Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning

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

(2021) Apurba Nandi et al.   JOURNAL OF CHEMICAL PHYSICS

Accelerating coupled cluster calculations with nonlinear dynamics and supervised machine learning

(2021) Valay Agarawal et al.   JOURNAL OF CHEMICAL PHYSICS

Accuracy of DLPNO-CCSD(T): Effect of Basis Set and System Size

(2021) Isolde Sandler et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models

(2021) Silvan Käser et al.   Journal of Chemical Theory and Computation

A Fragmentation-Based Graph Embedding Framework for QM/ML

(2021) Eric M. Collins et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Best practices in machine learning for chemistry

(2021) Nongnuch Artrith et al.   Nature Chemistry

Capture and Reactivity of an Elusive Carbon–Sulfur Centered Biradical

(2020) Dennis Gerbig et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

(2020) Benjamin G. Peyton et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules

(2020) Peter C. St. John et al.   Scientific Data

Quantum chemical accuracy from density functional approximations via machine learning

(2020) Mihail Bogojeski et al.   Nature Communications

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Charting a course for chemistry

(2019) Alán Aspuru-Guzik et al.   Nature Chemistry

Advances of machine learning in molecular modeling and simulation

(2019) Mojtaba Haghighatlari et al.   Current Opinion in Chemical Engineering

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

(2019) Frederic E. Bock et al.   Frontiers in Materials

Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver

(2019) Jacob Townsend et al.   Journal of Physical Chemistry Letters

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

(2019) Justin S. Smith et al.   Nature Communications

Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism

(2019) Zhaoping Xiong et al.   JOURNAL OF MEDICINAL CHEMISTRY

Single‐reference coupled cluster methods for computing excitation energies in large molecules: The efficiency and accuracy of approximations

(2019) Róbert Izsák   Wiley Interdisciplinary Reviews-Computational Molecular Science

Comprehensive Benchmark Results for the Domain Based Local Pair Natural Orbital Coupled Cluster Method (DLPNO-CCSD(T)) for Closed- and Open-Shell Systems

(2019) Dimitrios G. Liakos et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Atmospherically Relevant Radicals Derived from the Oxidation of Dimethyl Sulfide

(2018) Artur Mardyukov et al.   ACCOUNTS OF CHEMICAL RESEARCH

Communication: An improved linear scaling perturbative triples correction for the domain based local pair-natural orbital based singles and doubles coupled cluster method [DLPNO-CCSD(T)]

(2018) Yang Guo et al.   JOURNAL OF CHEMICAL PHYSICS

Self-Interaction Error in Density Functional Theory: An Appraisal

(2018) Junwei Lucas Bao et al.   Journal of Physical Chemistry Letters

Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

(2018) Johannes Hachmann et al.   MOLECULAR SIMULATION

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory

(2016) Christoph Riplinger et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Exploring the Accuracy Limits of Local Pair Natural Orbital Coupled-Cluster Theory

(2015) Dimitrios G. Liakos et al.   Journal of Chemical Theory and Computation

Bringing the MMFF force field to the RDKit: implementation and validation

(2014) Paolo Tosco et al.   Journal of Cheminformatics

An efficient and near linear scaling pair natural orbital based local coupled cluster method

(2013) Christoph Riplinger et al.   JOURNAL OF CHEMICAL PHYSICS

Challenges for Density Functional Theory

(2011) Aron J. Cohen et al.   CHEMICAL REVIEWS

The ORCA program system

(2011) Frank Neese   Wiley Interdisciplinary Reviews-Computational Molecular Science

Ionization energies of water from PNO-CI calculations

(2010) Wilfried Meyer   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Frontiers in electronic structure theory

(2010) C. David Sherrill   JOURNAL OF CHEMICAL PHYSICS

High-Accuracy Thermochemistry of Atmospherically Important Fluorinated and Chlorinated Methane Derivatives

(2010) József Csontos et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Calculations for millions of atoms with density functional theory: linear scaling shows its potential

(2010) D R Bowler et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Efficient and accurate local approximations to coupled-electron pair approaches: An attempt to revive the pair natural orbital method

(2009) Frank Neese et al.   JOURNAL OF CHEMICAL PHYSICS

Parallel Calculation of CCSD and CCSD(T) Analytic First and Second Derivatives

(2007) Michael E. Harding et al.   Journal of Chemical Theory and Computation