Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Abstract
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.
- Authors:
-
[1];
[2];
[2];
[2];
[2];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Florida, Gainesville, FL (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Jackson State Univ., MS (United States)
- Univ. of Florida, Gainesville, FL (United States)
- Univ. of North Carolina, Chapel Hill, NC (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1544717
- Report Number(s):
- LA-UR-18-25687
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, and Roitberg, Adrian E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. United States: N. p., 2019.
Web. doi:10.1038/s41467-019-10827-4.
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, & Roitberg, Adrian E. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. United States. https://doi.org/10.1038/s41467-019-10827-4
Smith, Justin Steven, Nebgen, Benjamin Tyler, Zubatiuk, Roman, Lubbers, Nicholas Edward, Devereux, Christian, Barros, Kipton Marcos, Isayev, Olexandr, Tretiak, Sergei, and Roitberg, Adrian E. Mon .
"Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning". United States. https://doi.org/10.1038/s41467-019-10827-4. https://www.osti.gov/servlets/purl/1544717.
@article{osti_1544717,
title = {Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning},
author = {Smith, Justin Steven and Nebgen, Benjamin Tyler and Zubatiuk, Roman and Lubbers, Nicholas Edward and Devereux, Christian and Barros, Kipton Marcos and Isayev, Olexandr and Tretiak, Sergei and Roitberg, Adrian E},
abstractNote = {Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.},
doi = {10.1038/s41467-019-10827-4},
journal = {Nature Communications},
number = 1,
volume = 10,
place = {United States},
year = {2019},
month = {7}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 310 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Protein–Ligand Scoring with Convolutional Neural Networks
journal, April 2017
- Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa
- Journal of Chemical Information and Modeling, Vol. 57, Issue 4
Systematic optimization of long-range corrected hybrid density functionals
journal, February 2008
- Chai, Jeng-Da; Head-Gordon, Martin
- The Journal of Chemical Physics, Vol. 128, Issue 8
Deep learning
journal, May 2015
- LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
- Nature, Vol. 521, Issue 7553
Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations
text, January 2018
- Patrick, Bleiziffer,; Kay, Schaller,; Sereina, Riniker,
- ETH Zurich
Mechanical properties of nanocrystalline materials
journal, May 2006
- Meyers, M. A.; Mishra, A.; Benson, D. J.
- Progress in Materials Science, Vol. 51, Issue 4
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
journal, March 2018
- Kranz, Julian J.; Kubillus, Maximilian; Ramakrishnan, Raghunathan
- Journal of Chemical Theory and Computation, Vol. 14, Issue 5
A simple and efficient CCSD(T)-F12 approximation
journal, December 2007
- Adler, Thomas B.; Knizia, Gerald; Werner, Hans-Joachim
- The Journal of Chemical Physics, Vol. 127, Issue 22
A full coupled‐cluster singles and doubles model: The inclusion of disconnected triples
journal, February 1982
- Purvis, George D.; Bartlett, Rodney J.
- The Journal of Chemical Physics, Vol. 76, Issue 4
W4-11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data
journal, July 2011
- Karton, Amir; Daon, Shauli; Martin, Jan M. L.
- Chemical Physics Letters, Vol. 510, Issue 4-6
The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics
preprint, January 2017
- Yao, Kun; Herr, John E.; Toth, David W.
- arXiv
A method to apply Piola-Kirchhoff stress in molecular statics simulation
journal, July 2021
- Ghasemi, Arman; Gao, Wei
- Computational Materials Science, Vol. 195
Toward True DNA Base-Stacking Energies: MP2, CCSD(T), and Complete Basis Set Calculations
journal, October 2002
- Hobza, Pavel; Šponer, Jiří
- Journal of the American Chemical Society, Vol. 124, Issue 39
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
journal, December 2017
- Smith, Justin S.; Isayev, Olexandr; Roitberg, Adrian E.
- Scientific Data, Vol. 4, Issue 1
Boosting for transfer learning
conference, January 2007
- Dai, Wenyuan; Yang, Qiang; Xue, Gui-Rong
- Proceedings of the 24th international conference on Machine learning - ICML '07
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
journal, October 2017
- Faber, Felix A.; Hutchison, Luke; Huang, Bing
- Journal of Chemical Theory and Computation, Vol. 13, Issue 11
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
text, January 2015
- Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
- American Chemical Society
First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications
journal, March 2017
- Hermann, Jan; DiStasio, Robert A.; Tkatchenko, Alexandre
- Chemical Reviews, Vol. 117, Issue 6
Extensions of the S66 Data Set: More Accurate Interaction Energies and Angular-Displaced Nonequilibrium Geometries
journal, October 2011
- Řezáč, Jan; Riley, Kevin E.; Hobza, Pavel
- Journal of Chemical Theory and Computation, Vol. 7, Issue 11
Validation of electronic structure methods for isomerization reactions of large organic molecules
journal, January 2011
- Luo, Sijie; Zhao, Yan; Truhlar, Donald G.
- Physical Chemistry Chemical Physics, Vol. 13, Issue 30
Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory
journal, March 2018
- Klucznik, Tomasz; Mikulak-Klucznik, Barbara; McCormack, Michael P.
- Chem, Vol. 4, Issue 3
The open science grid
journal, July 2007
- Pordes, Ruth; Petravick, Don; Kramer, Bill
- Journal of Physics: Conference Series, Vol. 78
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
text, January 2017
- S., Smith, Justin; Olexandr, Isayev,; E., Roitberg, Adrian
- The University of North Carolina at Chapel Hill University Libraries
Query by committee
conference, January 1992
- Seung, H. S.; Opper, M.; Sompolinsky, H.
- Proceedings of the fifth annual workshop on Computational learning theory - COLT '92
Nobel Lecture: Electronic structure of matter—wave functions and density functionals
journal, October 1999
- Kohn, W.
- Reviews of Modern Physics, Vol. 71, Issue 5
Protein-Ligand Scoring with Convolutional Neural Networks
text, January 2016
- Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa
- arXiv
Predicting reaction performance in C–N cross-coupling using machine learning
journal, February 2018
- Ahneman, Derek T.; Estrada, Jesús G.; Lin, Shishi
- Science, Vol. 360, Issue 6385
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
journal, August 2017
- Behler, Jörg
- Angewandte Chemie International Edition, Vol. 56, Issue 42
Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations
journal, February 2018
- Bleiziffer, Patrick; Schaller, Kay; Riniker, Sereina
- Journal of Chemical Information and Modeling, Vol. 58, Issue 3
Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment
journal, October 2015
- Rauscher, Sarah; Gapsys, Vytautas; Gajda, Michal J.
- Journal of Chemical Theory and Computation, Vol. 11, Issue 11
The Roles of Plastic Surgeons in Advancing Artificial Intelligence in Plastic Surgery
journal, April 2021
- Luan, Fang; Gao, Xuejun; Zhao, Shanbaga
- Aesthetic Plastic Surgery
Machine learning unifies the modeling of materials and molecules
journal, December 2017
- Bartók, Albert P.; De, Sandip; Poelking, Carl
- Science Advances, Vol. 3, Issue 12
Discovering a Transferable Charge Assignment Model Using Machine Learning
journal, July 2018
- Sifain, Andrew E.; Lubbers, Nicholas; Nebgen, Benjamin T.
- The Journal of Physical Chemistry Letters, Vol. 9, Issue 16
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
journal, April 2017
- Mardirossian, Narbe; Head-Gordon, Martin
- Molecular Physics, Vol. 115, Issue 19
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- Physical Review Letters, Vol. 108, Issue 5
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
journal, April 2017
- Mardirossian, Narbe; Head-Gordon, Martin
- Molecular Physics, Vol. 115, Issue 19
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
journal, January 2018
- Yao, Kun; Herr, John E.; Toth, David W.
- Chemical Science, Vol. 9, Issue 8
The Protein-Folding Problem, 50 Years On
journal, November 2012
- Dill, K. A.; MacCallum, J. L.
- Science, Vol. 338, Issue 6110
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015
- Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
- Journal of Chemical Theory and Computation, Vol. 11, Issue 5
Predicting reaction performance in C–N cross-coupling using machine learning
journal, February 2018
- Ahneman, Derek T.; Estrada, Jesús G.; Lin, Shishi
- Science, Vol. 360, Issue 6385
Amplify scientific discovery with artificial intelligence
journal, October 2014
- Gil, Y.; Greaves, M.; Hendler, J.
- Science, Vol. 346, Issue 6206
Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory
journal, March 2018
- Klucznik, Tomasz; Mikulak-Klucznik, Barbara; McCormack, Michael P.
- Chem, Vol. 4, Issue 3
Quantum Chemical Models (Nobel Lecture)
journal, July 1999
- Pople, John A.
- Angewandte Chemie International Edition, Vol. 38, Issue 13-14
Protein-Ligand Scoring with Convolutional Neural Networks
text, January 2016
- Ragoza, Matthew; Hochuli, Joshua; Idrobo, Elisa
- arXiv
Machine-learning-assisted materials discovery using failed experiments
journal, May 2016
- Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
- Nature, Vol. 533, Issue 7601
Extensions of the S66 Data Set: More Accurate Interaction Energies and Angular-Displaced Nonequilibrium Geometries
journal, October 2011
- Řezáč, Jan; Riley, Kevin E.; Hobza, Pavel
- Journal of Chemical Theory and Computation, Vol. 7, Issue 11
A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments
journal, May 2017
- Sellers, Benjamin D.; James, Natalie C.; Gobbi, Alberto
- Journal of Chemical Information and Modeling, Vol. 57, Issue 6
Universal fragment descriptors for predicting properties of inorganic crystals
text, January 2017
- Olexandr, Isayev,; Corey, Oses,; Eric, Gossett,
- The University of North Carolina at Chapel Hill University Libraries
Density functional theory with London dispersion corrections: Density functional theory with London dispersion corrections
journal, March 2011
- Grimme, Stefan
- Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 1, Issue 2
A Survey on Transfer Learning
journal, October 2010
- Pan, Sinno Jialin; Yang, Qiang
- IEEE Transactions on Knowledge and Data Engineering, Vol. 22, Issue 10
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
journal, August 2017
- Behler, Jörg
- Angewandte Chemie International Edition, Vol. 56, Issue 42
Quantum-chemical insights from deep tensor neural networks
journal, January 2017
- Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan
- Nature Communications, Vol. 8, Issue 1
Machine learning molecular dynamics for the simulation of infrared spectra
journal, January 2017
- Gastegger, Michael; Behler, Jörg; Marquetand, Philipp
- Chemical Science, Vol. 8, Issue 10
Quantum-Chemical Insights from Deep Tensor Neural Networks
text, January 2016
- Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan
- arXiv
Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
text, January 2016
- Isayev, Olexandr; Oses, Corey; Toher, Cormac
- arXiv
Machine learning of accurate energy-conserving molecular force fields
text, January 2017
- Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
- Technische Universität Berlin
Machine learning of accurate energy-conserving molecular force fields
journal, May 2017
- Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
- Science Advances, Vol. 3, Issue 5
Universal fragment descriptors for predicting properties of inorganic crystals
journal, June 2017
- Isayev, Olexandr; Oses, Corey; Toher, Cormac
- Nature Communications, Vol. 8, Issue 1
Mechanical properties of nanocrystalline materials
journal, May 2006
- Meyers, M. A.; Mishra, A.; Benson, D. J.
- Progress in Materials Science, Vol. 51, Issue 4
Assessment of the Performance of DFT and DFT-D Methods for Describing Distance Dependence of Hydrogen-Bonded Interactions
journal, December 2010
- Thanthiriwatte, Kanchana S.; Hohenstein, Edward G.; Burns, Lori A.
- Journal of Chemical Theory and Computation, Vol. 7, Issue 1
Systematic optimization of long-range corrected hybrid density functionals
journal, February 2008
- Chai, Jeng-Da; Head-Gordon, Martin
- The Journal of Chemical Physics, Vol. 128, Issue 8
Machine learning in materials informatics: recent applications and prospects
journal, December 2017
- Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
- npj Computational Materials, Vol. 3, Issue 1
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
text, January 2017
- Faber, Felix A.; Hutchison, Luke; Huang, Bing
- American Chemical Society
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
journal, January 2017
- Smith, J. S.; Isayev, O.; Roitberg, A. E.
- Chemical Science, Vol. 8, Issue 4
Towards exact molecular dynamics simulations with machine-learned force fields
journal, September 2018
- Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert
- Nature Communications, Vol. 9, Issue 1
Inferring multi-target QSAR models with taxonomy-based multi-task learning
journal, July 2013
- Rosenbaum, Lars; Dörr, Alexander; Bauer, Matthias R.
- Journal of Cheminformatics, Vol. 5, Issue 1
The ORCA program system: The ORCA program system
journal, June 2011
- Neese, Frank
- Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 2, Issue 1
Serendipity: Accidental Discoveries in Science (Roberts, Royston M.)
journal, December 1990
- Ramsay, O. Bertrand
- Journal of Chemical Education, Vol. 67, Issue 12
Tuning the activities of cuprous oxide nanostructures via the oxide-metal interaction
journal, May 2020
- Huang, Wugen; Liu, Qingfei; Zhou, Zhiwen
- Nature Communications, Vol. 11, Issue 1
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals [Supplemental Data]
fileset, June 2017
- Head-Gordon, Martin; Mardirossian, Narbe
- figshare-Supplementary information for journal article at DOI: 10.1080/00268976.2017.1333644, 10 files
The atomic simulation environment—a Python library for working with atoms
journal, June 2017
- Hjorth Larsen, Ask; Jørgen Mortensen, Jens; Blomqvist, Jakob
- Journal of Physics: Condensed Matter, Vol. 29, Issue 27
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
journal, March 2018
- Kranz, Julian J.; Kubillus, Maximilian; Ramakrishnan, Raghunathan
- Journal of Chemical Theory and Computation, Vol. 14, Issue 5
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
text, January 2011
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- arXiv
Density functional theory with London dispersion corrections: Density functional theory with London dispersion corrections
journal, March 2011
- Grimme, Stefan
- Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 1, Issue 2
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
text, January 2018
- Kranz, Julian J.; Kubillus, Maximilian; Ramakrishnan, Raghunathan
- American Chemical Society
A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments
journal, May 2017
- Sellers, Benjamin D.; James, Natalie C.; Gobbi, Alberto
- Journal of Chemical Information and Modeling, Vol. 57, Issue 6
An Introduction to Coupled Cluster Theory for Computational Chemists
book, January 2000
- Crawford, T. Daniel; Schaefer, Henry F.; Lipkowitz, Kenny B.
- Reviews in Computational Chemistry: Lipkowitz/Reviews
First-Principles Models for van der Waals Interactions in Molecules and Materials: Concepts, Theory, and Applications
journal, March 2017
- Hermann, Jan; DiStasio, Robert A.; Tkatchenko, Alexandre
- Chemical Reviews, Vol. 117, Issue 6
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
journal, March 2015
- Li, Zhenwei; Kermode, James R.; De Vita, Alessandro
- Physical Review Letters, Vol. 114, Issue 9
Machine Learning of Accurate Energy-Conserving Molecular Force Fields
text, January 2016
- Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.
- arXiv
Machine learning in materials informatics: recent applications and prospects
journal, December 2017
- Ramprasad, Rampi; Batra, Rohit; Pilania, Ghanshyam
- npj Computational Materials, Vol. 3, Issue 1
Assessment of the Performance of DFT and DFT-D Methods for Describing Distance Dependence of Hydrogen-Bonded Interactions
journal, December 2010
- Thanthiriwatte, Kanchana S.; Hohenstein, Edward G.; Burns, Lori A.
- Journal of Chemical Theory and Computation, Vol. 7, Issue 1
Machine learning unifies the modeling of materials and molecules.
text, January 2017
- Bartók, Albert P.; De, Sandip; Poelking, Carl
- Apollo - University of Cambridge Repository
W4-11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data
journal, July 2011
- Karton, Amir; Daon, Shauli; Martin, Jan M. L.
- Chemical Physics Letters, Vol. 510, Issue 4-6
A simple and efficient CCSD(T)-F12 approximation
journal, December 2007
- Adler, Thomas B.; Knizia, Gerald; Werner, Hans-Joachim
- The Journal of Chemical Physics, Vol. 127, Issue 22
Coupled-cluster theory in quantum chemistry
journal, February 2007
- Bartlett, Rodney J.; Musiał, Monika
- Reviews of Modern Physics, Vol. 79, Issue 1
Biomolecular Simulation: A Computational Microscope for Molecular Biology
journal, June 2012
- Dror, Ron O.; Dirks, Robert M.; Grossman, J. P.
- Annual Review of Biophysics, Vol. 41, Issue 1
Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients
journal, January 2017
- Turki, Turki; Wei, Zhi; Wang, Jason T. L.
- IEEE Access, Vol. 5
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
journal, April 2015
- Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias
- Journal of Chemical Theory and Computation, Vol. 11, Issue 5
Toward True DNA Base-Stacking Energies: MP2, CCSD(T), and Complete Basis Set Calculations
journal, October 2002
- Hobza, Pavel; Šponer, Jiří
- Journal of the American Chemical Society, Vol. 124, Issue 39
Generalized Gradient Approximation That Recovers the Second-Order Density-Gradient Expansion with Optimized Across-the-Board Performance
journal, July 2011
- Peverati, Roberto; Zhao, Yan; Truhlar, Donald G.
- The Journal of Physical Chemistry Letters, Vol. 2, Issue 16
Sources of error in electronic structure calculations on small chemical systems
journal, February 2006
- Feller, David; Peterson, Kirk A.; Crawford, T. Daniel
- The Journal of Chemical Physics, Vol. 124, Issue 5
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
journal, July 2018
- Nebgen, Benjamin; Lubbers, Nicholas; Smith, Justin S.
- Journal of Chemical Theory and Computation, Vol. 14, Issue 9
Discoveries missed, discoveries made: creativity, influence, and fame in chemistry
journal, January 1992
- Berson, Jerome A.
- Tetrahedron, Vol. 48, Issue 1
The Protein-Folding Problem, 50 Years On
journal, November 2012
- Dill, K. A.; MacCallum, J. L.
- Science, Vol. 338, Issue 6110
Generalized Gradient Approximation That Recovers the Second-Order Density-Gradient Expansion with Optimized Across-the-Board Performance
journal, July 2011
- Peverati, Roberto; Zhao, Yan; Truhlar, Donald G.
- The Journal of Physical Chemistry Letters, Vol. 2, Issue 16
Biomolecular Simulation: A Computational Microscope for Molecular Biology
journal, June 2012
- Dror, Ron O.; Dirks, Robert M.; Grossman, J. P.
- Annual Review of Biophysics, Vol. 41, Issue 1
Fast and accurate modeling of molecular atomization energies with machine learning
text, January 2012
- Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert
- American Physical Society
Machine learning prediction errors better than DFT accuracy
text, January 2017
- Faber, Felix A.; Hutchison, Luke; Huang, Bing
- arXiv
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
text, January 2017
- ,
- The University of North Carolina at Chapel Hill University Libraries
Machine learning unifies the modeling of materials and molecules
journal, December 2017
- Bartók, Albert P.; De, Sandip; Poelking, Carl
- Science Advances, Vol. 3, Issue 12
Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment
journal, October 2015
- Rauscher, Sarah; Gapsys, Vytautas; Gajda, Michal J.
- Journal of Chemical Theory and Computation, Vol. 11, Issue 11
An Introduction to Coupled Cluster Theory for Computational Chemists
book, January 2000
- Crawford, T. Daniel; Schaefer, Henry F.; Lipkowitz, Kenny B.
- Reviews in Computational Chemistry: Lipkowitz/Reviews
Quantum-chemical insights from deep tensor neural networks
journal, January 2017
- Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan
- Nature Communications, Vol. 8, Issue 1
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
text, January 2017
- S., Smith, Justin; Olexandr, Isayev,; E., Roitberg, Adrian
- The University of North Carolina at Chapel Hill University Libraries
Discovering a Transferable Charge Assignment Model Using Machine Learning
journal, July 2018
- Sifain, Andrew E.; Lubbers, Nicholas; Nebgen, Benjamin T.
- The Journal of Physical Chemistry Letters, Vol. 9, Issue 16
Amplify scientific discovery with artificial intelligence
journal, October 2014
- Gil, Y.; Greaves, M.; Hendler, J.
- Science, Vol. 346, Issue 6206
Machine-learning-assisted materials discovery using failed experiments
journal, May 2016
- Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
- Nature, Vol. 533, Issue 7601
Machine learning: Trends, perspectives, and prospects
journal, July 2015
- Jordan, M. I.; Mitchell, T. M.
- Science, Vol. 349, Issue 6245
Validation of electronic structure methods for isomerization reactions of large organic molecules
journal, January 2011
- Luo, Sijie; Zhao, Yan; Truhlar, Donald G.
- Physical Chemistry Chemical Physics, Vol. 13, Issue 30
Less is more: Sampling chemical space with active learning
journal, June 2018
- Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas
- The Journal of Chemical Physics, Vol. 148, Issue 24
Energy-free machine learning force field for aluminum
journal, August 2017
- Kruglov, Ivan; Sergeev, Oleg; Yanilkin, Alexey
- Scientific Reports, Vol. 7, Issue 1
Hierarchical modeling of molecular energies using a deep neural network
journal, June 2018
- Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton
- The Journal of Chemical Physics, Vol. 148, Issue 24
Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory
journal, January 2016
- Riplinger, Christoph; Pinski, Peter; Becker, Ute
- The Journal of Chemical Physics, Vol. 144, Issue 2
Works referencing / citing this record:
Opportunities for artificial intelligence in advancing precision medicine
preprint, January 2019
- Filipp, Fabian V.
- arXiv
Machine learning for electronically excited states of molecules
text, January 2020
- Westermayr, Julia; Marquetand, Philipp
- arXiv
Simulating protein–ligand binding with neural network potentials
journal, January 2020
- Lahey, Shae-Lynn J.; Rowley, Christopher N.
- Chemical Science, Vol. 11, Issue 9
Deep Learning for Optoelectronic Properties of Organic Semiconductors
text, January 2019
- Lu, Chengqiang; Liu, Qi; Sun, Qiming
- arXiv
Machine learning for interatomic potential models
journal, February 2020
- Mueller, Tim; Hernandez, Alberto; Wang, Chuhong
- The Journal of Chemical Physics, Vol. 152, Issue 5
Opportunities for Artificial Intelligence in Advancing Precision Medicine
journal, December 2019
- Filipp, Fabian V.
- Current Genetic Medicine Reports, Vol. 7, Issue 4
Machine learning and artificial neural network accelerated computational discoveries in materials science
journal, November 2019
- Hong, Yang; Hou, Bo; Jiang, Hengle
- WIREs Computational Molecular Science, Vol. 10, Issue 3
Dataset’s chemical diversity limits the generalizability of machine learning predictions
journal, November 2019
- Glavatskikh, Marta; Leguy, Jules; Hunault, Gilles
- Journal of Cheminformatics, Vol. 11, Issue 1
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
journal, September 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Advanced Materials, Vol. 31, Issue 46
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.
journalarticle, January 2019
- Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor
- Wiley
Efficient Cysteine Conformer Search with Bayesian Optimization
preprint, January 2020
- Fang, Lincan; Makkonen, Esko; Todorovic, Milica
- arXiv
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
journal, August 2019
- Zubatyuk, Roman; Smith, Justin S.; Leszczynski, Jerzy
- Science Advances, Vol. 5, Issue 8
Transforming solid-state precipitates via excess vacancies
journal, March 2020
- Bourgeois, Laure; Zhang, Yong; Zhang, Zezhong
- Nature Communications, Vol. 11, Issue 1