machine learning

Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions

Anstine D., Zubatyuk R., Gallegos L., Paton R., Wiest O., Nebgen B., Jones T., Gomes G., Tretiak S., Isayev O.

(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.

All that glitters is not gold: Importance of rigorous evaluation of proteochemometric models

Avdiunina P., Jamal S., Gusev F., Isayev O.

(2025)

Proteochemometric models (PCM) are used in computational drug discovery to leverage both protein and ligand representations for bioactivity prediction.

AIQM3: Targeting Coupled-Cluster Accuracy with Semi-Empirical Speed Across Seven Main Group Elements

Chen Y., Hou Y., Zubatyuk R., Isayev O., Dral P. O.

(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.

Machine Learning anomaly detection of automated HPLC experiments in the Cloud Laboratory

Gusev F., Kline B. C., Quinn R., Xu A., Smith B., Frezza B., Isayev O.

(2025)

Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility.

Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory

Gusev F., Kline B. C., Quinn R., Xu A., Smith B., Frezza B., Isayev O.

Digital Discovery, 4, 3445–3454 (2025)

Autonomous experiments are vulnerable to unforeseen adverse events.

Machine learning interatomic potentials at the centennial crossroads of quantum mechanics

Kalita B., Gokcan H., Isayev O.

Nature Computational Science, 5, 1120–1132 (2025)

Machine learning interatomic potentials at the centennial crossroads of quantum mechanics.

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry

Kalita B., Zubatyuk R., Anstine D. M., Bergeler M., Settels V., Stork C., Spicher S., Isayev O.

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

Kalita B., Zubatyuk R., Anstine D. M., Bergeler M., Settels V., Stork C., Spicher S., Isayev O.

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.

Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Liu Z., Vinskus J., Fu Y., Liu P., Noonan K. J. T., Isayev O.

JACS Au, 5, 4750–4761 (2025)

Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions.

Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer

Sarma R., Wang Y., Hebert D., Tran E., Shao C., Fu S., Cho I., Isayev O., Garcia-Bosch I.

(2025)

Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion.

Discovery of Novel Celecoxib Polymorphs Using AIMNet2 Machine Learning Interatomic Potential

Zheng P., Abramov Y., Sun C. C., Isayev O.

(2025)

Polymorphism plays a pivotal role in defining the solid-state properties of pharmaceutical compounds, yet the discovery and accurate energy ranking of polymorphs remain a challenge.

High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning

Zheng P., Isayev O.

Chemical Science, 16, 20553–20563 (2025)

Ring Vault contains 201 546 cyclic molecules across 11 elements.

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Dral P. O., Ge F., Hou Y., Zheng P., Chen Y., Barbatti M., Isayev O., Wang C., Xue B., Pinheiro Jr M., Su Y., Dai Y., Chen Y., Zhang L., Zhang S., Ullah A., Zhang Q., Ou Y.

J. Chem. Theory Comput., 20, 1193–1213 (2024)

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

J. Am. Chem. Soc., 146, 21583–21590 (2024)

Discovery of Crystallizable Organic Semiconductors with Machine Learning.

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

(2024)

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts.

Discovery of Crystallizable Organic Semiconductors with Machine Learning

Johnson H. M., Gusev F., Dull J. T., Seo Y., Priestley R. D., Isayev O., Rand B. P.

(2024)

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts.

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Liu Z., Vinskus J., Fu Y., Liu P., Noonan K., Isayev O.

(2024)

Ring strain energy (RSE) is crucial for understanding molecular reactivity.

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Zhang S., Makoś M. Z., Jadrich R. B., Kraka E., Barros K., Nebgen B. T., Tretiak S., Isayev O., Lubbers N., Messerly R. A., Smith J. S.

Nature Chemistry, 16, 727–734 (2024)

Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.

Themed collection on Insightful Machine Learning for Physical Chemistry

Clark A. E., Dral P. O., Tamblyn I., Isayev O.

Physical Chemistry Chemical Physics, 25, 22563–22564 (2023)

This themed collection includes a collection of articles on Insightful Machine Learning for Physical Chemistry.

Synergy of semiempirical models and machine learning in computational chemistry

Fedik N., Nebgen B., Lubbers N., Barros K., Kulichenko M., Li Y. W., Zubatyuk R., Messerly R., Isayev O., Tretiak S.

J. Chem. Phys., 159, 110901 (2023)

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches.

The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions

Liu Z., Moroz Y. S., Isayev O.

(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.

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

Messerly R., Zhang S., Makoś M., Jadrich R., Kraka E., Barros K., Nebgen B., Tretiak S., Isayev O., Lubbers N., Smith J.

(2023)

Abstract Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.

Extending machine learning beyond interatomic potentials for predicting molecular properties

Fedik N., Zubatyuk R., Kulichenko M., Lubbers N., Smith J. S., Nebgen B., Messerly R., Li Y. W., Boldyrev A. I., Barros K., Isayev O., Tretiak S.

Nat. Rev. Chem., 6, 653–672 (2022)

Extending machine learning beyond interatomic potentials for predicting molecular properties.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2022)

The behavior of proteins is closely related to the protonation states of the residues.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2022)

The behavior of proteins is closely related to the protonation states of the residues.

Active learning guided drug design lead optimization based on relative binding free energy modeling

Gusev F., Gutkin E., Kurnikova M. G., Isayev O.

(2022)

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE).

Roadmap on Machine learning in electronic structure

Kulik H. J., Hammerschmidt T., Schmidt J., Botti S., Marques M. A. L., Boley M., Scheffler M., Todorovi{\'c} M., Rinke P., Oses C., Smolyanyuk A., Curtarolo S., Tkatchenko A., Bart{\'o}k A. P., Manzhos S., Ihara M., Carrington T., Behler J., Isayev O., Veit M., Grisafi A., Nigam J., Ceriotti M., Sch{\"u}tt K. T., Westermayr J., Gastegger M., Maurer R. J., Kalita B., Burke K., Nagai R., Akashi R., Sugino O., Hermann J., No{\'e} F., Pilati S., Draxl C., Kuban M., Rigamonti S., Scheidgen M., Esters M., Hicks D., Toher C., Balachandran P. V., Tamblyn I., Whitelam S., Bellinger C., Ghiringhelli L. M.

Electron. Struct., 4, 023004 (2022)

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science.

Best practices in machine learning for chemistry

Artrith N., Butler K. T., Coudert F., Han S., Isayev O., Jain A., Walsh A.

Nature Chemistry, 13, 505–508 (2021)

Best practices in machine learning for chemistry.

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

Fronzi M., Isayev O., Winkler D. A., Shapter J. G., Ellis A. V., Sherrell P. C., Shepelin N. A., Corletto A., Ford M. J.

Advanced Intelligent Systems, 3 (2021)

The bandgap is one of the most fundamental properties of condensed matter.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2021)

The behavior of proteins is closely related to the protonation states of the residues.

Prediction of Protein pKa with Representation Learning

Gokcan H., Isayev O.

(2021)

The behavior of proteins is closely related to the protonation states of the residues.

Machine learned Hückel theory: Interfacing physics and deep neural networks

Zubatiuk T., Nebgen B., Lubbers N., Smith J. S., Zubatyuk R., Zhou G., Koh C., Barros K., Isayev O., Tretiak S.

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.

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence

Zubatiuk T., Isayev O.

Accounts of Chemical Research, 54, 1575–1585 (2021)

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence.

Teaching a neural network to attach and detach electrons from molecules

Zubatyuk R., Smith J. S., Nebgen B. T., Tretiak S., Isayev O.

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

Zubatyuk R., Smith J., Nebgen B. T., Tretiak S., Isayev O.

(2021)

Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

Devereux C., Smith J., Davis K., Barros K., Zubatyuk R., Isayev O., Roitberg A.

(2020)

Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles.

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

Fronzi M., Tawfik S. A., Ghazaleh M. A., Isayev O., Winkler D. A., Shapter J., Ford M. J.

Advanced Theory and Simulations, 3 (2020)

AbstractThe screening of novel materials is an important topic in the field of materials science.

Review for: Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

Isayev O.

(2020)

Review for: Assessing Conformer Energies using Electronic Structure and Machine Learning Methods.

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design

Popova M., Ginsburg B., Tropsha A., Isayev O.

(2020)

Deep learning models have demonstrated outstanding results in many data-rich areas of research, such as computer vision and natural language processing.

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Rufa D. A., Bruce Macdonald H. E., Fass J., Wieder M., Grinaway P. B., Roitberg A. E., Isayev O., Chodera J. D.

(2020)

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials.

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Smith J. S., Zubatyuk R., Nebgen B., Lubbers N., Barros K., Roitberg A. E., Isayev O., Tretiak S.

Scientific Data, 7 (2020)

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2020)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2020)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

Teaching a Neural Network to Attach and Detach Electrons from Molecules

Zubatyuk R., Smith J., Nebgen B. T., Tretiak S., Isayev O.

(2020)

Physics-inspired Artificial Intelligence (AI) is at the forefront of methods development in molecular modeling and computational chemistry.

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Smith J. S., Zubatyuk R., Nebgen B. T., Lubbers N., Barros K., Roitberg A., Isayev O., Tretiak S.

(2019)

Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models.

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Smith J. S., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A. E.

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

S Smith J., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A.

(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

S Smith J., Nebgen B. T., Zubatyuk R., Lubbers N., Devereux C., Barros K., Tretiak S., Isayev O., Roitberg A.

(2019)

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset.

Predicting Thermal Properties of Crystals Using Machine Learning

Tawfik S. A., Isayev O., Spencer M. J. S., Winkler D. A.

Advanced Theory and Simulations, 3 (2019)

AbstractCalculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science.

Machine learning for molecular and materials science

Butler K. T., Davies D. W., Cartwright H., Isayev O., Walsh A.

Nature, 559, 547–555 (2018)

Machine learning for molecular and materials science.

Discovering a Transferable Charge Assignment Model Using Machine Learning

Sifain A. E., Lubbers N., Nebgen B. T., Smith J. S., Lokhov A. Y., Isayev O., Roitberg A. E., Barros K., Tretiak S.

(2018)

Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics.

Discovering a Transferable Charge Assignment Model Using Machine Learning

Sifain A. E., Lubbers N., Nebgen B. T., Smith J. S., Lokhov A. Y., Isayev O., Roitberg A. E., Barros K., Tretiak S.

The Journal of Physical Chemistry Letters, 9, 4495–4501 (2018)

Discovering a Transferable Charge Assignment Model Using Machine Learning.

Transforming Computational Drug Discovery with Machine Learning and AI

Smith J. S., Roitberg A. E., Isayev O.

ACS Medicinal Chemistry Letters, 9, 1065–1069 (2018)

Transforming Computational Drug Discovery with Machine Learning and AI.

Less is more: Sampling chemical space with active learning

Smith J. S., Nebgen B., Lubbers N., Isayev O., Roitberg A. E.

The Journal of Chemical Physics, 148 (2018)

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

Tawfik S. A., Isayev O., Stampfl C., Shapter J., Winkler D. A., Ford M. J.

Advanced Theory and Simulations, 2 (2018)

Abstract There are now, in principle, a limitless number of hybrid van der Waals (vdW) heterostructures that can be built from the rapidly growing number of 2D layers.

Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.

Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

Tawfik S., Isayev O., Stampfl C., Shapter J., Winkler D., Ford M. J.

(2018)

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning.

Universal fragment descriptors for predicting properties of inorganic crystals

Isayev O., Oses C., Toher C., Gossett E., Curtarolo S., Tropsha A.

Nature Communications, 8 (2017)

AbstractAlthough historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases.

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

Smith J. S., Isayev O., Roitberg A. E.

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.