Research graph
References from A Physics-Constrained Synergetic Active- and Transfer-Learning Framework for Constructing a Spectroscopically Accurate Full-Dimensional Potential Energy Surface of H2O–N2 Complex. Local targets link to admitted publications; unresolved targets remain external evidence.
From intermolecular potentials to the spectra of van der Waals molecules, and vice versa
10.1021/cr00031a009 · 1994 · External reference
Intelligent Understanding of Spectra: From Structural Elucidation to Property Design
10.1039/d4cs01293c · 2025 · External reference
The Vibrational Spectroscopy and Dynamics of Weakly Bound Neutral Complexes
10.1126/science.240.4851.447 · 1988 · External reference
Perturbation theory approach to intermolecular potential energy surfaces of van der Waals complexes
10.1021/cr00031a008 · 1994 · External reference
Perspective: Accurate Ro-Vibrational Calculations on Small Molecules
10.1063/1.4962907 · 2016 · External reference
Perspective: Computing (Ro-)Vibrational Spectra of Molecules with More than Four Atoms
10.1063/1.4979117 · 2017 · External reference
Theoretical Methods for Small-Molecule Ro-Vibrational Spectroscopy
10.1088/0953-4075/43/13/133001 · 2010 · External reference
Theory Untangles the High-Resolution Infrared Spectrum of the ortho-H2–CO van der Waals Complex
10.1126/science.1221000 · 2012 · External reference
Calculation of Rotation-Vibration Energy Levels of the Water Molecule with Near-Experimental Accuracy Based on an ab Initio Potential Energy Surface
10.1021/jp312343z · 2013 · External reference
High-Accuracy Calculations of the Rotation-Vibration Spectrum of H3+
10.1088/1361-6455/aa8ca6 · 2017 · External reference
Basis Set Convergence of the Post-CCSD(T) Contribution to Noncovalent Interaction Energies
10.1021/ct500347q · 2014 · External reference
Platinum, Gold, and Silver Standards of Intermolecular Interaction Energy Calculations
10.1063/1.5116151 · 2019 · External reference
When Gold Is Not Enough: Platinum Standard of Quantum Chemistry with N7 Cost
10.1021/acs.jctc.2c00460 · 2022 · External reference
Constructing Multidimensional Molecular Potential Energy Surfaces from ab Initio Data
10.1146/annurev.physchem.50.1.537 · 1999 · External reference
A Hierarchical Construction Scheme for Accurate Potential Energy Surface Generation: An Application to the F+H2 Reaction
10.1063/1.2955729 · 2008 · External reference
Neural Network Potential Energy Surfaces for Small Molecules and Reactions
10.1021/acs.chemrev.0c00665 · 2021 · External reference
Machine Learning for Molecular and Materials Science
10.1038/s41586-018-0337-2 · 2018 · External reference
Machine Learning for Molecular Simulation
10.1146/annurev-physchem-042018-052331 · 2020 · External reference
Machine Learning Force Fields
10.1021/acs.chemrev.0c01111 · 2021 · External reference
Data Generation for Machine Learning Interatomic Potentials and Beyond
10.1021/acs.chemrev.4c00572 · 2024 · External reference
Permutationally Invariant Potential Energy Surfaces
10.1146/annurev-physchem-050317-021139 · 2018 · External reference
Permutationally Invariant Potential Energy Surfaces in High Dimensionality
10.1080/01442350903234923 · 2009 · External reference
Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization
10.1021/ct9004917 · 2010 · External reference
Permutation Invariant Polynomial Neural Network Approach to Fitting Potential Energy Surfaces
10.1063/1.4817187 · 2013 · External reference
Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
10.1063/1.4832697 · 2013 · External reference
Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial-neural network approach
10.1080/0144235x.2016.1200347 · 2016 · External reference
Communication: Fitting Potential Energy Surfaces with Fundamental Invariant Neural Network
10.1063/1.4961454 · 2016 · External reference
Ab initio potential energy surfaces and quantum dynamics for polyatomic bimolecular reactions
10.1021/acs.jctc.8b00006 · 2018 · External reference
MLRNet: Combining the Physics-Motivated Potential Models with Neural Networks for Intermolecular Potential Energy Surface Construction
10.1021/acs.jctc.2c01049 · 2023 · External reference
The TensorMol-0.1 Model Chemistry: A Neural Network Augmented with Long-Range Physics
10.1039/c7sc04934j · 2018 · External reference
Non-Covalent Interactions across Organic and Biological Subsets of Chemical Space: Physics-Based Potentials Parametrized from Machine Learning
10.1063/1.5009502 · 2018 · External reference
CONI-Net: Machine Learning of Separable Intermolecular Force Fields
10.1021/acs.jctc.1c00328 · 2021 · External reference
A New Potential Function Form Incorporating Extended Long-Range Behaviour: Application to Ground-State Ca2
10.1080/00268970701241656 · 2007 · External reference
Analytic Morse/Long-Range Potential Energy Surfaces and Predicted Infrared Spectra for CO–H2 Dimer and Frequency Shifts of CO in (para-H2)NN = 1–20 Clusters
10.1063/1.4826595 · 2013 · External reference
Long-Range Damping Functions Improve the Short-Range Behaviour of “MLR” Potential Energy Functions
10.1080/00268976.2010.527304 · 2011 · External reference
An Improved Simple Model for the van der Waals Potential Based on Universal Damping Functions for the Dispersion Coefficients
10.1063/1.447150 · 1984 · External reference
Less Is More: Sampling Chemical Space with Active Learning
10.1063/1.5023802 · 2018 · External reference
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events
10.1038/s41524-020-0283-z · 2020 · External reference
Automatically Growing Global Reactive Neural Network Potential Energy Surfaces: A Trajectory-Free Active Learning Strategy
10.1063/5.0004944 · 2020 · External reference
Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials
10.1021/acs.jctc.1c00166 · 2021 · External reference
Uncertainty-Driven Dynamics for Active Learning of Interatomic Potentials
10.1038/s43588-023-00406-5 · 2023 · External reference
Applying the Active Learning Strategy to the Construction of Full-Dimensional Neural Network Potential Energy Surfaces: Critical Tests in H2O–He Spectroscopic Calculation
10.1063/5.0263653 · 2025 · External reference
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
10.1021/acs.jctc.5b00099 · 2015 · External reference
Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited
10.1021/acs.jctc.8b00832 · 2019 · External reference
Quantum Chemical Accuracy from Density Functional Approximations via Machine Learning
10.1038/s41467-020-19093-1 · 2020 · External reference
Hierarchical Machine Learning of Potential Energy Surfaces
10.1063/5.0006498 · 2020 · External reference
Δ-Machine Learning for Potential Energy Surfaces: A PIP Approach to Bring a DFT-Based PES to the CCSD(T) Level of Theory
10.1063/5.0038301 · 2021 · External reference
Δ-Machine Learned Potential Energy Surfaces and Force Fields
10.1021/acs.jctc.2c01034 · 2023 · External reference
Approaching Coupled Cluster Accuracy with a General-Purpose Neural Network Potential through Transfer Learning
10.1038/s41467-019-10827-4 · 2019 · External reference
Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models
10.1021/acs.jctc.1c00249 · 2021 · External reference
Transfer-Learned Potential Energy Surfaces: Toward Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality
10.1063/5.0151266 · 2023 · External reference
Transfer Learning for Predictive Molecular Simulations: Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy
10.1021/acs.jctc.5c00523 · 2025 · External reference
Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy
10.1021/acs.jctc.2c01203 · 2023 · External reference
Water Vapor Feedback in Climate Models
10.1126/science.1119258 · 2005 · External reference
Water Dimer Rotationally Resolved Millimeter-Wave Spectrum Observation at Room Temperature
10.1103/physrevlett.110.093001 · 2013 · External reference
Atmospheric Water Vapor Complexes and the Continuum
10.1029/2003gl018914 · 2004 · External reference
Atmospheric Detection of Water Dimers via Near-Infrared Absorption
10.1126/science.1082282 · 2003 · External reference
Intermolecular V-V Energy Transfer in the Photodissociation of Weakly Bound Complexes: A New Experimental Approach
10.1103/physrevlett.71.54 · 1993 · External reference
New Section of the HITRAN Database: Collision-Induced Absorption (CIA)
10.1016/j.jqsrt.2011.11.004 · 2012 · External reference
H2O–N2 Collision-Induced Absorption Band Intensity in the Region of the N2 Fundamental: Ab Initio Investigation of Its Temperature Dependence and Comparison with Laboratory Data
10.1098/rsta.2011.0189 · 2012 · External reference
Water Vapor Continuous Absorption in Various Mixtures: Possible Role of Weakly Bound Complexes
10.1016/s0022-4073(98)00142-3 · 2000 · External reference
Microwave Spectrum and Molecular Structure of the N2–H2O Complex
10.1063/1.456149 · 1989 · External reference
The Vibration-Rotation-Tunneling Levels of N2–H2O and N2–D2O
10.1063/1.4923339 · 2015 · External reference
Explicitly Correlated ab Initio Potential Energy Surface and Predicted Rovibrational Spectra for H2O–N2 and D2O–N2 Complexes
10.1063/5.0009098 · 2020 · External reference
Intermolecular Potential and Second Virial Coefficient of the Water-Nitrogen Complex
10.1063/1.2446843 · 2007 · External reference
Potential Energy Surfaces for Interactions of H2O with H2, N2 and O2: A Hyperspherical Harmonics Representation, and a Minimal Model for the H2O–Rare-Gas-Atom Systems
10.1016/j.comptc.2011.12.024 · 2012 · External reference
Anchoring the Potential Energy Surface of the Nitrogen/Water Dimer, N2···H2O, with Explicitly Correlated Coupled-Cluster Computations
10.1016/j.comptc.2013.06.035 · 2013 · External reference
Rovibrational Analysis of the Water Bending Vibration in the Mid-Infrared Spectrum of Atmospherically Significant N2–H2O Complex
10.1016/j.cplett.2015.05.050 · 2015 · External reference
Understanding the High-Resolution Spectral Signature of the N2–H2O van der Waals Complex in the 2OH Stretch Region
10.1063/5.0150823 · 2023 · External reference
Infrared Spectra and Tunneling Dynamics of the N2–D2O and OC–D2O Complexes in the ν2 Bend Region of D2O
10.1063/1.4836616 · 2013 · External reference
Spectroscopic Study of the Tunneling Dynamics in N2–Water Observed in the O–D Stretch Region
10.1063/5.0071732 · 2021 · External reference
Basis-Set Convergence in Correlated Calculations on Ne, N2, and H2O
10.1016/s0009-2614(98)00111-0 · 1998 · External reference
On the Role of High Excitations in the Intermolecular Potential of H2–CO
10.1080/00268970600659537 · 2006 · External reference
ABLRI: A Program for Calculating the Long-Range Interaction Energy between Two Monomers in Their Non-Degenerate States
10.1063/5.0205486 · 2024 · External reference
Coupled-Cluster Methods Including Noniterative Corrections for Quadruple Excitations
10.1063/1.1950567 · 2005 · External reference
Molpro: A General-Purpose Quantum Chemistry Program Package
10.1002/wcms.82 · 2012 · External reference
The Molpro Quantum Chemistry Package
10.1063/5.0005081 · 2020 · External reference
The MRCC Program System: Accurate Quantum Chemistry from Water to Proteins
10.1063/1.5142048 · 2020 · External reference
Overview of Developments in the MRCC Program System
10.1021/acs.jpca.4c07807 · 2025 · External reference
Generalized Discrete Variable Approximation in Quantum Mechanics
10.1063/1.448462 · 1985 · External reference
A Novel Discrete Variable Representation for Quantum Mechanical Reactive Scattering via the S-Matrix Kohn Method
10.1063/1.462100 · 1992 · External reference
Molpro: A General-Purpose Quantum Chemistry Program Package
10.1002/wcms.82 · ExternalCitation · doi-reference
Potential Energy Surfaces for Interactions of H2O with H2, N2 and O2: A Hyperspherical Harmonics Representation, and a Minimal Model for the H2O–Rare-Gas-Atom Systems
10.1016/j.comptc.2011.12.024 · ExternalCitation · doi-reference
Anchoring the Potential Energy Surface of the Nitrogen/Water Dimer, N2···H2O, with Explicitly Correlated Coupled-Cluster Computations
10.1016/j.comptc.2013.06.035 · ExternalCitation · doi-reference
Rovibrational Analysis of the Water Bending Vibration in the Mid-Infrared Spectrum of Atmospherically Significant N2–H2O Complex
10.1016/j.cplett.2015.05.050 · ExternalCitation · doi-reference
New Section of the HITRAN Database: Collision-Induced Absorption (CIA)
10.1016/j.jqsrt.2011.11.004 · ExternalCitation · doi-reference
Basis-Set Convergence in Correlated Calculations on Ne, N2, and H2O
10.1016/s0009-2614(98)00111-0 · ExternalCitation · doi-reference
Water Vapor Continuous Absorption in Various Mixtures: Possible Role of Weakly Bound Complexes
10.1016/s0022-4073(98)00142-3 · ExternalCitation · doi-reference
Neural Network Potential Energy Surfaces for Small Molecules and Reactions
10.1021/acs.chemrev.0c00665 · ExternalCitation · doi-reference
Machine Learning Force Fields
10.1021/acs.chemrev.0c01111 · ExternalCitation · doi-reference
Data Generation for Machine Learning Interatomic Potentials and Beyond
10.1021/acs.chemrev.4c00572 · ExternalCitation · doi-reference
Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials
10.1021/acs.jctc.1c00166 · ExternalCitation · doi-reference
Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models
10.1021/acs.jctc.1c00249 · ExternalCitation · doi-reference
CONI-Net: Machine Learning of Separable Intermolecular Force Fields
10.1021/acs.jctc.1c00328 · ExternalCitation · doi-reference
When Gold Is Not Enough: Platinum Standard of Quantum Chemistry with N7 Cost
10.1021/acs.jctc.2c00460 · ExternalCitation · doi-reference
Δ-Machine Learned Potential Energy Surfaces and Force Fields
10.1021/acs.jctc.2c01034 · ExternalCitation · doi-reference
MLRNet: Combining the Physics-Motivated Potential Models with Neural Networks for Intermolecular Potential Energy Surface Construction
10.1021/acs.jctc.2c01049 · ExternalCitation · doi-reference
Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy
10.1021/acs.jctc.2c01203 · ExternalCitation · doi-reference
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
10.1021/acs.jctc.5b00099 · ExternalCitation · doi-reference
Transfer Learning for Predictive Molecular Simulations: Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy
10.1021/acs.jctc.5c00523 · ExternalCitation · doi-reference
Ab initio potential energy surfaces and quantum dynamics for polyatomic bimolecular reactions
10.1021/acs.jctc.8b00006 · ExternalCitation · doi-reference
Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited
10.1021/acs.jctc.8b00832 · ExternalCitation · doi-reference
Overview of Developments in the MRCC Program System
10.1021/acs.jpca.4c07807 · ExternalCitation · doi-reference
Perturbation theory approach to intermolecular potential energy surfaces of van der Waals complexes
10.1021/cr00031a008 · ExternalCitation · doi-reference
From intermolecular potentials to the spectra of van der Waals molecules, and vice versa
10.1021/cr00031a009 · ExternalCitation · doi-reference
Basis Set Convergence of the Post-CCSD(T) Contribution to Noncovalent Interaction Energies
10.1021/ct500347q · ExternalCitation · doi-reference
Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization
10.1021/ct9004917 · ExternalCitation · doi-reference
Calculation of Rotation-Vibration Energy Levels of the Water Molecule with Near-Experimental Accuracy Based on an ab Initio Potential Energy Surface
10.1021/jp312343z · ExternalCitation · doi-reference
Atmospheric Water Vapor Complexes and the Continuum
10.1029/2003gl018914 · ExternalCitation · doi-reference
Approaching Coupled Cluster Accuracy with a General-Purpose Neural Network Potential through Transfer Learning
10.1038/s41467-019-10827-4 · ExternalCitation · doi-reference
Quantum Chemical Accuracy from Density Functional Approximations via Machine Learning
10.1038/s41467-020-19093-1 · ExternalCitation · doi-reference
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events
10.1038/s41524-020-0283-z · ExternalCitation · doi-reference
Machine Learning for Molecular and Materials Science
10.1038/s41586-018-0337-2 · ExternalCitation · doi-reference
Uncertainty-Driven Dynamics for Active Learning of Interatomic Potentials
10.1038/s43588-023-00406-5 · ExternalCitation · doi-reference
The TensorMol-0.1 Model Chemistry: A Neural Network Augmented with Long-Range Physics
10.1039/c7sc04934j · ExternalCitation · doi-reference
Intelligent Understanding of Spectra: From Structural Elucidation to Property Design
10.1039/d4cs01293c · ExternalCitation · doi-reference
Coupled-Cluster Methods Including Noniterative Corrections for Quadruple Excitations
10.1063/1.1950567 · ExternalCitation · doi-reference
Intermolecular Potential and Second Virial Coefficient of the Water-Nitrogen Complex
10.1063/1.2446843 · ExternalCitation · doi-reference
A Hierarchical Construction Scheme for Accurate Potential Energy Surface Generation: An Application to the F+H2 Reaction
10.1063/1.2955729 · ExternalCitation · doi-reference
An Improved Simple Model for the van der Waals Potential Based on Universal Damping Functions for the Dispersion Coefficients
10.1063/1.447150 · ExternalCitation · doi-reference
Generalized Discrete Variable Approximation in Quantum Mechanics
10.1063/1.448462 · ExternalCitation · doi-reference
Microwave Spectrum and Molecular Structure of the N2–H2O Complex
10.1063/1.456149 · ExternalCitation · doi-reference
A Novel Discrete Variable Representation for Quantum Mechanical Reactive Scattering via the S-Matrix Kohn Method
10.1063/1.462100 · ExternalCitation · doi-reference
Permutation Invariant Polynomial Neural Network Approach to Fitting Potential Energy Surfaces
10.1063/1.4817187 · ExternalCitation · doi-reference
Analytic Morse/Long-Range Potential Energy Surfaces and Predicted Infrared Spectra for CO–H2 Dimer and Frequency Shifts of CO in (para-H2)NN = 1–20 Clusters
10.1063/1.4826595 · ExternalCitation · doi-reference
Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
10.1063/1.4832697 · ExternalCitation · doi-reference
Infrared Spectra and Tunneling Dynamics of the N2–D2O and OC–D2O Complexes in the ν2 Bend Region of D2O
10.1063/1.4836616 · ExternalCitation · doi-reference
The Vibration-Rotation-Tunneling Levels of N2–H2O and N2–D2O
10.1063/1.4923339 · ExternalCitation · doi-reference
Communication: Fitting Potential Energy Surfaces with Fundamental Invariant Neural Network
10.1063/1.4961454 · ExternalCitation · doi-reference
Perspective: Accurate Ro-Vibrational Calculations on Small Molecules
10.1063/1.4962907 · ExternalCitation · doi-reference
Perspective: Computing (Ro-)Vibrational Spectra of Molecules with More than Four Atoms
10.1063/1.4979117 · ExternalCitation · doi-reference
Non-Covalent Interactions across Organic and Biological Subsets of Chemical Space: Physics-Based Potentials Parametrized from Machine Learning
10.1063/1.5009502 · ExternalCitation · doi-reference
Less Is More: Sampling Chemical Space with Active Learning
10.1063/1.5023802 · ExternalCitation · doi-reference
Platinum, Gold, and Silver Standards of Intermolecular Interaction Energy Calculations
10.1063/1.5116151 · ExternalCitation · doi-reference
The MRCC Program System: Accurate Quantum Chemistry from Water to Proteins
10.1063/1.5142048 · ExternalCitation · doi-reference
Automatically Growing Global Reactive Neural Network Potential Energy Surfaces: A Trajectory-Free Active Learning Strategy
10.1063/5.0004944 · ExternalCitation · doi-reference
The Molpro Quantum Chemistry Package
10.1063/5.0005081 · ExternalCitation · doi-reference
Hierarchical Machine Learning of Potential Energy Surfaces
10.1063/5.0006498 · ExternalCitation · doi-reference
Explicitly Correlated ab Initio Potential Energy Surface and Predicted Rovibrational Spectra for H2O–N2 and D2O–N2 Complexes
10.1063/5.0009098 · ExternalCitation · doi-reference
Δ-Machine Learning for Potential Energy Surfaces: A PIP Approach to Bring a DFT-Based PES to the CCSD(T) Level of Theory
10.1063/5.0038301 · ExternalCitation · doi-reference
Spectroscopic Study of the Tunneling Dynamics in N2–Water Observed in the O–D Stretch Region
10.1063/5.0071732 · ExternalCitation · doi-reference
Understanding the High-Resolution Spectral Signature of the N2–H2O van der Waals Complex in the 2OH Stretch Region
10.1063/5.0150823 · ExternalCitation · doi-reference
Transfer-Learned Potential Energy Surfaces: Toward Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality
10.1063/5.0151266 · ExternalCitation · doi-reference
ABLRI: A Program for Calculating the Long-Range Interaction Energy between Two Monomers in Their Non-Degenerate States
10.1063/5.0205486 · ExternalCitation · doi-reference
Applying the Active Learning Strategy to the Construction of Full-Dimensional Neural Network Potential Energy Surfaces: Critical Tests in H2O–He Spectroscopic Calculation
10.1063/5.0263653 · ExternalCitation · doi-reference
On the Role of High Excitations in the Intermolecular Potential of H2–CO
10.1080/00268970600659537 · ExternalCitation · doi-reference
A New Potential Function Form Incorporating Extended Long-Range Behaviour: Application to Ground-State Ca2
10.1080/00268970701241656 · ExternalCitation · doi-reference
Long-Range Damping Functions Improve the Short-Range Behaviour of “MLR” Potential Energy Functions
10.1080/00268976.2010.527304 · ExternalCitation · doi-reference
Permutationally Invariant Potential Energy Surfaces in High Dimensionality
10.1080/01442350903234923 · ExternalCitation · doi-reference
Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial-neural network approach
10.1080/0144235x.2016.1200347 · ExternalCitation · doi-reference
Theoretical Methods for Small-Molecule Ro-Vibrational Spectroscopy
10.1088/0953-4075/43/13/133001 · ExternalCitation · doi-reference
High-Accuracy Calculations of the Rotation-Vibration Spectrum of H3+
10.1088/1361-6455/aa8ca6 · ExternalCitation · doi-reference
H2O–N2 Collision-Induced Absorption Band Intensity in the Region of the N2 Fundamental: Ab Initio Investigation of Its Temperature Dependence and Comparison with Laboratory Data
10.1098/rsta.2011.0189 · ExternalCitation · doi-reference
Water Dimer Rotationally Resolved Millimeter-Wave Spectrum Observation at Room Temperature
10.1103/physrevlett.110.093001 · ExternalCitation · doi-reference
Intermolecular V-V Energy Transfer in the Photodissociation of Weakly Bound Complexes: A New Experimental Approach
10.1103/physrevlett.71.54 · ExternalCitation · doi-reference
Atmospheric Detection of Water Dimers via Near-Infrared Absorption
10.1126/science.1082282 · ExternalCitation · doi-reference
Water Vapor Feedback in Climate Models
10.1126/science.1119258 · ExternalCitation · doi-reference
Theory Untangles the High-Resolution Infrared Spectrum of the ortho-H2–CO van der Waals Complex
10.1126/science.1221000 · ExternalCitation · doi-reference
The Vibrational Spectroscopy and Dynamics of Weakly Bound Neutral Complexes
10.1126/science.240.4851.447 · ExternalCitation · doi-reference
Machine Learning for Molecular Simulation
10.1146/annurev-physchem-042018-052331 · ExternalCitation · doi-reference
Permutationally Invariant Potential Energy Surfaces
10.1146/annurev-physchem-050317-021139 · ExternalCitation · doi-reference
Constructing Multidimensional Molecular Potential Energy Surfaces from ab Initio Data
10.1146/annurev.physchem.50.1.537 · ExternalCitation · doi-reference
