Research Topic
neural network
48 publications exploring this topic
2025
Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
(2025)
Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.
AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs
Chemical Science, 16, 10228–10244 (2025)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
Democratizing Reaction Kinetics through Machine Vision and Learning
(2025)
Democratizing Reaction Kinetics through Machine Vision and Learning.
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
Journal of Chemical Theory and Computation, 21, 10362–10372 (2025)
Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials.
AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements
(2025)
The AIQM series methods are successful neural network-based models that target coupled-cluster accuracy while maintaining high robustness and transferability across various tasks by leveraging Δ-learning.
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie International Edition (2025)
Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.
AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry
Angewandte Chemie (2025)
Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges.
Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials
Crystal Growth & Design, 25, 9092–9106 (2025)
Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.
Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy
(2025)
Molecular geometry is crucial for biological activity and chemical reactivity; however, computational methods for generating 3D structures are limited by the vast scale of conformational space and the complexities of stereochemistry.
Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer
(2025)
Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion.
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry
Journal of Chemical Information and Modeling, 65, 4367–4380 (2025)
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.
2024
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2024)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2024)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials
Journal of Chemical Theory and Computation, 20, 9138–9147 (2024)
ANI/EFP: Modeling Long-Range Interactions in ANI Neural Network with Effective Fragment Potentials.
Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions
(2024)
Ring strain energy (RSE) is crucial for understanding molecular reactivity.
2023
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
(2023)
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
(2023)
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets.
2022
Prediction of Protein pKa with Representation Learning
(2022)
The behavior of proteins is closely related to the protonation states of the residues.
Prediction of Protein pKa with Representation Learning
(2022)
The behavior of proteins is closely related to the protonation states of the residues.
Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds
Communications Chemistry, 5 (2022)
AbstractDeep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.
Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials
(2022)
Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input.
Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials
(2022)
Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input.
2021
Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures
Advanced Intelligent Systems, 3 (2021)
The bandgap is one of the most fundamental properties of condensed matter.
Prediction of Protein pKa with Representation Learning
(2021)
The behavior of proteins is closely related to the protonation states of the residues.
Prediction of Protein pKa with Representation Learning
(2021)
The behavior of proteins is closely related to the protonation states of the residues.
A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery
(2021)
Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties.
Artificial intelligence-enhanced quantum chemical method with broad applicability
Nature Communications, 12 (2021)
Abstract High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level.
Machine learned Hückel theory: Interfacing physics and deep neural networks
The Journal of Chemical Physics, 154 (2021)
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials.
Teaching a neural network to attach and detach electrons from molecules
Nature Communications, 12 (2021)
Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2021)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2020
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials
Journal of Chemical Information and Modeling, 60, 3408–3415 (2020)
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials.
TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials
(2020)
This paper presents TorchANI, a PyTorch based software for training/inferenceof ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces andother physical properties of molecular systems.
DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
(2020)
Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).
DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
(2020)
Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).
DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions
(2020)
Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).
DRACON: disconnected graph neural network for atom mapping in chemical reactions
Physical Chemistry Chemical Physics, 22, 26478–26486 (2020)
We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs.
Teaching a Neural Network to Attach and Detach Electrons from Molecules
(2020)
Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.
2019
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Nature Communications, 10 (2019)
Abstract Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset.
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
(2019)
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
(2019)
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Science Advances, 5 (2019)
We introduce a modular, chemically inspired deep neural network model for prediction of several atomic and molecular properties.
2018
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks
Journal of Chemical Theory and Computation, 14, 4687–4698 (2018)
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks.
Discovering a Transferable Charge Assignment Model Using Machine Learning
(2018)
Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics.
Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
(2018)
Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.
Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
(2018)
Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.
Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network
(2018)
Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena.
2017
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Scientific Data, 4 (2017)
AbstractOne of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Chemical Science, 8, 3192–3203 (2017)
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms.