATURE MACHINE INTELLIGENCE (2021)

Article Multidisciplinary Sciences

Inverse design of porous materials using artificial neural networks

Baekjun Kim et al.

SCIENCE ADVANCES (2020)

Article Chemistry, Physical

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

Geun Ho Gu et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Computer Science, Artificial Intelligence

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Mario Krenn et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Chemistry, Multidisciplinary

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

Jidon Jang et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2020)

Article Chemistry, Physical

Beyond Scaling Relations for the Description of Catalytic Materials

Mie Andersen et al.

ACS CATALYSIS (2019)

Review Chemistry, Physical

First-principles-based multiscale modelling of heterogeneous catalysis

Albert Bruix et al.

NATURE CATALYSIS (2019)

Article Chemistry, Multidisciplinary

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

Philippe Schwaller et al.

ACS CENTRAL SCIENCE (2019)

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 Chemistry, Multidisciplinary

Computational Screening of All Stoichiometric Inorganic Materials

Daniel W. Davies et al.

Article Multidisciplinary Sciences

The thermodynamic scale of inorganic crystalline metastability

Wenhao Sun et al.

SCIENCE ADVANCES (2016)

Article Computer Science, Artificial Intelligence

A survey of grammatical inference methods for natural language learning

Arianna D'Ulizia et al.

ARTIFICIAL INTELLIGENCE REVIEW (2011)

Review Chemistry, Multidisciplinary

Towards the computational design of solid catalysts

J. K. Norskov et al.

NATURE CHEMISTRY (2009)

Article Chemistry, Multidisciplinary

Where are genes in paulingite? Mathematical principles of formation of inorganic materials on the atomic level

V. Ya. Shevchenko et al.

STRUCTURAL CHEMISTRY (2008)

Review Chemistry, Multidisciplinary

Reticular chemistry: Occurrence and taxonomy of nets and grammar for the design of frameworks

NW Ockwig et al.

ACCOUNTS OF CHEMICAL RESEARCH (2005)

Article Chemistry, Physical

The Bronsted-Evans-Polanyi relation and the volcano curve in heterogeneous catalysis

T Bligaard et al.

JOURNAL OF CATALYSIS (2004)