Pages that link to "Q33643510"

The following pages link to Machine learning of accurate energy-conserving molecular force fields. (Q33643510):

Displaying 45 items.

• Bypassing the Kohn-Sham equations with machine learning (Q42261808) (← links)
• Machine learning molecular dynamics for the simulation of infrared spectra. (Q45943844) (← links)
• Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels. (Q45946850) (← links)
• ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. (Q47107534) (← links)
• An atomistic fingerprint algorithm for learning ab initio molecular force fields (Q47558547) (← links)
• Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster. (Q48060483) (← links)
• Learning a Local-Variable Model of Aromatic and Conjugated Systems. (Q49384986) (← links)
• Perturbed path integrals in imaginary time: Efficiently modeling nuclear quantum effects in molecules and materials. (Q52653793) (← links)
• The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. (Q55411474) (← links)
• Nonadiabatic Excited-State Dynamics with Machine Learning (Q57029211) (← links)
• Towards exact molecular dynamics simulations with machine-learned force fields (Q58700300) (← links)
• Unmasking Clever Hans predictors and assessing what machines really learn (Q62030385) (← links)
• Electronic structure at coarse-grained resolutions from supervised machine learning (Q64063692) (← links)
• Machine Learning of Coarse-Grained Molecular Dynamics Force Fields (Q64285708) (← links)
• A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes' reactivity. (Q64973287) (← links)
• De novo exploration and self-guided learning of potential-energy surfaces (Q79107437) (← links)
• Coarse-graining auto-encoders for molecular dynamics (Q83558323) (← links)
• Neural Network Based in Silico Simulation of Combustion Reactions (Q84726505) (← links)
• Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (Q89623133) (← links)
• Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics (Q90285011) (← links)
• Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts (Q90518896) (← links)
• A quantitative uncertainty metric controls error in neural network-driven chemical discovery (Q90535520) (← links)
• Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry (Q90653919) (← links)
• Simulation vs Understanding A Tension, in Quantum Chemistry and Beyond. PART B The March of Simulation, for Better or Worse (Q91056542) (← links)
• Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions (Q91291774) (← links)
• Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (Q91528852) (← links)
• Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network (Q92860487) (← links)
• Fast and Accurate Artificial Neural Network Potential Model for MAPbI3 Perovskite Materials (Q92947489) (← links)
• TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials (Q96615384) (← links)
• Machine learning accurate exchange and correlation functionals of the electronic density (Q97522191) (← links)
• Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes (Q98159731) (← links)
• Deep-neural-network solution of the electronic Schrödinger equation (Q99616898) (← links)
• Retrospective on a decade of machine learning for chemical discovery (Q100385791) (← links)
• Quantum chemical accuracy from density functional approximations via machine learning (Q100694202) (← links)
• A general-purpose machine-learning force field for bulk and nanostructured phosphorus (Q101120965) (← links)
• Coarse graining molecular dynamics with graph neural networks (Q102218441) (← links)
• Software and Application Patterns for Explanation Methods (Q102633326) (← links)
• Layer-Wise Relevance Propagation: An Overview (Q102633541) (← links)
• Quantum-Chemical Insights from Interpretable Atomistic Neural Networks (Q102634967) (← links)
• Multiscale computational understanding and growth of 2D materials: a review (Q106473599) (← links)
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems (Q108768061) (← links)
• HOOMD-TF: GPU-Accelerated, Online Machine Learning in the HOOMD-blue Molecular Dynamics Engine (Q113307527) (← links)
• Automated discovery of fundamental variables hidden in experimental data (Q113632741) (← links)
• NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces (Q121463242) (← links)
• Neural network potentials for chemistry: concepts, applications and prospects (Q128210099) (← links)