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Greatest papers with code

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 $\sim 60, 000$ 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 MATERIALS SCIENCE COMPUTATIONAL PHYSICS

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 MATERIALS SCIENCE

DScribe: Library of Descriptors for Machine Learning in Materials Science

18 Apr 2019SINGROUP/dscribe

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations.

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

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 CHEMICAL PHYSICS

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