BetaCitation Atlas

RESEARCH PAPER · The Journal of Chemical Physics · 2019

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller

Read original paper ↗

See where this research leads.

113 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic moleculesOriginalDeep learning for molecular design—a review of the state of the art677Machine-learned electron correlation model based on correlation energy density at complete basis set limit34Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation279Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning65Dataset’s chemical diversity limits the generalizability of machine learning predictions127Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions476Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications81Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules39FCHL revisited: Faster and more accurate quantum machine learning313Machine Learning for Molecular Simulation906Machine learning for the solution of the Schrödinger equation87Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78Quantum Chemistry in the Age of Machine Learning417Deep Learning for Optoelectronic Properties of Organic Semiconductors58Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables55Machine-Learning Coupled Cluster Properties through a Density Tensor Representation33Hierarchical machine learning of potential energy surfaces110Machine learning and excited-state molecular dynamics62Remarkable Accuracy of an O(N6) Perturbative Correction to Opposite-Spin CCSD: Are Triples Necessary for Chemical Accuracy in Coupled Cluster?3Machine learning accurate exchange and correlation functionals of the electronic density212A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials2Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost34A deep neural network for molecular wave functions in quasi-atomic minimal basis representation48Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy72Ab initio solution of the many-electron Schrödinger equation with deep neural networks505OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features242Quantum chemical accuracy from density functional approximations via machine learning384Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints42Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies33Machine-learned electron correlation model based on frozen core approximation16Machine Learning for Electronically Excited States of Molecules398Mean-field density matrix decompositions14DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory87Quantum Information and Algorithms for Correlated Quantum Matter125Graph neural networks and high throughput quantum chemistry workflows for detailed kinetic modeling1Physically inspired deep learning of molecular excitations and photoemission spectra65A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds20Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications79Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states51Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling14Analytical gradients for molecular-orbital-based machine learning13A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation54Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations91Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions9Perspective on integrating machine learning into computational chemistry and materials science203Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840A Multitask Approach to Learn Molecular Properties17Physics-Inspired Structural Representations for Molecules and Materials523Machine-Learned Energy Functionals for Multiconfigurational Wave Functions24Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction3Artificial Neural Networks as Propagators in Quantum Dynamics31OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy88Quantum Deep Descriptor: Physically Informed Transfer Learning from Small Molecules to Polymers25DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks10Molecular representations for machine learning applications in chemistry93Prospects of quantum computing for molecular sciences73An orbital-based representation for accurate quantum machine learning19Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning142Machine Learning in Computational Chemistry1Review on the lithium transport mechanism in solid‐state battery materials58Fast Near Ab Initio Potential Energy Surfaces Using Machine Learning25Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space18Informing geometric deep learning with electronic interactions to accelerate quantum chemistry100Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory35Pursuing the basis set limit of CCSD(T) non-covalent interaction energies for medium-sized complexes: case study on the S66 compilation28Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning7DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models22Data-driven acceleration of coupled-cluster and perturbation theory methods9Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond16Redesigning density functional theory with machine learning4Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression12Machine Learning Quantum Mechanics Computer Simulations0An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation20Accurate Prediction of Three-Body Intermolecular Interactions via Electron Deformation Density-Based Machine Learning10Machine-Learned Electronically Excited States with the MolOrbImage Generated from the Molecular Ground State13Molecular Representations for Machine Learning15Oxide– and Silicate–Water Interfaces and Their Roles in Technology and the Environment209Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data2Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory13Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations12Fitting Potential Energy Surfaces by Learning the Charge Density Matrix with Permutationally Invariant Polynomials6Construct exchange-correlation functional via machine learning20Encoding Hole–Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials5A machine learning-based high-precision density functional method for drug-like molecules7The emergence of machine learning force fields in drug design30SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations10Equivariant neural network for Green's functions of molecules and materials11Making quantum chemistry compressive and expressive: Toward practical ab‐initio simulation4Deductive Machine Learning Challenges and Opportunities in Chemical Applications5Machine learning for accuracy in density functional approximations9Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials79Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to Cn Clusters for n = 3–10, 202Constructing Accurate and Efficient General-Purpose Atomistic Machine Learning Model with Transferable Accuracy for Quantum Chemistry21Learning Intermolecular Electronic Coupling with Molecular-Orbital-Based Descriptors7Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales8Machine Learning Approaches for Developing Potential Surfaces: Applications to OH – (H 2 O) n ( n = 1–3) Complexes5Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning1Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme5Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules5A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy6Extension of Composite Method and Machine‐Learned Electron Correlation Model to Fourth‐Period Elements0Predicting the Post-Hartree-Fock Electron Correlation Energy of Complex Systems with the Information-Theoretic Approach3Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework4Diffusion Monte Carlo Study of the Structure and Spectroscopy of H 3 O –0Learning Molecular Conformational Energies Using Semilocal Density Fingerprints0Δ-Machine learning toward CCSD accuracy for homohalogenated borane–phosphine adducts: screening low-energy structures from DFT and MP2 libraries0A Rotationally Invariant Cartesian Ansatz for the Bond‐Order Charge‐Density Matrix Approach to Learning Potential Energy Surfaces0Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials0Integrating Coupled‐Cluster Theory Within AI Workflows for Accurate Molecular Geometries0Local Correlation-Based Machine Learning Enables Coupled-Cluster-Level Energetics for Thousand-Atom Molecular Systems0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

113 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Deep learning for molecular design—a review of the state of the art

    Molecular Systems Design & Engineering · 2019-01-01

    Daniel C. Elton, Zois Boukouvalas, Mark Fuge, Peter W. Chung

    Read original paper ↗ ·

  2. Machine-learned electron correlation model based on correlation energy density at complete basis set limit

    The Journal of Chemical Physics · 2019-07-09

    Takuro Nudejima, Yasuhiro Ikabata, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai

    Read original paper ↗ ·

  3. Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

    The Journal of Physical Chemistry Letters · 2019-08-09

    Yaolong Zhang, Ce Hu, Bin Jiang

    Read original paper ↗ ·

  4. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt

    Angewandte Chemie · 2019-09-25

    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

    Read original paper ↗ ·

  5. Autonomous Discovery in the Chemical Sciences Part I: Progress

    Angewandte Chemie International Edition · 2019-09-25

    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen

    Read original paper ↗ ·

  6. Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning

    Journal of Chemical Theory and Computation · 2019-10-22

    Lixue Cheng, Nikola Kovachki, Matthew Welborn, Thomas F. Miller

    Read original paper ↗ ·

  7. Dataset’s chemical diversity limits the generalizability of machine learning predictions

    Journal of Cheminformatics · 2019-11-12

    Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy, Benoit Da Mota

    Read original paper ↗ ·

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

    Nature Communications · 2019-11-15

    Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, K. Robert Müller, Reinhard J. Maurer

    Read original paper ↗ ·

  9. Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications

    Journal of Chemical Theory and Computation · 2019-11-27

    László Gyevi‐Nagy, Mihály Kállay, Péter R. Nagy

    Read original paper ↗ ·

  10. Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules

    Molecules · 2020-01-20

    Barbara Patrizi, Concetta Cozza, Adriana Pietropaolo, Paolo Foggi, Mario Siciliani de Cumis

    Read original paper ↗ ·

  11. FCHL revisited: Faster and more accurate quantum machine learning

    The Journal of Chemical Physics · 2020-01-27

    Anders Steen Christensen, Lars Andersen Bratholm, Felix A. Faber, O. Anatole von Lilienfeld

    Read original paper ↗ ·

  12. Machine Learning for Molecular Simulation

    Annual Review of Physical Chemistry · 2020-02-24

    Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

    Read original paper ↗ ·

  13. Machine learning for the solution of the Schrödinger equation

    Machine Learning Science and Technology · 2020-03-01

    Sergei Manzhos

    Read original paper ↗ ·

  14. Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

    Journal of Chemical Theory and Computation · 2020-03-02

    WooSeok Jeong, Samuel J. Stoneburner, Daniel S. King, Ruye Li, Andrew Walker, Roland H. Lindh, Laura Gagliardi

    Read original paper ↗ ·

  15. Quantum Chemistry in the Age of Machine Learning

    The Journal of Physical Chemistry Letters · 2020-03-03

    Pavlo O. Dral

    Read original paper ↗ ·

  16. Deep Learning for Optoelectronic Properties of Organic Semiconductors

    The Journal of Physical Chemistry C · 2020-03-11

    Chengqiang Lu, Qi Liu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Liang Shi, Chee‐Kong Lee

    Read original paper ↗ ·

  17. Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables

    The Journal of Physical Chemistry B · 2020-04-10

    Joseph R. Cendagorta, Jocelyn Tolpin, Elia Schneider, Robert Q. Topper, Mark E. Tuckerman

    Read original paper ↗ ·

  18. Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

    The Journal of Physical Chemistry A · 2020-05-15

    Benjamin G. Peyton, Connor G. Briggs, Ruhee D’Cunha, Johannes T. Margraf, T. Daniel Crawford

    Read original paper ↗ ·

  19. Hierarchical machine learning of potential energy surfaces

    The Journal of Chemical Physics · 2020-05-27

    Pavlo O. Dral, Alec Owens, Alexey Dral, Gábor Cśanyi

    Read original paper ↗ ·

  20. Machine learning and excited-state molecular dynamics

    Machine Learning Science and Technology · 2020-06-12

    Julia Westermayr, Philipp Marquetand

    Read original paper ↗ ·

  21. Remarkable Accuracy of an O(N6) Perturbative Correction to Opposite-Spin CCSD: Are Triples Necessary for Chemical Accuracy in Coupled Cluster?

    Journal of Chemical Theory and Computation · 2020-06-18

    David W. Small

    Read original paper ↗ ·

  22. Machine learning accurate exchange and correlation functionals of the electronic density

    Nature Communications · 2020-07-14

    Sebastian Dick, Mariví Fernández-Serra

    Read original paper ↗ ·

  23. A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials

    Royal Society of Chemistry eBooks · 2020-07-15

    Sadasivan Shankar, Richard N. Zare

    Read original paper ↗ ·

  24. Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

    The Journal of Physical Chemistry Letters · 2020-07-21

    Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik

    Read original paper ↗ ·

  25. A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

    The Journal of Chemical Physics · 2020-07-28

    M. Gastegger, A. McSloy, M. Luya, K. T. Schütt, R. J. Maurer

    Read original paper ↗ ·

  26. Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

    The Journal of Physical Chemistry A · 2020-08-03

    Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan

    Read original paper ↗ ·

  27. Ab initio solution of the many-electron Schrödinger equation with deep neural networks

    Physical Review Research · 2020-09-16

    David Pfau, James S. Spencer, Alexander G. D. G. Matthews, W. M. C. Foulkes

    Read original paper ↗ ·

  28. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

    The Journal of Chemical Physics · 2020-09-25

    Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller

    Read original paper ↗ ·

  29. Quantum chemical accuracy from density functional approximations via machine learning

    Nature Communications · 2020-10-16

    Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus‐Robert Müller, Kieron Burke

    Read original paper ↗ ·

  30. Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints

    Journal of Chemical Information and Modeling · 2020-10-29

    Obaidur Rahaman, Alessio Gagliardi

    Read original paper ↗ ·

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

    Journal of Chemical Theory and Computation · 2020-11-03

    Jacob Townsend, Konstantinos D. Vogiatzis

    Read original paper ↗ ·

  32. Machine-learned electron correlation model based on frozen core approximation

    The Journal of Chemical Physics · 2020-11-11

    Yasuhiro Ikabata, Ryo Fujisawa, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai

    Read original paper ↗ ·

  33. Machine Learning for Electronically Excited States of Molecules

    Chemical Reviews · 2020-11-19

    Julia Westermayr, Philipp Marquetand

    Read original paper ↗ ·

  34. Mean-field density matrix decompositions

    The Journal of Chemical Physics · 2020-12-03

    Janus J. Eriksen

    Read original paper ↗ ·

  35. DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

    Journal of Chemical Theory and Computation · 2020-12-09

    Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan

    Read original paper ↗ ·

  36. Quantum Information and Algorithms for Correlated Quantum Matter

    Chemical Reviews · 2020-12-16

    Kade Head-Marsden, Johannes Flick, Christopher J. Ciccarino, Prineha Narang

    Read original paper ↗ ·

  37. Graph neural networks and high throughput quantum chemistry workflows for detailed kinetic modeling

    Unknown journal · 2021-01-01

    Sai Krishna Sirumalla

    Read original paper ↗ ·

  38. Physically inspired deep learning of molecular excitations and photoemission spectra

    Chemical Science · 2021-01-01

    Julia Westermayr, Reinhard J. Maurer

    Read original paper ↗ ·

  39. A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds

    Journal of Chemical Theory and Computation · 2021-01-04

    Ruocheng Han, Mauricio Rodríguez‐Mayorga, Sandra Luber

    Read original paper ↗ ·

  40. Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications

    Journal of Chemical Theory and Computation · 2021-01-05

    László Gyevi‐Nagy, Mihály Kállay, Péter R. Nagy

    Read original paper ↗ ·

  41. Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

    The Journal of Chemical Physics · 2021-02-12

    Tamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller

    Read original paper ↗ ·

  42. Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

    Macromolecules · 2021-03-18

    Ioan-Bogdan Magdău, Thomas F. Miller

    Read original paper ↗ ·

  43. Analytical gradients for molecular-orbital-based machine learning

    The Journal of Chemical Physics · 2021-03-25

    Sebastian J. R. Lee, Tamara Husch, Feizhi Ding, Thomas F. Miller

    Read original paper ↗ ·

  44. A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation

    Journal of Chemical Theory and Computation · 2021-04-16

    Daniel S. King, Laura Gagliardi

    Read original paper ↗ ·

  45. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

    Machine Learning Science and Technology · 2021-04-30

    April M Miksch, Tobias Morawietz, Johannes Kästner, Alexander Urban, Nongnuch Artrith

    Read original paper ↗ ·

  46. Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions

    The Journal of Physical Chemistry Letters · 2021-06-01

    Ruocheng Han, Sandra Luber

    Read original paper ↗ ·

  47. Perspective on integrating machine learning into computational chemistry and materials science

    The Journal of Chemical Physics · 2021-06-21

    Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer

    Read original paper ↗ ·

  48. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

    Chemical Reviews · 2021-07-07

    John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus‐Robert Müller, Alexandre Tkatchenko

    Read original paper ↗ ·

  49. A Multitask Approach to Learn Molecular Properties

    Journal of Chemical Information and Modeling · 2021-07-21

    Zheng Tan, Yan Li, Weimei Shi, Shiqing Yang

    Read original paper ↗ ·

  50. Physics-Inspired Structural Representations for Molecules and Materials

    Chemical Reviews · 2021-07-26

    Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Cśanyi, Michele Ceriotti

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.