| scholarly article | Q13442814 |
| P818 | arXiv ID | 2209.11581 |
| P356 | DOI | 10.1039/D2DD00102K |
| P50 | author | Markus Meuwly | Q87909911 |
| P2093 | author name string | Kai Töpfer | |
| Silvan Käser | |||
| Luis Itza Vazquez-Salazar | |||
| P2860 | cites work | A logical calculus of the ideas immanent in nervous activity | Q22337370 |
| Deep learning for computational biology | Q26740441 | ||
| Comparison of simple potential functions for simulating liquid water | Q26778447 | ||
| Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning | Q27144201 | ||
| The FAIR Guiding Principles for scientific data management and stewardship | Q27942822 | ||
| The roaming atom: straying from the reaction path in formaldehyde decomposition | Q28289101 | ||
| Quantum-chemical insights from deep tensor neural networks | Q28817466 | ||
| U1 snRNP regulates cancer cell migration and invasion in vitro | Q92480757 | ||
| The C(3P) + NO(X2Π) → O(3P) + CN(X2Σ+), N(2D)/N(4S) + CO(X1Σ+) reaction: Rates, branching ratios, and final states from 15 K to 20 000 K | Q91339538 | ||
| PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges | Q91666219 | ||
| Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics | Q92080634 | ||
| The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe | Q92381014 | ||
| Learning representations by back-propagating errors | Q29469983 | ||
| Neural Networks for the Prediction of Organic Chemistry Reactions | Q30826751 | ||
| Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach | Q31024412 | ||
| ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. | Q33631061 | ||
| Machine learning of accurate energy-conserving molecular force fields. | Q33643510 | ||
| Ab initio quantum chemistry: methodology and applications | Q33784246 | ||
| Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations | Q34019146 | ||
| The perceptron: a probabilistic model for information storage and organization in the brain | Q34245162 | ||
| Quantum chemistry structures and properties of 134 kilo molecules | Q35066538 | ||
| Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space | Q35765681 | ||
| Free energies of chemical reactions in solution and in enzymes with ab initio quantum mechanics/molecular mechanics methods | Q37055269 | ||
| Systematic methods for structurally consistent coarse-grained models | Q38049271 | ||
| Potential energy surface fitting by a statistically localized, permutationally invariant, local interpolating moving least squares method for the many-body potential: method and application to N4. | Q38186748 | ||
| A survey on deep learning in medical image analysis | Q38646751 | ||
| Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces. | Q38699766 | ||
| First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems | Q38777403 | ||
| Machine learning unifies the modeling of materials and molecules. | Q45942900 | ||
| Machine learning molecular dynamics for the simulation of infrared spectra. | Q45943844 | ||
| Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. | Q45945599 | ||
| Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. | Q45949599 | ||
| Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations. | Q46007547 | ||
| The graph neural network model | Q46044773 | ||
| ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. | Q47107534 | ||
| Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network | Q48060404 | ||
| Planning chemical syntheses with deep neural networks and symbolic AI. | Q51095881 | ||
| Construction of high-dimensional neural network potentials using environment-dependent atom pairs. | Q51365882 | ||
| Permutationally Invariant Potential Energy Surfaces. | Q51366354 | ||
| Effect of the damping function in dispersion corrected density functional theory. | Q51596718 | ||
| Ab initio global potential-energy surface for H5(+) --> H3(+) + H2. | Q51633406 | ||
| Neural Network Models of Potential Energy Surfaces: Prototypical Examples. | Q51981198 | ||
| Deriving effective mesoscale potentials from atomistic simulations. | Q52010759 | ||
| Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. | Q52310020 | ||
| Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. | Q52563927 | ||
| Ab Initio Potential Energy Surfaces and Quantum Dynamics for Polyatomic Bimolecular Reactions. | Q52630907 | ||
| "Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation. | Q53778323 | ||
| Zur Quantentheorie der Molekeln | Q54152578 | ||
| Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems. | Q54514855 | ||
| Communication: Fitting potential energy surfaces with fundamental invariant neural network. | Q54756737 | ||
| The Matter Simulation (R)evolution. | Q55064556 | ||
| Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality | Q92496550 | ||
| Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation | Q92517316 | ||
| Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations | Q92525564 | ||
| Bayesian machine learning for quantum molecular dynamics | Q92528184 | ||
| Exhaustive state-to-state cross sections for reactive molecular collisions from importance sampling simulation and a neural network representation | Q92603009 | ||
| A Machine Learning Approach for Prediction of Rate Constants | Q92688040 | ||
| Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia | Q93123923 | ||
| Hierarchical machine learning of potential energy surfaces | Q96119225 | ||
| Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms | Q96119477 | ||
| Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations | Q96165553 | ||
| Dynamics on Multiple Potential Energy Surfaces: Quantitative Studies of Elementary Processes Relevant to Hypersonics | Q96222923 | ||
| Polarizable embedding QM/MM: the future gold standard for complex (bio)systems? | Q96690116 | ||
| Transfer Learning for Drug Discovery | Q97549068 | ||
| Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials | Q97549116 | ||
| Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions | Q97675652 | ||
| AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials | Q98203298 | ||
| Wavelet scattering networks for atomistic systems with extrapolation of material properties | Q98886892 | ||
| Quantum machine learning using atom-in-molecule-based fragments selected on the fly | Q99418783 | ||
| Retrospective on a decade of machine learning for chemical discovery | Q100385791 | ||
| Neural Network Potential Energy Surfaces for Small Molecules and Reactions | Q100456262 | ||
| Quantum chemical accuracy from density functional approximations via machine learning | Q100694202 | ||
| Training atomic neural networks using fragment-based data generated in virtual reality | Q100943057 | ||
| Incompleteness of Atomic Structure Representations | Q101125414 | ||
| Machine Learning for Electronically Excited States of Molecules | Q102204876 | ||
| Towards Explainable Artificial Intelligence | Q102362940 | ||
| Quantum-Chemical Insights from Interpretable Atomistic Neural Networks | Q102634967 | ||
| Capturing roaming molecular fragments in real time | Q103000458 | ||
| DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics | Q104006308 | ||
| Full-dimensional, ab initio potential energy surface for glycine with characterization of stationary points and zero-point energy calculations by means of diffusion Monte Carlo and semiclassical dynamics | Q104678473 | ||
| Green Algorithms: Quantifying the Carbon Footprint of Computation | Q108653809 | ||
| Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems | Q108768061 | ||
| Gaussian Process Regression for Materials and Molecules | Q111149624 | ||
| A taxonomic, genetic and ecological data resource for the vascular plants of Britain and Ireland | Q112331195 | ||
| Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning | Q113314355 | ||
| Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data | Q113314527 | ||
| Machine Learning Approach for Describing Water OH Stretch Vibrations | Q113314859 | ||
| Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments | Q113314867 | ||
| Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies | Q113314948 | ||
| Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models | Q113315013 | ||
| Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems | Q113315244 | ||
| Machine Learning for Chemical Reactions | Q114871883 | ||
| Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO2 Self-Reaction and Its Dynamics Study | Q114946883 | ||
| Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science | Q115741827 | ||
| Deep physical neural networks trained with backpropagation | Q115778052 | ||
| The Carbon Footprint of Bioinformatics | Q120174495 | ||
| NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces | Q121463242 | ||
| Deep potentials for materials science | Q121463299 | ||
| A survey of deep learning techniques for autonomous driving | Q126797068 | ||
| Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design | Q127334867 | ||
| Sampling molecular conformations and dynamics in a multiuser virtual reality framework. | Q55401272 | ||
| The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. | Q55411474 | ||
| Multilayer feedforward networks are universal approximators | Q56020518 | ||
| Computational Chemistry | Q56040351 | ||
| Cramming More Components Onto Integrated Circuits | Q56156888 | ||
| Approximation by superpositions of a sigmoidal function | Q56532755 | ||
| Approximation capabilities of multilayer feedforward networks | Q56532756 | ||
| Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties | Q56919389 | ||
| Machine learning of molecular electronic properties in chemical compound space | Q56919402 | ||
| Protein Family-specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data | Q57056976 | ||
| A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression | Q57144630 | ||
| A dual‐level Shepard interpolation method for generating potential energy surfaces for dynamics calculations | Q57402413 | ||
| The potential energy surface and near-dissociation states of He-H2+ | Q57961315 | ||
| Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach | Q58280316 | ||
| Explicitly correlated MRCI-F12 potential energy surfaces for methane fit with several permutation invariant schemes and full-dimensional vibrational calculations | Q58280363 | ||
| Do Better Quality Embedding Potentials Accelerate the Convergence of QM/MM Models? The Case of Solvated Acid Clusters | Q58692239 | ||
| Towards exact molecular dynamics simulations with machine-learned force fields | Q58700300 | ||
| Potential energy surfaces for macromolecules. A neural network technique | Q59448377 | ||
| A Survey on Transfer Learning | Q59678580 | ||
| A refined ring polymer molecular dynamics theory of chemical reaction rates | Q60303463 | ||
| One Molecule, Two Atoms, Three Views, Four Bonds? | Q60326942 | ||
| Unmasking Clever Hans predictors and assessing what machines really learn | Q62030385 | ||
| SchNet - A deep learning architecture for molecules and materials | Q62495627 | ||
| Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials | Q62665314 | ||
| Constructing high-dimensional neural network potentials: A tutorial review | Q62665341 | ||
| Atom-centered symmetry functions for constructing high-dimensional neural network potentials | Q62665362 | ||
| High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide | Q62665364 | ||
| Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces | Q62665382 | ||
| wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials | Q62787589 | ||
| Machine learning material properties from the periodic table using convolutional neural networks. | Q64922267 | ||
| Rate constants for fine-structure excitations in O-H collisions with error bars obtained by machine learning | Q69166171 | ||
| Calculation of effective interaction potentials from radial distribution functions: A reverse Monte Carlo approach | Q78072769 | ||
| A random-sampling high dimensional model representation neural network for building potential energy surfaces | Q80240255 | ||
| A hierarchical construction scheme for accurate potential energy surface generation: an application to the F+H2 reaction | Q81635578 | ||
| Permutation invariant polynomial neural network approach to fitting potential energy surfaces | Q87221121 | ||
| A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information | Q89362831 | ||
| Hierarchical modeling of molecular energies using a deep neural network | Q89362888 | ||
| Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy | Q89363024 | ||
| FCHL revisited: Faster and more accurate quantum machine learning | Q89480716 | ||
| The N(4S) + O2(X3Σ) ↔ O(3P) + NO(X2Π) reaction: thermal and vibrational relaxation rates for the 2A', 4A' and 2A'' states | Q89521482 | ||
| Machine Learning for Molecular Simulation | Q89858287 | ||
| Unsupervised machine learning in atomistic simulations, between predictions and understanding | Q90035140 | ||
| Sharing Data from Molecular Simulations | Q90126305 | ||
| Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited | Q90275056 | ||
| Definitions, methods, and applications in interpretable machine learning | Q90753735 | ||
| Near dissociation states for H2+-He on MRCI and FCI potential energy surfaces | Q91220542 | ||
| Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground | Q91337726 | ||
| P433 | issue | 1 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | artificial neural network | Q192776 |
| chemistry | Q2329 | ||
| P304 | page(s) | 28-58 | |
| P577 | publication date | 2023-01-01 | |
| P1433 | published in | Digital Discovery | Q123693765 |
| P1476 | title | Neural network potentials for chemistry: concepts, applications and prospects | |
| P478 | volume | 2 |
Search more.