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Article ; Online: Teaching a neural network to attach and detach electrons from molecules.

Zubatyuk, Roman / Smith, Justin S / Nebgen, Benjamin T / Tretiak, Sergei / Isayev, Olexandr

2021 Volume 12, Issue 1, Page(s) 4870

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 ... ...

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. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2-3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.
Language	English
Publishing date	2021-08-11
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-24904-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial intelligence-enhanced quantum chemical method with broad applicability.

Zheng, Peikun / Zubatyuk, Roman / Wu, Wei / Isayev, Olexandr / Dral, Pavlo O

Nature communications

2021 Volume 12, Issue 1, Page(s) 7022

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 ... ...

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-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
Language	English
Publishing date	2021-12-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-27340-2
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Article: Crystal structure of 4-tert-butyl-2-{2-[N-(3,3-dimethyl-2-oxobut-yl)-N-iso-propyl-carbamo-yl]phen-yl}-1-isopropyl-1H-imidazol-3-ium perchlorate.

Hordiyenko, Olga V / Zubatyuk, Roman I

Acta crystallographica. Section E, Crystallographic communications

2015 Volume 71, Issue Pt 2, Page(s) 223–225

Abstract: In the title salt, C26H40N3O2 (+)·ClO4 (-), the positive charge of the organic cation is delocalized between the two N atoms of the imidazole ring. The C N bond distances are 1.338 (2) and 1.327 (3) Å. The substituents on the benzene ring are rotated ... ...

Abstract	In the title salt, C26H40N3O2 (+)·ClO4 (-), the positive charge of the organic cation is delocalized between the two N atoms of the imidazole ring. The C N bond distances are 1.338 (2) and 1.327 (3) Å. The substituents on the benzene ring are rotated almost orthogonal with respect to this ring due to the presence of the bulky isopropyl substituents. The dihedral angle between the benzene and imidazole rings is 75.15 (12)°. Three of the O atoms of the anion are disordered over two sets of sites due to rotation around one of the O-Cl bonds. The ratio of the refined occupancies is 0.591 (14):0.409 (14). In the crystal, the cation and perchlorate anion are bound by an N-H⋯O hydrogen bond. In addition, the cation-anion pairs are linked into layers parallel to (001) by multiple weak C-H⋯O hydrogen bonds.
Language	English
Publishing date	2015-01-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2041947-8
ISSN	2056-9890 ; 1600-5368
ISSN	2056-9890 ; 1600-5368
DOI	10.1107/S2056989015001486
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Article ; Online: Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network.

Zubatyuk, Roman / Smith, Justin S / Leszczynski, Jerzy / Isayev, Olexandr

Science advances

2019 Volume 5, Issue 8, Page(s) eaav6490

Abstract: Atomic and molecular properties could be evaluated from the fundamental Schrodinger's equation and therefore represent different modalities of the same quantum phenomena. Here, we present AIMNet, a modular and chemically inspired deep neural network ... ...

Abstract	Atomic and molecular properties could be evaluated from the fundamental Schrodinger's equation and therefore represent different modalities of the same quantum phenomena. Here, we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in the computational cost. With AIMNet, we show a new dimension of transferability: the ability to learn new targets using multimodal information from previous training. The model can learn implicit solvation energy (SMD method) using only a fraction of the original training data and an archive median absolute deviation error of 1.1 kcal/mol compared to experimental solvation free energies in the MNSol database.
Language	English
Publishing date	2019-08-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.aav6490
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Article ; Online: Synergy of semiempirical models and machine learning in computational chemistry.

Fedik, Nikita / Nebgen, Benjamin / Lubbers, Nicholas / Barros, Kipton / Kulichenko, Maksim / Li, Ying Wai / Zubatyuk, Roman / Messerly, Richard / Isayev, Olexandr / Tretiak, Sergei

The Journal of chemical physics

2023 Volume 159, Issue 11

Abstract: Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, ...

Abstract	Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.
Language	English
Publishing date	2023-09-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	3113-6
ISSN	1089-7690 ; 0021-9606
ISSN (online)	1089-7690
ISSN	0021-9606
DOI	10.1063/5.0151833
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Article ; Online: Extending machine learning beyond interatomic potentials for predicting molecular properties.

Fedik, Nikita / Zubatyuk, Roman / Kulichenko, Maksim / Lubbers, Nicholas / Smith, Justin S / Nebgen, Benjamin / Messerly, Richard / Li, Ying Wai / Boldyrev, Alexander I / Barros, Kipton / Isayev, Olexandr / Tretiak, Sergei

Nature reviews. Chemistry

2022 Volume 6, Issue 9, Page(s) 653–672

Abstract: Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its ... ...

Abstract	Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.
Language	English
Publishing date	2022-08-25
Publishing country	England
Document type	Journal Article ; Review
ISSN	2397-3358
ISSN (online)	2397-3358
DOI	10.1038/s41570-022-00416-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.

Devereux, Christian / Smith, Justin S / Huddleston, Kate K / Barros, Kipton / Zubatyuk, Roman / Isayev, Olexandr / Roitberg, Adrian E

Journal of chemical theory and computation

2020 Volume 16, Issue 7, Page(s) 4192–4202

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 ... ...

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 ∼10
MeSH term(s)	Deep Learning ; Density Functional Theory ; Halogens/chemistry ; Molecular Dynamics Simulation ; Sulfur/chemistry ; Thermodynamics
Chemical Substances	Halogens ; Sulfur (70FD1KFU70)
Language	English
Publishing date	2020-06-29
Publishing country	United States
Document type	Journal Article
ISSN	1549-9626
ISSN (online)	1549-9626
DOI	10.1021/acs.jctc.0c00121
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Article ; Online: The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules.

Smith, Justin S / Zubatyuk, Roman / Nebgen, Benjamin / Lubbers, Nicholas / Barros, Kipton / Roitberg, Adrian E / Isayev, Olexandr / Tretiak, Sergei

Scientific data

2020 Volume 7, Issue 1, Page(s) 134

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 ... ...

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.
Language	English
Publishing date	2020-05-01
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-020-0473-z
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Article: Effect of Microenvironment on the Geometrical Structure of d(A)₅ d(T)₅ and d(G)₅ d(C)₅ DNA Mini-Helixes and the Dickerson Dodecamer: A Density Functional Theory Study

Gorb, Leonid / Pekh, Anatolii / Nyporko, Alexey / Ilchenko, Mykola / Golius, Anastasiia / Zubatiuk, Tetiana / Zubatyuk, Roman / Dubey, Igor / Hovorun, Dmytro M / Leszczynski, Jerzy

Journal of physical chemistry. 2020 Sept. 25, v. 124, no. 42

2020

Abstract: We report a comprehensive quantum-chemical study on d(A)₅·d(T)₅ and d(G)₅·d(C)₅ DNA mini-helixes and the Dickerson dodecamer d[CGCGAATTCGCG]. The research was performed to model the evolution of the spatial structure of d(A)₅·d(T)₅ and d(G)₅ d(C)₅ DNA ... ...

Abstract	We report a comprehensive quantum-chemical study on d(A)₅·d(T)₅ and d(G)₅·d(C)₅ DNA mini-helixes and the Dickerson dodecamer d[CGCGAATTCGCG]. The research was performed to model the evolution of the spatial structure of d(A)₅·d(T)₅ and d(G)₅ d(C)₅ DNA mini-helixes all the way from vacuum to water bulk. The influence of external factors such as the presence of counterions and the extent of hydration was included. Also, for comparison, limited calculations have been carried out on the Dickerson dodecamer. The study has been performed at the density functional theory level using B97D3 and ωB97XD exchange–correlation functionals augmented by the Def2SVP basis set. We found that the (dA)₅·(dT)₅ anion when placed in vacuum forms a DNA duplex, which possesses an intermediate form between a helix and a ladder. The presence of compensating Na⁺ counterions or explicit microhydration of minor and major grooves stabilizes a DNA mini-helix of B-shape. Factors such as water bulk play a minor role. Somewhat different behavior has been found in the case of the (dG)₅·(dC)₅ duplex. In this case, we observe the formation of B-type mini-helixes even for the (dG)₅·(dC)₅ anion placed in vacuum. This is due to an additional stabilization originated from the appearance of an extra hydrogen bond, compared to an AT base pair. To assess whether the obtained results are transferable to different sizes of mini-helixes, similar calculations have been performed for the duplex formed by the Dickerson dodecamer which contains a total of 12 dG·dC and dA·dT base pairs. It has been found that in vacuum, analogous to the d(A)₅·d(T)₅ duplex, this system possesses a shape which is also quite close to a ladder. However, the presence of factors such as hydration restores the B-type geometry. Also, our results completely in line with the results of electrospray-ionization experiments suggest that uncompensated by counterions the DNA backbone preserves the duplex geometry in vacuum. We present arguments that this state is kinetically unstable.
Keywords	DNA ; density functional theory ; evolution ; geometry ; hydrogen bonding
Language	English
Dates of publication	2020-0925
Size	p. 9343-9353.
Publishing place	American Chemical Society
Document type	Article
Note	NAL-AP-2-clean
ISSN	1520-5207
DOI	10.1021/acs.jpcb.0c06154
Database	NAL-Catalogue (AGRICOLA)

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Article: trans-Chloridobis-(ethane-1,2-di-amine-κ(2) N,N')(thio-cyanato-κN)cobalt(III) diammine-tetra-kis-(thio-cyanato-κN)cromate(III).

Rusanova, Julia A / Semenaka, Valentyna V / Zubatyuk, Roman I

Acta crystallographica. Section E, Structure reports online

2014 Volume 70, Issue Pt 3, Page(s) m110–1

Abstract: The title ionic complex [CoCl(NCS)(C2H8N2)2][Cr(NCS)4(NH3)2], which crystallizes as a non-merohedral twin, is build up of a complex cation [CoCl(NCS)(en)2](+) (en is ethane-1,2-di-amine) and the Reinecke's salt anion [Cr(NCS)4(NH3)2](-) as complex ... ...

Abstract	The title ionic complex [CoCl(NCS)(C2H8N2)2][Cr(NCS)4(NH3)2], which crystallizes as a non-merohedral twin, is build up of a complex cation [CoCl(NCS)(en)2](+) (en is ethane-1,2-di-amine) and the Reinecke's salt anion [Cr(NCS)4(NH3)2](-) as complex counter-ion. A network of N-H⋯S and N-H⋯Cl hydrogen bonds, as well as short S⋯S contacts [3.538 (2) and 3.489 (3) Å], between the NCS groups of the complex anions link the mol-ecules into a three-dimentional supra-molecular network. Intensity statistic indicated twinning by non-mero-hedry with refined weighs of twin components are 0.5662:0.4338.
Language	English
Publishing date	2014-02-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2041947-8
ISSN	1600-5368
ISSN	1600-5368
DOI	10.1107/S1600536814003869
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