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Formation Energy

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Neural Message Passing for Quantum Chemistry

ICML 2017 Microsoft/gated-graph-neural-network-samples

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

#2 best model for Drug Discovery on QM9

DRUG DISCOVERY FORMATION ENERGY GRAPH REGRESSION MOLECULAR PROPERTY PREDICTION NODE CLASSIFICATION

SchNet - a deep learning architecture for molecules and materials

J. Chem. Phys. 2017 atomistic-machine-learning/schnetpack

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.

DRUG DISCOVERY FORMATION ENERGY IMAGE RETRIEVAL SPEECH RECOGNITION

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

NeurIPS 2017 atomistic-machine-learning/schnetpack

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.

FORMATION ENERGY

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chem. Mater. 2018 materialsvirtuallab/megnet

Similarly, we show that MEGNet models trained on ∼60, 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

DRUG DISCOVERY FORMATION ENERGY RELATIONAL REASONING

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Phys. Rev. Lett. 2017 txie-93/cgcnn

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.

BAND GAP FORMATION ENERGY

Directional Message Passing for Molecular Graphs

ICLR 2020 klicperajo/dimenet

We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them.

DRUG DISCOVERY FORMATION ENERGY

Crystal Graph Neural Networks for Data Mining in Materials Science

Technical report, RIMCS LLC 2019 Tony-Y/cgnn

This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.

BAND GAP FORMATION ENERGY MATERIALS SCREENING TOTAL MAGNETIZATION

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction

14 Nov 2018soumyasanyal/mt-cgcnn

Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.

BAND GAP FORMATION ENERGY MULTI-TASK LEARNING

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

8 Jun 2018toshi-k/kaggle-champs-scalar-coupling

Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.

DRUG DISCOVERY FORMATION ENERGY

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges

J. Chem. Theory Comput. 2019 MMunibas/PhysNet

Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.

DRUG DISCOVERY FORMATION ENERGY