DScribe: Library of descriptors for machine learning in materials science (original) (raw)
Crystal structure representations for machine learning models of formation energies
Rickard Armiento
International Journal of Quantum Chemistry, 2015
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ML4Chem: A Machine Learning Package for Chemistry and Materials Science
Muammar W El Khatib Rodriguez
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A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
Michael Bussmann
2021
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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Daniele GiofreĢ
The Journal of Chemical Physics
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FLAME: A library of atomistic modeling environments
alireza ghasemi
Computer Physics Communications, 2020
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Dataset and scripts for A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
Michael Bussmann
2021
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Machine learning of ab-initio energy landscapes for crystal structure predictions
joshua gabriel
Computational Materials Science, 2018
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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Wiktor Pronobis
Journal of Physical Chemistry Letters, 2015
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Toward machine learning for microscopic mechanisms: A formula search for crystal structure stability based on atomic properties
Loriano Storchi
Journal of Applied Physics, 2022
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Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
Kamal Choudhary
Physical Review Materials
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
O. Von Lilienfeld, Matthias Rupp
Physical Review Letters, 2012
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A universal model for the formation energy prediction of inorganic compounds
fankai xie
2021
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Compact atomic descriptors enable accurate predictions via linear models
Stefano de gironcoli
The Journal of Chemical Physics, 2021
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Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
amar krishna
Physical Review B, 2017
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Opportunities and Challenges for Machine Learning in Materials Science
Ms. KANUPRIYA KHANDELWAL
Annual Review of Materials Research, 2020
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Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds
Kamal Choudhary
Physical Review Materials
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Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
Wiktor Pronobis
Journal of Chemical Theory and Computation, 2018
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Theoretical prediction of properties of atomistic systems: Density functional theory and machine learning
Alexander Lindmaa
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Trocadero: a multiple-algorithm multiple-model atomistic simulation program
Elena Gonzalez Hernandez
2003
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Applying machine learning techniques to predict the properties of energetic materials
Daniel C Elton
Scientific Reports, 2018
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A universal model for accurately predicting the formation energy of inorganic compounds
fankai xie
Science China Materials
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Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules
Igor Poltavsky
The Journal of Chemical Physics, 2021
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Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
Alexander Balatsky
Advanced Quantum Technologies, 2019
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BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules
Yashaswi Pathak
2019
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The prediction of topologically partitioned intra-atomic and inter-atomic energies by the machine learning method kriging
Paul Popelier
Theoretical Chemistry Accounts, 2016
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Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
Andrew Ferguson
Journal of Chemical Theory and Computation, 2020
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Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain
HSUAN MING YU
Physical Review B
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Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset
Gabriel Pinheiro
The Journal of Physical Chemistry A, 2020
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The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
Raphael Finkel
Computational Materials Science
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The Structural Information Filtered Features Potential for Machine Learning calculations of energies and forces of atomic systems
Arturo Hernandez Zeledon
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Towards machine learning for microscopic mechanisms: a formula search for crystal structure stability based on atomic properties
Loriano Storchi
2022
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Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models
Paul Popelier
Journal of Chemical Theory and Computation, 2018
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Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
George Giannakopoulos
2022
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End-to-end AI Framework for Hyperparameter Optimization, Model Training, and Interpretable Inference for Molecules and Crystals
Santanu Chaudhuri
Zenodo (CERN European Organization for Nuclear Research), 2023
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