Machine learning for molecular and materials science

Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

(2018) Tomasz Klucznik et al.   Chem

Planning chemical syntheses with deep neural networks and symbolic AI

(2018) Marwin H. S. Segler et al.   NATURE

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods

(2017) Fleur Legrain et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Functional materials discovery using energy–structure–function maps

(2017) Angeles Pulido et al.   NATURE

Quantum machine learning

(2017) Jacob Biamonte et al.   NATURE

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Elucidating reaction mechanisms on quantum computers

(2017) Markus Reiher et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

An autonomous organic reaction search engine for chemical reactivity

(2017) Vincenza Dragone et al.   Nature Communications

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

(2017) Bowen Liu et al.   ACS Central Science

Low Data Drug Discovery with One-Shot Learning

(2017) Han Altae-Tran et al.   ACS Central Science

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Learning surface molecular structures via machine vision

(2017) Maxim Ziatdinov et al.   npj Computational Materials

Computer-Assisted Synthetic Planning: The End of the Beginning

(2016) Sara Szymkuć et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

(2016) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

ωB97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation

(2016) Narbe Mardirossian et al.   JOURNAL OF CHEMICAL PHYSICS

Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

(2016) Igor V. Tetko et al.   Molecular Informatics

Materials science with large-scale data and informatics: Unlocking new opportunities

(2016) Joanne Hill et al.   MRS BULLETIN

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

(2016) Rafael Gómez-Bombarelli et al.   NATURE MATERIALS

Quantum-Enhanced Machine Learning

(2016) Vedran Dunjko et al.   PHYSICAL REVIEW LETTERS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Generation of crystal structures using known crystal structures as analogues

(2016) Jason C. Cole et al.   Acta Crystallographica Section B-Structural Science Crystal Engineering and Materials

Computational Screening of All Stoichiometric Inorganic Materials

(2016) Daniel W. Davies et al.   Chem

The AFLOW standard for high-throughput materials science calculations

(2015) Camilo E. Calderon et al.   COMPUTATIONAL MATERIALS SCIENCE

Will it crystallise? Predicting crystallinity of molecular materials

(2015) Jerome G. P. Wicker et al.   CRYSTENGCOMM

ZINC 15 – Ligand Discovery for Everyone

(2015) Teague Sterling et al.   Journal of Chemical Information and Modeling

Application of text mining in the biomedical domain

(2015) Wilco W.M. Fleuren et al.   METHODS

The quest for new functionality

(2015) Aron Walsh   Nature Chemistry

Big–deep–smart data in imaging for guiding materials design

(2015) Sergei V. Kalinin et al.   NATURE MATERIALS

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Human-level concept learning through probabilistic program induction

(2015) B. M. Lake et al.   SCIENCE

Identifying systematic DFT errors in catalytic reactions

(2015) Rune Christensen et al.   Catalysis Science & Technology

Hybrid MPI-OpenMP Parallelism in the ONETEP Linear-Scaling Electronic Structure Code: Application to the Delamination of Cellulose Nanofibrils

(2014) Karl A. Wilkinson et al.   Journal of Chemical Theory and Computation

Stable and Efficient Linear Scaling First-Principles Molecular Dynamics for 10000+ Atoms

(2014) Michiaki Arita et al.   Journal of Chemical Theory and Computation

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure-Activity Modeling and Dataset Comparison

(2012) N. Kireeva et al.   Molecular Informatics

Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation

(2012) Jess Wellendorff et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

(2011) Johannes Hachmann et al.   Journal of Physical Chemistry Letters

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

(2010) Denis Fourches et al.   Journal of Chemical Information and Modeling

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Best Practices for QSAR Model Development, Validation, and Exploitation

(2010) Alexander Tropsha   Molecular Informatics

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Efficient integration for all-electron electronic structure calculation using numeric basis functions

(2009) V. Havu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Quantum Algorithm for Linear Systems of Equations

(2009) Aram W. Harrow et al.   PHYSICAL REVIEW LETTERS

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network

(2008) Victor Hugo C. de Albuquerque et al.   Nondestructive Testing and Evaluation