标题
Open Catalyst 2020 (OC20) Dataset and Community Challenges
作者
关键词
-
出版物
ACS Catalysis
Volume -, Issue -, Pages 6059-6072
出版商
American Chemical Society (ACS)
发表日期
2021-05-05
DOI
10.1021/acscatal.0c04525
参考文献
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。- Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
- (2020) Fabian Jirasek et al. Journal of Physical Chemistry Letters
- A Critical Review of Machine Learning of Energy Materials
- (2020) Chi Chen et al. Advanced Energy Materials
- Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation
- (2020) Geun Ho Gu et al. ADVANCED MATERIALS
- Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening
- (2020) Geun Ho Gu et al. Journal of Physical Chemistry Letters
- Completing density functional theory by machine learning hidden messages from molecules
- (2020) Ryo Nagai et al. npj Computational Materials
- LOBSTER : Local orbital projections, atomic charges, and chemical‐bonding analysis from projector‐augmented‐wave‐based density‐functional theory
- (2020) Ryky Nelson et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Extended tight‐binding quantum chemistry methods
- (2020) Christoph Bannwarth et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- Exploring chemical compound space with quantum-based machine learning
- (2020) O. Anatole von Lilienfeld et al. Nature Reviews Chemistry
- Recursive evaluation and iterative contraction of N-body equivariant features
- (2020) Jigyasa Nigam et al. JOURNAL OF CHEMICAL PHYSICS
- Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys
- (2020) Jacques A. Esterhuizen et al. Chem
- Machine-learned metrics for predicting the likelihood of success in materials discovery
- (2020) Yoolhee Kim et al. npj Computational Materials
- Solving the electronic structure problem with machine learning
- (2019) Anand Chandrasekaran et al. npj Computational Materials
- Beyond Scaling Relations for the Description of Catalytic Materials
- (2019) Mie Andersen et al. ACS Catalysis
- Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation
- (2019) Jacob R. Boes et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Prediction of atomization energy using graph kernel and active learning
- (2019) Yu-Hang Tang et al. JOURNAL OF CHEMICAL PHYSICS
- Machine Learning for Computational Heterogeneous Catalysis
- (2019) Philomena Schlexer Lamoureux et al. ChemCatChem
- Non-iterative structural topology optimization using deep learning
- (2019) Baotong Li et al. COMPUTER-AIDED DESIGN
- High-throughput calculations of catalytic properties of bimetallic alloy surfaces
- (2019) Osman Mamun et al. Scientific Data
- Catalysis-Hub.org, an open electronic structure database for surface reactions
- (2019) Kirsten T. Winther et al. Scientific Data
- Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
- (2019) Seoin Back et al. Journal of Physical Chemistry Letters
- Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis
- (2019) Sebastian Matera et al. ACS Catalysis
- Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks
- (2019) Geng Sun et al. Journal of Chemical Theory and Computation
- Inverting” X-ray Absorption Spectra of Catalysts by Machine Learning in Search for Activity Descriptors
- (2019) Janis Timoshenko et al. ACS Catalysis
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- Artificial Intelligence to Accelerate the Discovery of N2 Electroreduction Catalysts
- (2019) Myungjoon Kim et al. CHEMISTRY OF MATERIALS
- Machine learning for predicting thermodynamic properties of pure fluids and their mixtures
- (2019) Yuanbin Liu et al. ENERGY
- Machine Learning for Catalysis Informatics: Recent Applications and Prospects
- (2019) Takashi Toyao et al. ACS Catalysis
- Machine learning for heterogeneous catalyst design and discovery
- (2018) Bryan R. Goldsmith et al. AICHE JOURNAL
- Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity
- (2018) Geng Sun et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Active learning with non-ab initio input features toward efficient CO2 reduction catalysts
- (2018) Juhwan Noh et al. Chemical Science
- Extracting Knowledge from Data through Catalysis Informatics
- (2018) Andrew J. Medford et al. ACS Catalysis
- Accelerating the discovery of materials for clean energy in the era of smart automation
- (2018) Daniel P. Tabor et al. Nature Reviews Materials
- Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection
- (2018) Geun Ho Gu et al. Reaction Chemistry & Engineering
- An electronic structure descriptor for oxygen reactivity at metal and metal-oxide surfaces
- (2018) Colin F. Dickens et al. SURFACE SCIENCE
- Neural network predictions of oxygen interactions on a dynamic Pd surface
- (2017) Jacob R. Boes et al. MOLECULAR SIMULATION
- Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts
- (2017) Xianfeng Ma et al. PHYSICAL REVIEW LETTERS
- Combining theory and experiment in electrocatalysis: Insights into materials design
- (2017) Zhi Wei Seh et al. SCIENCE
- To address surface reaction network complexity using scaling relations machine learning and DFT calculations
- (2017) Zachary W. Ulissi et al. Nature Communications
- High-throughput screening of bimetallic catalysts enabled by machine learning
- (2017) Zheng Li et al. Journal of Materials Chemistry A
- Application of Artificial Neural Networks for Catalysis: A Review
- (2017) Hao Li et al. Catalysts
- Amp : A modular approach to machine learning in atomistic simulations
- (2016) Alireza Khorshidi et al. COMPUTER PHYSICS COMMUNICATIONS
- Acceleration of saddle-point searches with machine learning
- (2016) Andrew A. Peterson JOURNAL OF CHEMICAL PHYSICS
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
- (2015) Scott Kirklin et al. npj Computational Materials
- Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers
- (2014) Federico Calle-Vallejo et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- The Catalyst Genome
- (2012) Jens K. Nørskov et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
- (2012) Shyue Ping Ong et al. COMPUTATIONAL MATERIALS SCIENCE
- CatApp: A Web Application for Surface Chemistry and Heterogeneous Catalysis
- (2011) Jens S. Hummelshøj et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Crystal Orbital Hamilton Population (COHP) Analysis As Projected from Plane-Wave Basis Sets
- (2011) Volker L. Deringer et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks
- (2009) A. Pukrittayakamee et al. JOURNAL OF CHEMICAL PHYSICS
- A grid-based Bader analysis algorithm without lattice bias
- (2009) W Tang et al. JOURNAL OF PHYSICS-CONDENSED MATTER
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreDiscover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversation