entos: A Quantum Molecular Simulation Package

Manby, Frederick R.; Miller, Thomas F.; Bygrave, Peter J.; Ding, Fazhu; Dresselhaus, Thomas; Batista-Romero, Fidel; Buccheri, Alexander; Bungey, Callum; Lee, Sebastian; Meli, Rocco; Miyamoto, Kaito; Steinmann, Casper; Tsuchiya, Takashi; Welborn, Matthew; Wiles, Timothy A.; Williams, Zack

doi:10.26434/chemrxiv.7762646.v2

Cited by 26 publications

(15 citation statements)

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“…( H ) Molecular orbitals and electron density of atorvastatin. Visualization of molecular orbitals and the electron density obtained using semiempirical GFN1-xTB method ( 34 , 35 ) (used at https://envision.entos.ai ). ( I ) DNA binding protein assembly.…”

Section: Resultsmentioning

confidence: 99%

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Sehnal

Bittrich

Deshpande

et al. 2021

Nucleic Acids Research

671

533

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Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.

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Section: Resultsmentioning

confidence: 99%

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Sehnal

Bittrich

Deshpande

et al. 2021

Nucleic Acids Research

671

533

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“…3). A recent initiative toward an integration of ML is the ENTOS quantum chemistry package and ENTOS AI 152 .…”

Section: Please Cite This Article As Doi:101063/50047760mentioning

confidence: 99%

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr

Gastegger

Schütt³

et al. 2021

The Journal of Chemical Physics

147

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Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of highdimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties -be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.

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“…The features for all structures were generated with the EN-TOS QCORE 42 package. The feature generation is based on a HF calculation applying a cc-pVTZ 43 basis for the elements H, C, N, O, S, and Cl.…”

Section: Computational Detailsmentioning

confidence: 99%

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

Husch

Sun

Cheng

et al. 2021

The Journal of Chemical Physics

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Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet's theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different data sets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T data set (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this data set with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a data set comprised of thirteen heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone-backbone interaction data set to an accuracy of 0.28 kcal/mol.

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entos: A Quantum Molecular Simulation Package

Abstract: We describe the a new molecular simulation package that is designed for ab initio molecular dynamics simulations of molecular and condensed-phase chemical reactions and other<br>processes, with particular focus on mean-field and quantum embedding methods for electronic structure.<br>

Cited by 26 publications

References 1 publication

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Perspective on integrating machine learning into computational chemistry and materials science

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

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