2024
Zhang, Shuhao; Makoś, Małgorzata Z.; Jadrich, Ryan B.; Kraka, Elfi; Barros, Kipton; Nebgen, Benjamin T.; Tretiak, Sergei; Isayev, Olexandr; Lubbers, Nicholas; Messerly, Richard A.; Smith, Justin S.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential Journal Article
In: Nat. Chem., 2024.
Abstract | Links | BibTeX | Tags: Active learning, ANI, Organic reactions
@article{Zhang2024,
title = {Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential},
author = {Shuhao Zhang and Ma\lgorzata Z. Mako\'{s} and Ryan B. Jadrich and Elfi Kraka and Kipton Barros and Benjamin T. Nebgen and Sergei Tretiak and Olexandr Isayev and Nicholas Lubbers and Richard A. Messerly and Justin S. Smith},
doi = {10.1038/s41557-023-01427-3},
year = {2024},
date = {2024-03-07},
urldate = {2024-03-07},
journal = {Nat. Chem.},
publisher = {Springer Science and Business Media LLC},
abstract = {Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.},
keywords = {Active learning, ANI, Organic reactions},
pubstate = {published},
tppubtype = {article}
}
Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.
Dral, Pavlo O.; Ge, Fuchun; Hou, Yi-Fan; Zheng, Peikun; Chen, Yuxinxin; Barbatti, Mario; Isayev, Olexandr; Wang, Cheng; Xue, Bao-Xin; Jr, Max Pinheiro; Su, Yuming; Dai, Yiheng; Chen, Yangtao; Zhang, Lina; Zhang, Shuang; Ullah, Arif; Zhang, Quanhao; Ou, Yanchi
MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows Journal Article
In: J. Chem. Theory Comput., vol. 20, no. 3, pp. 1193–1213, 2024.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{Dral2024,
title = {MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows},
author = {Pavlo O. Dral and Fuchun Ge and Yi-Fan Hou and Peikun Zheng and Yuxinxin Chen and Mario Barbatti and Olexandr Isayev and Cheng Wang and Bao-Xin Xue and Max Pinheiro Jr and Yuming Su and Yiheng Dai and Yangtao Chen and Lina Zhang and Shuang Zhang and Arif Ullah and Quanhao Zhang and Yanchi Ou},
doi = {10.1021/acs.jctc.3c01203},
year = {2024},
date = {2024-02-13},
urldate = {2024-02-13},
journal = {J. Chem. Theory Comput.},
volume = {20},
number = {3},
pages = {1193--1213},
publisher = {American Chemical Society (ACS)},
abstract = {Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
2023
Inizan, Théo Jaffrelot; Plé, Thomas; Adjoua, Olivier; Ren, Pengyu; Gökcan, Hatice; Isayev, Olexandr; Lagardère, Louis; Piquemal, Jean-Philip
Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects Journal Article
In: Chem. Sci., vol. 14, no. 20, pp. 5438–5452, 2023.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{JaffrelotInizan2023,
title = {Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects},
author = {Th\'{e}o Jaffrelot Inizan and Thomas Pl\'{e} and Olivier Adjoua and Pengyu Ren and Hatice G\"{o}kcan and Olexandr Isayev and Louis Lagard\`{e}re and Jean-Philip Piquemal},
doi = {10.1039/d2sc04815a},
year = {2023},
date = {2023-05-24},
urldate = {2023-05-24},
journal = {Chem. Sci.},
volume = {14},
number = {20},
pages = {5438--5452},
publisher = {Royal Society of Chemistry (RSC)},
abstract = {\<jats:p\>Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models.\</jats:p\>},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models.</jats:p>
Anstine, Dylan M.; Isayev, Olexandr
Machine Learning Interatomic Potentials and Long-Range Physics Journal Article
In: J. Phys. Chem. A, vol. 127, no. 11, pp. 2417–2431, 2023, ISSN: 1520-5215.
Abstract | Links | BibTeX | Tags: AIMNet, ANI, Machine learning potential, Review
@article{Anstine2023,
title = {Machine Learning Interatomic Potentials and Long-Range Physics},
author = {Dylan M. Anstine and Olexandr Isayev},
doi = {10.1021/acs.jpca.2c06778},
issn = {1520-5215},
year = {2023},
date = {2023-03-23},
urldate = {2023-03-23},
journal = {J. Phys. Chem. A},
volume = {127},
number = {11},
pages = {2417--2431},
publisher = {American Chemical Society (ACS)},
abstract = {Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient.},
keywords = {AIMNet, ANI, Machine learning potential, Review},
pubstate = {published},
tppubtype = {article}
}
Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient.
2022
Liu, Zhen; Zubatiuk, Tetiana; Roitberg, Adrian; Isayev, Olexandr
Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials Journal Article
In: J. Chem. Inf. Model., vol. 62, no. 22, pp. 5373–5382, 2022.
Abstract | Links | BibTeX | Tags: ANI, Drug Discovery
@article{Liu2022,
title = {Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials},
author = {Zhen Liu and Tetiana Zubatiuk and Adrian Roitberg and Olexandr Isayev},
doi = {10.1021/acs.jcim.2c00817},
year = {2022},
date = {2022-11-28},
urldate = {2022-11-28},
journal = {J. Chem. Inf. Model.},
volume = {62},
number = {22},
pages = {5373--5382},
publisher = {American Chemical Society (ACS)},
abstract = {Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automatize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization, and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereoconfiguration that yields the lowest-energy conformer. With Auto3D, we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked with DFT methods on geometry optimization and electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies and is several orders of magnitude faster than DFT methods.},
keywords = {ANI, Drug Discovery},
pubstate = {published},
tppubtype = {article}
}
Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automatize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization, and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereoconfiguration that yields the lowest-energy conformer. With Auto3D, we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked with DFT methods on geometry optimization and electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies and is several orders of magnitude faster than DFT methods.
Gokcan, Hatice; Isayev, Olexandr
Learning molecular potentials with neural networks Journal Article
In: WIREs Comput Mol Sci, vol. 12, no. 2, pp. e1564, 2022.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential, Review
@article{Gokcan2021,
title = {Learning molecular potentials with neural networks},
author = {Hatice Gokcan and Olexandr Isayev},
doi = {10.1002/wcms.1564},
year = {2022},
date = {2022-07-14},
journal = {WIREs Comput Mol Sci},
volume = {12},
number = {2},
pages = {e1564},
publisher = {Wiley},
abstract = {\<jats:title\>Abstract\</jats:title\>\<jats:p\>The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability.\</jats:p\>\<jats:p\>This article is categorized under:\<jats:list list-type="simple"\>\<jats:list-item\>\<jats:p\>Data Science \> Artificial Intelligence/Machine Learning\</jats:p\>\</jats:list-item\>\<jats:list-item\>\<jats:p\>Molecular and Statistical Mechanics \> Molecular Interactions\</jats:p\>\</jats:list-item\>\<jats:list-item\>\<jats:p\>Software \> Molecular Modeling\</jats:p\>\</jats:list-item\>\</jats:list\>\</jats:p\>},
keywords = {ANI, Machine learning potential, Review},
pubstate = {published},
tppubtype = {article}
}
<jats:title>Abstract</jats:title><jats:p>The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability.</jats:p><jats:p>This article is categorized under:<jats:list list-type="simple"><jats:list-item><jats:p>Data Science > Artificial Intelligence/Machine Learning</jats:p></jats:list-item><jats:list-item><jats:p>Molecular and Statistical Mechanics > Molecular Interactions</jats:p></jats:list-item><jats:list-item><jats:p>Software > Molecular Modeling</jats:p></jats:list-item></jats:list></jats:p>
Zheng, Peikun; Yang, Wudi; Wu, Wei; Isayev, Olexandr; Dral, Pavlo O.
Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods Journal Article
In: J. Phys. Chem. Lett., vol. 13, no. 15, pp. 3479–3491, 2022.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{Zheng2022,
title = {Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods},
author = {Peikun Zheng and Wudi Yang and Wei Wu and Olexandr Isayev and Pavlo O. Dral},
doi = {10.1021/acs.jpclett.2c00734},
year = {2022},
date = {2022-04-21},
urldate = {2022-04-21},
journal = {J. Phys. Chem. Lett.},
volume = {13},
number = {15},
pages = {3479--3491},
publisher = {American Chemical Society (ACS)},
abstract = {Enthalpies of formation and reaction are important thermodynamic properties that have a crucial impact on the outcome of chemical transformations. Here we implement the calculation of enthalpies of formation with a general-purpose ANI-1ccx neural network atomistic potential. We demonstrate on a wide range of benchmark sets that both ANI-1ccx and our other general-purpose data-driven method AIQM1 approach the coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably achieved without specifically training the machine learning parts of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show that these data-driven methods provide statistical means for uncertainty quantification of their predictions, which we use to detect and eliminate outliers and revise reference experimental data. Uncertainty quantification may also help in the systematic improvement of such data-driven methods.},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Enthalpies of formation and reaction are important thermodynamic properties that have a crucial impact on the outcome of chemical transformations. Here we implement the calculation of enthalpies of formation with a general-purpose ANI-1ccx neural network atomistic potential. We demonstrate on a wide range of benchmark sets that both ANI-1ccx and our other general-purpose data-driven method AIQM1 approach the coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably achieved without specifically training the machine learning parts of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show that these data-driven methods provide statistical means for uncertainty quantification of their predictions, which we use to detect and eliminate outliers and revise reference experimental data. Uncertainty quantification may also help in the systematic improvement of such data-driven methods.
Gokcan, Hatice; Isayev, Olexandr
Prediction of protein pKawith representation learning Journal Article
In: Chem. Sci., vol. 13, no. 8, pp. 2462–2474, 2022.
Abstract | Links | BibTeX | Tags: ANI, Drug Discovery
@article{Gokcan2022,
title = {Prediction of protein p\textit{K}_{a}with representation learning},
author = {Hatice Gokcan and Olexandr Isayev},
doi = {10.1039/d1sc05610g},
year = {2022},
date = {2022-02-23},
urldate = {2022-02-23},
journal = {Chem. Sci.},
volume = {13},
number = {8},
pages = {2462--2474},
publisher = {Royal Society of Chemistry (RSC)},
abstract = {\<jats:p\>We developed new empirical ML model for protein p\<jats:italic\>K\</jats:italic\>\<jats:sub\>a\</jats:sub\>prediction with MAEs below 0.5 for all amino acid types.\</jats:p\>},
keywords = {ANI, Drug Discovery},
pubstate = {published},
tppubtype = {article}
}
<jats:p>We developed new empirical ML model for protein p<jats:italic>K</jats:italic><jats:sub>a</jats:sub>prediction with MAEs below 0.5 for all amino acid types.</jats:p>
2021
Zheng, Peikun; Zubatyuk, Roman; Wu, Wei; Isayev, Olexandr; Dral, Pavlo O.
Artificial intelligence-enhanced quantum chemical method with broad applicability Journal Article
In: Nat Commun, vol. 12, pp. 7022 , 2021, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{Zheng2021,
title = {Artificial intelligence-enhanced quantum chemical method with broad applicability},
author = {Peikun Zheng and Roman Zubatyuk and Wei Wu and Olexandr Isayev and Pavlo O. Dral},
doi = {10.1038/s41467-021-27340-2},
issn = {2041-1723},
year = {2021},
date = {2021-12-15},
urldate = {2021-12-15},
journal = {Nat Commun},
volume = {12},
pages = {7022 },
publisher = {Springer Science and Business Media LLC},
abstract = {High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence\textendashquantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C\<jats:sub\>60\</jats:sub\>) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules\textemdashthe task difficult for both experiment and theory. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties.},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence–quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C<jats:sub>60</jats:sub>) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules—the task difficult for both experiment and theory. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties.
2020
Gao, Xiang; Ramezanghorbani, Farhad; Isayev, Olexandr; Smith, Justin S.; Roitberg, Adrian E.
TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials Journal Article
In: J. Chem. Inf. Model., vol. 60, no. 7, pp. 3408–3415, 2020.
Links | BibTeX | Tags: ANI, Machine learning potential
@article{Gao2020,
title = {TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials},
author = {Xiang Gao and Farhad Ramezanghorbani and Olexandr Isayev and Justin S. Smith and Adrian E. Roitberg},
doi = {10.1021/acs.jcim.0c00451},
year = {2020},
date = {2020-07-27},
urldate = {2020-07-27},
journal = {J. Chem. Inf. Model.},
volume = {60},
number = {7},
pages = {3408--3415},
publisher = {American Chemical Society (ACS)},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Devereux, Christian; Smith, Justin S.; Huddleston, Kate K.; Barros, Kipton; Zubatyuk, Roman; Isayev, Olexandr; Roitberg, Adrian E.
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Journal Article
In: J. Chem. Theory Comput., vol. 16, no. 7, pp. 4192–4202, 2020, ISSN: 1549-9626.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{Devereux2020,
title = {Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens},
author = {Christian Devereux and Justin S. Smith and Kate K. Huddleston and Kipton Barros and Roman Zubatyuk and Olexandr Isayev and Adrian E. Roitberg},
doi = {10.1021/acs.jctc.0c00121},
issn = {1549-9626},
year = {2020},
date = {2020-07-14},
urldate = {2020-07-14},
journal = {J. Chem. Theory Comput.},
volume = {16},
number = {7},
pages = {4192--4202},
publisher = {American Chemical Society (ACS)},
abstract = {Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼106 factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications.},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here, we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, and S) make up ∼90% of drug-like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and nonbonded interactions. ANI-2x is shown to accurately predict molecular energies compared to density functional theory with a ∼106 factor speedup and a negligible slowdown compared to ANI-1x and shows subchemical accuracy across most of the COMP6 benchmark. The resulting model is a valuable tool for drug development which can potentially replace both quantum calculations and classical force fields for a myriad of applications.
Smith, Justin S.; Zubatyuk, Roman; Nebgen, Benjamin; Lubbers, Nicholas; Barros, Kipton; Roitberg, Adrian E.; Isayev, Olexandr; Tretiak, Sergei
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules Journal Article
In: Sci Data, vol. 7, no. 1, 2020, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags: ANI, dataset, Machine learning potential
@article{Smith2020,
title = {The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules},
author = {Justin S. Smith and Roman Zubatyuk and Benjamin Nebgen and Nicholas Lubbers and Kipton Barros and Adrian E. Roitberg and Olexandr Isayev and Sergei Tretiak},
doi = {10.1038/s41597-020-0473-z},
issn = {2052-4463},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Sci Data},
volume = {7},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.},
keywords = {ANI, dataset, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.
2017
Smith, Justin S.; Isayev, Olexandr; Roitberg, Adrian E.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Journal Article
In: Chemical Science, iss. 8, pp. 3192-3203, 2017.
Abstract | Links | BibTeX | Tags: ANI, Machine learning potential
@article{Smith2017,
title = {ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost},
author = {Justin S. Smith and Olexandr Isayev and Adrian E. Roitberg },
url = {https://olexandrisayev.com/wp-content/uploads/2024/02/c6sc05720a.pdf},
doi = {10.1039/C6SC05720A},
year = {2017},
date = {2017-02-08},
urldate = {2017-02-08},
journal = {Chemical Science},
issue = {8},
pages = {3192-3203},
abstract = {Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.},
keywords = {ANI, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.