4.8 Review

Ab Initio Machine Learning in Chemical Compound Space

Related references

Note: Only part of the references are listed.
Article Chemistry, Physical

Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

Charles D. Griego et al.

Summary: This tutorial introduces an easy and cost-efficient calculation scheme APDFT, which can rapidly predict binding energies of reaction intermediates and reaction barrier heights, aiding in accelerating the search for novel catalysts. The APDFT scheme brings no appreciable computational cost once reference calculations are performed, showing significant impact on DFT-driven catalyst explorations.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2021)

Review Chemistry, Multidisciplinary

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

Sergei Manzhos et al.

Summary: This review focuses on the development of neural network-based methods for constructing molecular potential energy surfaces that explicitly include all many-body contributions. Various approaches including single NN PES fitting and more complex methods are discussed, highlighting the effectiveness of NNs in building representations with low-dimensional functions and emerging tools for accurate PESs in relatively large molecular systems.

CHEMICAL REVIEWS (2021)

Article Chemistry, Multidisciplinary

A DFT/machine-learning hybrid method for the prediction of3JHCCHcouplings

Armando Navarro-Vazquez

Summary: A machine learning model has been developed for predicting vicinal proton-proton couplings with accuracy comparable or better than the Altona equation, especially in systems like epoxide or cyclopropane rings where the Altona equation may not perform well.

MAGNETIC RESONANCE IN CHEMISTRY (2021)

Review Chemistry, Multidisciplinary

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr et al.

Summary: This review focuses on how machine learning is used to speed up excited-state simulations and advance the research field. Applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, and others.

CHEMICAL REVIEWS (2021)

Article Chemistry, Physical

Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory

Apurba Nandi et al.

Summary: The study presents a machine learning approach to improve the accuracy of potential energy surfaces based on low-level density functional theory energies and gradients. By using a simple equation and fitting high-dimensional PESs with the permutationally invariant polynomial method, the approach is demonstrated to be effective for multiple molecules. Results show excellent agreement with benchmark results, even with a relatively small number of CCSD(T) energies used for training.

JOURNAL OF CHEMICAL PHYSICS (2021)

Article Multidisciplinary Sciences

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

Johannes Hoja et al.

Summary: The QM7-X dataset contains approximately 4.2 million equilibrium and non-equilibrium structures of small organic molecules, with a comprehensive coverage of various physicochemical properties. It is expected to play a critical role in the development of next-generation machine-learning models for exploring broader regions of chemical compound space and designing molecules with targeted properties.

SCIENTIFIC DATA (2021)

Article Multidisciplinary Sciences

Quantum chemical benchmark databases of gold-standard dimer interaction energies

Alexander G. Donchev et al.

Summary: This paper introduces three benchmark collections of quantum mechanical data encompassing approximately 3,700 different types of noncovalent molecular interactions. These datasets can be useful for research in electronic structure theory and computational chemistry.

SCIENTIFIC DATA (2021)

Article Chemistry, Physical

Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot et al.

Summary: The OC20 dataset provides rich information on catalysts, offering more data support for building machine learning models. By demonstrating the baseline with three graph neural network models, it provides a direction for further research in the catalysis community. The dataset and baseline models are provided as open resources, encouraging the community to work together to solve these important tasks.

ACS CATALYSIS (2021)

Article Multidisciplinary Sciences

Simplifying inverse materials design problems for fixed lattices with alchemical chirality

Guido Falk von Rudorff et al.

Summary: The study shows that four-dimensional chirality arising from the antisymmetry of alchemical perturbations dissects chemical compound space and defines approximate ranks, reducing its dimensionality and breaking down its combinatorial scaling. Alchemical chirality deepens our understanding of chemical compound space and enables the establishment of trends for materials with fixed lattices without the need for empirical rules.

SCIENCE ADVANCES (2021)

Article Computer Science, Artificial Intelligence

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

Amit Gupta et al.

Summary: This study presents a machine learning strategy and accurate reference dataset for accelerated and accurate screening of nuclear magnetic resonance spectra. By creating the QM9-NMR dataset and using kernel-ridge regression models, isotropic shielding was successfully predicted, and validation was performed on non-trivial benchmark sets.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Article Materials Science, Multidisciplinary

Machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery

Andrew S. Rosen et al.

Summary: This study introduces a Quantum MOF (QMOF) database, which contains computed quantum-chemical properties for over 14,000 experimentally synthesized MOFs. Machine learning models trained on this database can efficiently discover MOFs with targeted properties, with the successful prediction of MOFs with low band gaps being highlighted.

MATTER (2021)

Article Computer Science, Artificial Intelligence

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Behnam Parsaeifard et al.

Summary: The study compares the performance of different atomic environment fingerprints and their ability to resolve differences in local environments, while also examining the related atomic movements and fingerprint invariance.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Review Chemistry, Multidisciplinary

Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques

Janine George et al.

Summary: Chemical heuristics play a crucial role in the advancement of chemistry and materials science, while machine learning offers opportunities to enhance this approach. There are relationships between traditional heuristics and machine learning approaches, and they work best when integrated.

TRENDS IN CHEMISTRY (2021)

Article Chemistry, Multidisciplinary

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part A. Stage Setting

Roald Hoffmann et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2020)

Article Chemistry, Multidisciplinary

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse

Roald Hoffmann et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2020)

Article Chemistry, Multidisciplinary

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part C. Toward Consilience

Roald Hoffmann et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2020)

Article Chemistry, Physical

Machine Learning for Molecular Simulation

Frank Noé et al.

Annual Review of Physical Chemistry (2020)

Review Chemistry, Physical

The Exploration of Chemical Reaction Networks

Jan P. Unsleber et al.

ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 71 (2020)

Article Chemistry, Physical

FCHL revisited: Faster and more accurate quantum machine learning

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Article Chemistry, Physical

Noncovalent Quantum Machine Learning Corrections to Density Functionals

Pal D. Mezei et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

Article Chemistry, Physical

Quantum Chemistry in the Age of Machine Learning

Pavlo O. Dral

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Multidisciplinary Sciences

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Annika Stuke et al.

SCIENTIFIC DATA (2020)

Article Chemistry, Physical

Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders

Nicolae C. Iovanac et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Article Chemistry, Physical

Predicting Deprotonation Sites Using Alchemical Derivatives

Macarena Munoz et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Article Chemistry, Physical

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Julia Westermayr et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Multidisciplinary Sciences

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Colin A. Grambow et al.

SCIENTIFIC DATA (2020)

Review Chemistry, Multidisciplinary

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka et al.

CHEMICAL REVIEWS (2020)

Article Nanoscience & Nanotechnology

Machine learning approaches for the prediction of materials properties

Siwar Chibani et al.

APL MATERIALS (2020)

Article Chemistry, Medicinal

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Xiang Gao et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Article Chemistry, Physical

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Kevin Rossi et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

Review Chemistry, Multidisciplinary

Exploring chemical compound space with quantum-based machine learning

O. Anatole von Lilienfeld et al.

NATURE REVIEWS CHEMISTRY (2020)

Article Chemistry, Medicinal

Transfer Learning for Drug Discovery

Chenjing Cai et al.

JOURNAL OF MEDICINAL CHEMISTRY (2020)

Article Chemistry, Physical

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Ryosuke Jinnouchi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Chemistry, Physical

Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks

Martin Stoehr et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

Article Multidisciplinary Sciences

Understanding the diversity of the metal-organic framework ecosystem

Seyed Mohamad Moosavi et al.

NATURE COMMUNICATIONS (2020)

Article Chemistry, Physical

Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet

Silvan Kaeser et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Article Chemistry, Physical

Multifidelity Statistical Machine Learning for Molecular Crystal Structure Prediction

Olga Egorova et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2020)

Article Chemistry, Physical

Nonempirical Definition of the Mendeleev Numbers: Organizing the Chemical Space

Zahed Allahyari et al.

JOURNAL OF PHYSICAL CHEMISTRY C (2020)

Article Multidisciplinary Sciences

Evidence for supercritical behaviour of high-pressure liquid hydrogen

Bingqing Cheng et al.

NATURE (2020)

Article Chemistry, Multidisciplinary

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

Bing Huang et al.

NATURE CHEMISTRY (2020)

Article Chemistry, Multidisciplinary

Deep-neural-network solution of the electronic Schrodinger equation

Jan Hermann et al.

NATURE CHEMISTRY (2020)

Article Multidisciplinary Sciences

Quantum chemical accuracy from density functional approximations via machine learning

Mihail Bogojeski et al.

NATURE COMMUNICATIONS (2020)

Editorial Material Multidisciplinary Sciences

Retrospective on a decade of machine learning for chemical discovery

O. Anatole von Lilienfeld et al.

NATURE COMMUNICATIONS (2020)

Article Multidisciplinary Sciences

Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates

Giorgio Pesciullesi et al.

NATURE COMMUNICATIONS (2020)

Editorial Material Multidisciplinary Sciences

Machine learning for chemical discovery

Alexandre Tkatchenko

NATURE COMMUNICATIONS (2020)

Article Multidisciplinary Sciences

Materials Cloud, a platform for open computational science

Leopold Talirz et al.

SCIENTIFIC DATA (2020)

Article Multidisciplinary Sciences

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

Sebastiaan P. Huber et al.

SCIENTIFIC DATA (2020)

Article Chemistry, Physical

Machine learning with bond information for local structure optimizations in surface science

Estefania Garijo del Rio et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Article Chemistry, Physical

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Osman Mamun et al.

NPJ COMPUTATIONAL MATERIALS (2020)

Article Computer Science, Artificial Intelligence

Wasserstein metric for improved quantum machine learning with adjacency matrix representations

Onur Caylak et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Chemistry, Multidisciplinary

Data enhanced Hammett-equation: reaction barriers in chemical space

Marco Bragato et al.

CHEMICAL SCIENCE (2020)

Article Chemistry, Medicinal

tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes

David Balcells et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Article Chemistry, Physical

Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies

Jacob Townsend et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

Article Physics, Multidisciplinary

Alchemical perturbation density functional theory

Guido Falk von Rudorff et al.

PHYSICAL REVIEW RESEARCH (2020)

Article Computer Science, Artificial Intelligence

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

Mario Krenn et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

On the role of gradients for machine learning of molecular energies and forces

Anders S. Christensen et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

Thousands of reactants and transition states for competing E2 and SN2 reactions

Guido Falk Von Rudorff et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Computer Science, Artificial Intelligence

Machine learning and excited-state molecular dynamics

Julia Westermayr et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Chemistry, Physical

Rapid and accurate molecular deprotonation energies from quantum alchemy

Guido Falk von Rudorff et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)

Review Chemistry, Multidisciplinary

QSAR without borders

Eugene N. Muratov et al.

CHEMICAL SOCIETY REVIEWS (2020)

Review Physics, Multidisciplinary

Machine learning for quantum matter

Juan Carrasquilla

ADVANCES IN PHYSICS-X (2020)

Article Computer Science, Artificial Intelligence

Machine learning the computational cost of quantum chemistry

Stefan Heinen et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Article Materials Science, Multidisciplinary

Locality meets machine learning: Excited and ground-state energy surfaces of large systems at the cost of small ones

Mahboobeh Babaei et al.

PHYSICAL REVIEW B (2020)

Article Chemistry, Multidisciplinary

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

Philippe Schwaller et al.

CHEMICAL SCIENCE (2020)

Article Chemistry, Physical

Predicting Chemical Reaction Barriers with a Machine Learning Model

Aayush R. Singh et al.

CATALYSIS LETTERS (2019)

Article Computer Science, Interdisciplinary Applications

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Stefan Chmiela et al.

COMPUTER PHYSICS COMMUNICATIONS (2019)

Article Chemistry, Physical

Operators in quantum machine learning: Response properties in chemical space

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Article Multidisciplinary Sciences

Ligand biological activity predicted by cleaning positive and negative chemical correlations

Alpha A. Lee et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Multidisciplinary Sciences

Accurate molecular polarizabilities with coupled cluster theory and machine learning

David M. Wilkins et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Chemistry, Multidisciplinary

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Kunal Ghosh et al.

ADVANCED SCIENCE (2019)

Article Materials Science, Multidisciplinary

Active learning of uniformly accurate interatomic potentials for materials simulation

Linfeng Zhang et al.

PHYSICAL REVIEW MATERIALS (2019)

Article Nanoscience & Nanotechnology

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Rohit Batra et al.

ACS APPLIED MATERIALS & INTERFACES (2019)

Review Chemistry, Physical

Machine Learning for Computational Heterogeneous Catalysis

Philomena Schlexer Lamoureux et al.

CHEMCATCHEM (2019)

Review Chemistry, Multidisciplinary

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

Jessica G. Freeze et al.

CHEMICAL REVIEWS (2019)

Article Chemistry, Physical

Atom-density representations for machine learning

Michael J. Willatt et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Article Chemistry, Physical

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

Oliver T. Unke et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Article Chemistry, Multidisciplinary

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Jiang Wang et al.

ACS CENTRAL SCIENCE (2019)

Article Chemistry, Physical

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

Annika Stuke et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Editorial Material Biochemical Research Methods

Promoting transparency and reproducibility in enhanced molecular simulations

Massimiliano Bonomi et al.

NATURE METHODS (2019)

Article Multidisciplinary Sciences

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Justin S. Smith et al.

NATURE COMMUNICATIONS (2019)

Article Chemistry, Multidisciplinary

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

Philippe Schwaller et al.

ACS CENTRAL SCIENCE (2019)

Review Chemistry, Physical

Recent advances and applications of machine learning in solid-state materials science

Jonathan Schmidt et al.

NPJ COMPUTATIONAL MATERIALS (2019)

Article Chemistry, Physical

Gaussian Process-Based Refinement of Dispersion Corrections

Jonny Proppe et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Article Chemistry, Physical

Atoms in Molecules from Alchemical Perturbation Density Functional Theory

Guido Falk von Rudorff et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2019)

Article Multidisciplinary Sciences

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K. T. Schuett et al.

NATURE COMMUNICATIONS (2019)

Article Chemistry, Multidisciplinary

Dataset's chemical diversity limits the generalizability of machine learning predictions

Marta Glavatskikh et al.

JOURNAL OF CHEMINFORMATICS (2019)

Article Chemistry, Multidisciplinary

Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

Anders S. Christensen et al.

CHIMIA (2019)

Article Chemistry, Multidisciplinary

Data-driven Chemical Reaction Prediction and Retrosynthesis

Vishnu H. Nair et al.

CHIMIA (2019)

Article Chemistry, Medicinal

Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks

Isidro Cortes-Ciriano et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Article Chemistry, Physical

Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited

Peter Zaspel et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Article Chemistry, Physical

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Felix Musil et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

Article Chemistry, Physical

Alchemical Normal Modes Unify Chemical Space

Stijn Fias et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

Article Biochemistry & Molecular Biology

PubChem 2019 update: improved access to chemical data

Sunghwan Kim et al.

NUCLEIC ACIDS RESEARCH (2019)

Article Multidisciplinary Sciences

Ab initio thermodynamics of liquid and solid water

Bingqing Cheng et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Chemistry, Physical

A Bayesian approach to NMR crystal structure determination

Edgar A. Engel et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2019)

Article Computer Science, Artificial Intelligence

Reconstructing quantum states with generative models

Juan Carrasquilla et al.

NATURE MACHINE INTELLIGENCE (2019)

Article Chemistry, Multidisciplinary

Machine learning enables long time scale molecular photodynamics simulations

Julia Westermayr et al.

CHEMICAL SCIENCE (2019)

Article Materials Science, Multidisciplinary

Local Bayesian optimizer for atomic structures

Estefania Garijo del Rio et al.

PHYSICAL REVIEW B (2019)

Article Chemistry, Multidisciplinary

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Jon Paul Janet et al.

CHEMICAL SCIENCE (2019)

Article Physics, Fluids & Plasmas

Controlled exploration of chemical space by machine learning of coarse-grained representations

Christian Hoffmann et al.

PHYSICAL REVIEW E (2019)

Article Multidisciplinary Sciences

Benchmarking Computational Alchemy for Carbide, Nitride, and Oxide Catalysts

Charles D. Griego et al.

ADVANCED THEORY AND SIMULATIONS (2019)

Article Chemistry, Physical

Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms

James L. McDonagh et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Multidisciplinary

Quantum Machine Learning in Chemical Compound Space

O. Anatole von Lilienfeld

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)

Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Alchemical and structural distribution based representation for universal quantum machine learning

Felix A. Faber et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Machine learning of molecular properties: Locality and active learning

Konstantin Gubaev et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Editorial Material Chemistry, Physical

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

Matthias Rupp et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Less is more: Sampling chemical space with active learning

Justin S. Smith et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Solid harmonic wavelet scattering for predictions of molecule properties

Michael Eickenberg et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Constant size descriptors for accurate machine learning models of molecular properties

Christopher R. Collins et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Physical

Exploring Chemical Space with Alchemical Derivatives: BN-Simultaneous Substitution Patterns in C60

Robert Balawender et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

Julian J. Kranz et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Atomic Energies from a Convolutional Neural Network

Xin Chen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation

Ole Schuett et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Editorial Material Chemistry, Physical

Machine Learning

William F. Schneider et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2018)

Article Chemistry, Physical

Exploration versus Exploitation in Global Atomistic Structure Optimization

Mathias S. Jorgensen et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2018)

Article Nanoscience & Nanotechnology

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

Nicolas Mounet et al.

NATURE NANOTECHNOLOGY (2018)

Article Physics, Multidisciplinary

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Andrea Grisafi et al.

PHYSICAL REVIEW LETTERS (2018)

Article Physics, Multidisciplinary

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

T. L. Jacobsen et al.

PHYSICAL REVIEW LETTERS (2018)

Article Chemistry, Multidisciplinary

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

Kun Yao et al.

CHEMICAL SCIENCE (2018)

Article Chemistry, Multidisciplinary

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

Benjamin Meyer et al.

CHEMICAL SCIENCE (2018)

Article Chemistry, Multidisciplinary

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

ACS CENTRAL SCIENCE (2018)

Article Chemistry, Multidisciplinary

The Matter Simulation (R)evolution

Alan Aspuru-Guzik et al.

ACS CENTRAL SCIENCE (2018)

Article Materials Science, Multidisciplinary

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

Daniele Dragoni et al.

PHYSICAL REVIEW MATERIALS (2018)

Article Chemistry, Physical

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

Matthew Welborn et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

Benjamin Nebgen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

Gregor N. Simm et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

Article Chemistry, Physical

Discovering a Transferable Charge Assignment Model Using Machine Learning

Andrew E. Sifain et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)

Article Chemistry, Physical

Nonadiabatic Excited-State Dynamics with Machine Learning

Pavlo O. Dral et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Multidisciplinary Sciences

Planning chemical syntheses with deep neural networks and symbolic AI

Marwin H. S. Segler et al.

NATURE (2018)

Review Multidisciplinary Sciences

Inverse molecular design using machine learning: Generative models for matter engineering

Benjamin Sanchez-Lengeling et al.

SCIENCE (2018)

Article Multidisciplinary Sciences

Deep neural networks for accurate predictions of crystal stability

Weike Ye et al.

NATURE COMMUNICATIONS (2018)

Article Multidisciplinary Sciences

Towards exact molecular dynamics simulations with machine-learned force fields

Stefan Chmiela et al.

NATURE COMMUNICATIONS (2018)

Article Physics, Multidisciplinary

Extrapolating Quantum Observables with Machine Learning: Inferring Multiple Phase Transitions from Properties of a Single Phase

Rodrigo A. Vargas-Hernandez et al.

PHYSICAL REVIEW LETTERS (2018)

Article Multidisciplinary Sciences

Chemical shifts in molecular solids by machine learning

Federico M. Paruzzo et al.

NATURE COMMUNICATIONS (2018)

Article Chemistry, Medicinal

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry

Maho Nakata et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Article Chemistry, Physical

Exploring dissociative water adsorption on isoelectronically BN doped graphene using alchemical derivatives

Yasmine S. Al-Hamdani et al.

JOURNAL OF CHEMICAL PHYSICS (2017)

Article Chemistry, Physical

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

Felix A. Faber et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)

Article Chemistry, Physical

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

S. T. John et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2017)

Article Chemistry, Physical

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

Kun Yao et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)

Article Chemistry, Physical

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Nicholas J. Browning et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)

Article Chemistry, Physical

Alchemical Predictions for Computational Catalysis: Potential and Limitations

Karthikeyan Saravanan et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)

Article Physics, Multidisciplinary

Machine learning phases of matter

Juan Carrasquilla et al.

NATURE PHYSICS (2017)

Article Chemistry, Physical

How predictive could alchemical derivatives be?

Macarena Munoz et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)

Article Chemistry, Physical

Addressing uncertainty in atomistic machine learning

Andrew A. Peterson et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2017)

Article Multidisciplinary Sciences

Chemical transferability of functional groups follows from the nearsightedness of electronic matter

Stijn Fias et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)

Article Multidisciplinary Sciences

Solving the quantum many-body problem with artificial neural networks

Giuseppe Carleo et al.

SCIENCE (2017)

Article Chemistry, Multidisciplinary

Machine learning molecular dynamics for the simulation of infrared spectra

Michael Gastegger et al.

CHEMICAL SCIENCE (2017)

Article Chemistry, Multidisciplinary

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

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

Article Multidisciplinary Sciences

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Zachary W. Ulissi et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

Article Physics, Multidisciplinary

Machine Learning Phases of Strongly Correlated Fermions

Kelvin Ch'ng et al.

PHYSICAL REVIEW X (2017)

Article Multidisciplinary Sciences

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

Justin S. Smith et al.

SCIENTIFIC DATA (2017)

Article Multidisciplinary Sciences

Machine learning unifies the modeling of materials and molecules

Albert P. Bartok et al.

SCIENCE ADVANCES (2017)

Article Multidisciplinary Sciences

Machine learning of accurate energy-conserving molecular force fields

Stefan Chmiela et al.

SCIENCE ADVANCES (2017)

Article Multidisciplinary Sciences

Machine learning quantum phases of matter beyond the fermion sign problem

Peter Broecker et al.

SCIENTIFIC REPORTS (2017)

Article Materials Science, Multidisciplinary

Active learning of linearly parametrized interatomic potentials

Evgeny V. Podryabinkin et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

Article Chemistry, Multidisciplinary

Synthesis of trinorbornane

Lorenzo Delarue Bizzini et al.

CHEMICAL COMMUNICATIONS (2017)

Review Chemistry, Multidisciplinary

First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

Joerg Behler

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2017)

Article Chemistry, Physical

How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

Fleur Legrain et al.

CHEMISTRY OF MATERIALS (2017)

Article Materials Science, Multidisciplinary

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Logan Ward et al.

PHYSICAL REVIEW B (2017)

Article Chemistry, Physical

Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

Jonathan Schmidt et al.

CHEMISTRY OF MATERIALS (2017)

Article Materials Science, Multidisciplinary

Accurate interatomic force fields via machine learning with covariant kernels

Aldo Glielmo et al.

PHYSICAL REVIEW B (2017)

Article Materials Science, Multidisciplinary

Multi-fidelity machine learning models for accurate bandgap predictions of solids

G. Pilania et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

Article Chemistry, Physical

A fingerprint based metric for measuring similarities of crystalline structures

Li Zhu et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Chemistry, Physical

Fast and accurate predictions of covalent bonds in chemical space

K. Y. Samuel Chang et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Chemistry, Physical

Virtual Screening for High Carrier Mobility in Organic Semiconductors

Christoph Schober et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2016)

Article Chemistry, Physical

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

Zachary W. Ulissi et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2016)

Article Multidisciplinary Sciences

Machine-learning-assisted materials discovery using failed experiments

Paul Raccuglia et al.

NATURE (2016)

Article Physics, Multidisciplinary

The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining

Henning Glawe et al.

NEW JOURNAL OF PHYSICS (2016)

Article Physics, Multidisciplinary

Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals

Felix A. Faber et al.

PHYSICAL REVIEW LETTERS (2016)

Review Chemistry, Physical

The ReaxFF reactive force-field: development, applications and future directions

Thomas P. Senftle et al.

NPJ COMPUTATIONAL MATERIALS (2016)

Article Chemistry, Multidisciplinary

Many Molecular Properties from One Kernel in Chemical Space

Raghunathan Ramakrishnan et al.

CHIMIA (2015)

Editorial Material Chemistry, Physical

Special issue on machine learning and quantum mechanics

Matthias Rupp

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

O. Anatole von Lilienfeld et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Review Chemistry, Physical

Machine learning for quantum mechanics in a nutshell

Matthias Rupp

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Crystal structure representations for machine learning models of formation energies

Felix Faber et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Physical

Reproducing kernel potential energy surfaces in biomolecular simulations: Nitric oxide binding to myoglobin

Maksym Soloviov et al.

JOURNAL OF CHEMICAL PHYSICS (2015)

Article Chemistry, Physical

Electronic spectra from TDDFT and machine learning in chemical space

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL PHYSICS (2015)

Article Chemistry, Physical

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

Pavlo O. Dral et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Physical

Materials Design On-the-Fly

Tiago F. T. Cerqueira et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Physical

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Raghunathan Ramakrishnan et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Physical

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

Tristan Bereau et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Computer Science, Interdisciplinary Applications

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

A. P. Thompson et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2015)

Article Chemistry, Physical

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Matthias Rupp et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)

Article Physics, Multidisciplinary

Gaussian Process Model for Collision Dynamics of Complex Molecules

Jie Cui et al.

PHYSICAL REVIEW LETTERS (2015)

Article Physics, Multidisciplinary

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Zhenwei Li et al.

PHYSICAL REVIEW LETTERS (2015)

Article Physics, Multidisciplinary

Big Data of Materials Science: Critical Role of the Descriptor

Luca M. Ghiringhelli et al.

PHYSICAL REVIEW LETTERS (2015)

Article Multidisciplinary Sciences

Finding optimal surface sites on heterogeneous catalysts by counting nearest neighbors

Federico Calle-Vallejo et al.

SCIENCE (2015)

Article Chemistry, Physical

Adaptive machine learning framework to accelerate ab initio molecular dynamics

Venkatesh Botu et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Chemistry, Multidisciplinary

Fast Prediction of Adsorption Properties for Platinum Nanocatalysts with Generalized Coordination Numbers

Federico Calle-Vallejo et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2014)

Review Chemistry, Multidisciplinary

Conceptual DFT: chemistry from the linear response function

Paul Geerlings et al.

CHEMICAL SOCIETY REVIEWS (2014)

Article Materials Science, Multidisciplinary

Modeling electronic quantum transport with machine learning

Alejandro Lopez-Bezanilla et al.

PHYSICAL REVIEW B (2014)

Article Materials Science, Multidisciplinary

Machine learning for many-body physics: The case of the Anderson impurity model

Louis-Francois Arsenault et al.

PHYSICAL REVIEW B (2014)

Article Materials Science, Multidisciplinary

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

K. T. Schuett et al.

PHYSICAL REVIEW B (2014)

Article Engineering, Multidisciplinary

RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY

Loic Le Gratiet et al.

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION (2014)

Article Multidisciplinary Sciences

Quantum chemistry structures and properties of 134 kilo molecules

Raghunathan Ramakrishnan et al.

SCIENTIFIC DATA (2014)

Review Chemistry, Physical

First Principles View on Chemical Compound Space: Gaining Rigorous Atomistic Control of Molecular Properties

O. Anatole von Lilienfeld

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2013)

Article Chemistry, Physical

Efficient methods and practical guidelines for simulating isotope effects

Michele Ceriotti et al.

JOURNAL OF CHEMICAL PHYSICS (2013)

Article Chemistry, Physical

Exploring Chemical Space with the Alchemical Derivatives

Robert Balawender et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Chemistry, Physical

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Katja Hansen et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)

Article Chemistry, Multidisciplinary

Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds

Aaron M. Virshup et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Materials Science, Multidisciplinary

On representing chemical environments

Albert P. Bartok et al.

PHYSICAL REVIEW B (2013)

Article Chemistry, Multidisciplinary

Bond-energy decoupling: principle and application to heterogeneous catalysis

Bing Huang et al.

CHEMICAL SCIENCE (2013)

Article Multidisciplinary Sciences

Accelerating materials property predictions using machine learning

Ghanshyam Pilania et al.

SCIENTIFIC REPORTS (2013)

Article Materials Science, Multidisciplinary

AFLOW: An automatic framework for high-throughput materials discovery

Stefano Curtarolo et al.

COMPUTATIONAL MATERIALS SCIENCE (2012)

Article Chemistry, Medicinal

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Lars Ruddigkeit et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2012)

Article Chemistry, Physical

Higher order alchemical derivatives from coupled perturbed self-consistent field theory

Michal Lesiuk et al.

JOURNAL OF CHEMICAL PHYSICS (2012)

Article Chemistry, Physical

Optimizing transition states via kernel-based machine learning

Zachary D. Pozun et al.

JOURNAL OF CHEMICAL PHYSICS (2012)

Article Multidisciplinary Sciences

Large-scale prediction and testing of drug activity on side-effect targets

Eugen Lounkine et al.

NATURE (2012)

Article Multidisciplinary Sciences

Automated design of ligands to polypharmacological profiles

Jeremy Besnard et al.

NATURE (2012)

Editorial Material Physics, Multidisciplinary

Comment on Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Reply

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Physical and Chemical Nature of the Scaling Relations between Adsorption Energies of Atoms on Metal Surfaces

F. Calle-Vallejo et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Finding Density Functionals with Machine Learning

John C. Snyder et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Systematic Study of Au6 to Au12 Gold Clusters on MgO(100) F Centers Using Density-Functional Theory

Lasse B. Vilhelmsen et al.

PHYSICAL REVIEW LETTERS (2012)

Editorial Material Physics, Multidisciplinary

Comment on Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Jonathan E. Moussa

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Chemistry, Physical

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Article Chemistry, Physical

Toward Quantitative Structure-Property Relationships for Charge Transfer Rates of Polycyclic Aromatic Hydrocarbons

Milind Misra et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)

Article Chemistry, Physical

Path Integral Computation of Quantum Free Energy Differences Due to Alchemical Transformations Involving Mass and Potential

Alejandro Perez et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)

Article Chemistry, Physical

SHARC: ab Initio Molecular Dynamics with Surface Hopping in the Adiabatic Representation Including Arbitrary Couplings

Martin Richter et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)

Article Chemistry, Physical

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

Geoffroy Hautier et al.

CHEMISTRY OF MATERIALS (2010)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Chemistry, Medicinal

Extended-Connectivity Fingerprints

David Rogers et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2010)

Article Chemistry, Physical

Alchemical derivatives of reaction energetics

Daniel Sheppard et al.

JOURNAL OF CHEMICAL PHYSICS (2010)

Article Biotechnology & Applied Microbiology

Virtual screening: an endless staircase?

Gisbert Schneider

NATURE REVIEWS DRUG DISCOVERY (2010)

Article Physics, Multidisciplinary

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

Albert P. Bartok et al.

PHYSICAL REVIEW LETTERS (2010)

Article Chemistry, Physical

Discrete Optimization of Electronic Hyperpolarizabilities in a Chemical Subspace

B. Christopher Rinderspacher et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2009)

Review Chemistry, Physical

Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning

Chris M. Handley et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2009)

Article Chemistry, Physical

Mindless DFT Benchmarking

Martin Korth et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2009)

Article Chemistry, Multidisciplinary

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

Lorenz C. Blum et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2009)

Review Chemistry, Multidisciplinary

Towards the computational design of solid catalysts

J. K. Norskov et al.

NATURE CHEMISTRY (2009)

Article Chemistry, Multidisciplinary

Scaling relationships for adsorption energies on transition metal oxide, sulfide, and nitride surfaces

Eva M. Fernandez et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2008)

Article Physics, Multidisciplinary

Scaling properties of adsorption energies for hydrogen-containing molecules on transition-metal surfaces

F. Abild-Pedersen et al.

PHYSICAL REVIEW LETTERS (2007)

Article Physics, Multidisciplinary

Generalized neural-network representation of high-dimensional potential-energy surfaces

Joerg Behler et al.

PHYSICAL REVIEW LETTERS (2007)

Article Chemistry, Physical

Gaussian-4 theory

Larry A. Curtiss et al.

JOURNAL OF CHEMICAL PHYSICS (2007)

Article Chemistry, Physical

Molecular grand-canonical ensemble density functional theory and exploration of chemical space

O. Anatole von Lilienfeld et al.

JOURNAL OF CHEMICAL PHYSICS (2006)

Article Chemistry, Multidisciplinary

Designing molecules by optimizing potentials

ML Wang et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2006)

Article Physics, Multidisciplinary

Variational particle number approach for rational compound design

OA von Lilienfeld et al.

PHYSICAL REVIEW LETTERS (2005)

Article Multidisciplinary Sciences

Nearsightedness of electronic matter

E Prodan et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)

Article Chemistry, Medicinal

General melting point prediction based on a diverse compound data set and artificial neural networks

M Karthikeyan et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2005)

Article Biochemical Research Methods

Predicting protein-protein interactions using signature products

S Martin et al.

BIOINFORMATICS (2005)

Article Chemistry, Medicinal

ZINC - A free database of commercially available compounds for virtual screening

JJ Irwin et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2005)

Article Chemistry, Multidisciplinary

Virtual exploration of the small-molecule chemical universe below 160 daltons

T Fink et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2005)

Article Chemistry, Multidisciplinary

MOLGEN-CID -: A canonizer for molecules and graphs accessible through the Internet

J Braun et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2004)

Article Chemistry, Physical

Classical and quasiclassical spectral analysis of CH5+ using an ab initio potential energy surface

A Brown et al.

JOURNAL OF CHEMICAL PHYSICS (2003)

Review Chemistry, Multidisciplinary

Conceptual density functional theory

P Geerlings et al.

CHEMICAL REVIEWS (2003)

Article Chemistry, Multidisciplinary

The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies

JL Faulon et al.

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES (2003)

Article Physics, Multidisciplinary

Combined electronic structure and evolutionary search approach to materials design -: art. no. 255506

GH Jóhannesson et al.

PHYSICAL REVIEW LETTERS (2002)

Article Biochemical Research Methods

Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor

DP Visco et al.

JOURNAL OF MOLECULAR GRAPHICS & MODELLING (2002)

Article Chemistry, Multidisciplinary

The Cambridge Structural Database: a quarter of a million crystal structures and rising

FH Allen

ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE (2002)

Article Chemistry, Physical

ReaxFF: A reactive force field for hydrocarbons

ACT van Duin et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2001)

Article Computer Science, Theory & Methods

Data mining with sparse grids

J Garcke et al.

COMPUTING (2001)