Book cover

  • © 2021

Basis Sets in Computational Chemistry

  • Eva Perlt 0

Department of Chemistry, University of California, Irvine, USA

You can also search for this editor in PubMed   Google Scholar

Discusses basis sets for different methods and problems

Includes a chapter on mathematical error analysis

Features contributions from leaders in the field

Part of the book series: Lecture Notes in Chemistry (LNC, volume 107)

9343 Accesses

20 Citations

  • Table of contents

About this book

Editors and affiliations, about the editor, bibliographic information.

  • Publish with us

Buying options

  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Other ways to access

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (8 chapters)

Front matter, an introduction and overview of basis sets for molecular and solid-state calculations.

  • Jeppe Olsen

Slater-Type Orbitals

  • Devashis Majumdar, Pabitra Narayan Samanta, Szczepan Roszak, Jerzy Leszczynski

Local Orbitals in Quantum Chemistry

  • Nadia Ben Amor, Stefano Evangelisti, Thierry Leininger, Dirk Andrae

An Introduction to Discretization Error Analysis for Computational Chemists

  • Eric Cancès

Basis Sets for Correlated Methods

  • Daniel Claudino, Rodney J. Bartlett

Gaussian Basis Sets for Solid State Calculations

Basis sets for heavy atoms.

  • Diego Fernando da Silva Paschoal, Mariana da Silva Gomes, Larissa Pereira Nogueira Machado, Hélio Ferreira Dos Santos

Adaptable Gaussian Bases for Quantum Dynamics of the Nuclei

  • Sophya Garashchuk

Back Matter

This book addresses the construction and application of the major types of basis sets for computational chemistry calculations. In addition to a general introduction, it includes mathematical basics and a discussion of errors arising from incomplete or inappropriate basis sets. The different chapters introduce local orbitals and orbital localization as well as Slater-type orbitals and review basis sets for special applications, such as those for correlated methods, solid-state calculations, heavy atoms and time-dependent adaptable Gaussian bases for quantum dynamics simulations. This detailed review of the purpose of basis sets, their design, applications, possible problems and available solutions provides graduate students and beginning researchers with information not easily obtained from the available textbooks and offers valuable supporting material for any quantum chemistry or computational chemistry course at the graduate and/or undergraduate level. This book is also useful as a guide for researchers who are new to computational chemistry but are willing to extend their research tools by applying such methods. 

  • quantum chemistry
  • wave function
  • basis set superposition error
  • electronic structure
  • density functional theory
  • slater-type orbital
  • discretization error analysis

Eva Perlt studied chemistry in Leipzig and received her Ph.D. in the group of Barbara Kirchner in 2011 working in the field of statistical thermodynamics as well as basis sets for ab initio molecular dynamics simulations. Since 2018, Eva Perlt has been a postdoctoral researcher in the group of Filipp U. Furche at the University of California, Irvine. Her research focus changed to the investigation of nuclear quantum effects and Beyond Born–Oppenheimer approaches. She deals with non-adiabatic molecular dynamics to investigate photochemical processes. Additionally, she is working on the development of nuclear wavefunction methods to treat light nuclei as quantum particles. In 2015, she was awarded the Sigrid Peyerimhoff prize for young scientists.

Book Title : Basis Sets in Computational Chemistry

Editors : Eva Perlt

Series Title : Lecture Notes in Chemistry

DOI : https://doi.org/10.1007/978-3-030-67262-1

Publisher : Springer Cham

eBook Packages : Chemistry and Materials Science , Chemistry and Material Science (R0)

Copyright Information : Springer Nature Switzerland AG 2021

Hardcover ISBN : 978-3-030-67261-4 Published: 07 May 2021

Softcover ISBN : 978-3-030-67264-5 Published: 07 May 2022

eBook ISBN : 978-3-030-67262-1 Published: 06 May 2021

Series ISSN : 0342-4901

Series E-ISSN : 2192-6603

Edition Number : 1

Number of Pages : VII, 255

Number of Illustrations : 22 b/w illustrations, 64 illustrations in colour

Topics : Theoretical and Computational Chemistry , Math. Applications in Chemistry , Physical Chemistry

Policies and ethics

  • Find a journal
  • Track your research

research paper on computational chemistry

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

  •  We're Hiring!
  •  Help Center

Computational Chemistry

  • Most Cited Papers
  • Most Downloaded Papers
  • Newest Papers
  • Save to Library
  • Last »
  • Theoretical Chemistry Follow Following
  • Chemistry Follow Following
  • Physical Chemistry Follow Following
  • Quantum Chemistry Follow Following
  • Molecular Dynamics Simulation Follow Following
  • Inorganic Chemistry Follow Following
  • Catalysis Follow Following
  • Organic Chemistry Follow Following
  • Medicinal Chemistry Follow Following
  • Spectroscopy Follow Following

Enter the email address you signed up with and we'll email you a reset link.

  • Academia.edu Publishing
  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 28 September 2020

Computation sparks chemical discovery

Nature Communications volume  11 , Article number:  4811 ( 2020 ) Cite this article

9177 Accesses

1 Citations

19 Altmetric

Metrics details

Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at Nature Communications are eager to continue our engagement in this exciting and rapidly evolving field.

Theoretical and computational modelling is ubiquitous in materials research. Modelling can significantly help to bridge the results of fundamental materials research to actual materials production by significantly reducing timescales. The computational chemistry approaches developed over the years have been an invaluable tool to provide deep insight into chemical processes beyond what can be directly measured experimentally. A new Collection [ https://www.nature.com/collections/ncomms-compchem ] showcases recent progress in developing these computational frameworks.

For many years, density functional theory (DFT) was considered the method of choice to study the electronic structure of molecules, materials and condensed systems, enabling an optimal trade-off between accuracy and computational cost. This balance could be achieved by including the complex many-body electron–electron interactions within a functional of the density, i.e. the exchange and correlation functional. During the 1980s and 1990s, thei key to the huge advances achieved by molecular simulations was to develop more and more accurate quantum-mechanical approximations in order to climb the so-called Jacob’s ladder, with each rung representing increasing levels of complexity and decreasing levels of approximation to the exact exchange and correlation functional. This led to the so-called chemical modelling revolution, as highlighted by Tkatchenko in his Comment entitled Machine learning for chemical discovery 1 .

Considering how the world has changed with the increasing availability of curated datasets containing reliable quantum-mechanical properties of molecules and materials, and how our ability to collect big data has greatly surpassed our capability to analyze it, a completely different strategy is to think about how seemingly unrelated data and properties may impact each other, studying the hidden interconnections between them. In this vein, an alternative approach to advance the predictive capability of computational approaches is to replace the physically motivated path by a data-driven search. This has given rise to big-data-driven science, which applies machine learning (ML) techniques to molecular and materials science. While ML approaches have been in use for decades for identifying correlations from big amounts of data, only recently has the computational community started to invest tremendously in programme infrastructures based on the synergetic collaboration between materials scientists, who have experimental and theoretical expertise, and computer scientists to develop ML methods aimed at discovering new molecules and materials. Under development are ML methodologies that combine electronic structure calculations and statistical analysis tools, which when fed with increasingly available molecular big data, can serve as alternatives to standard methods to explore the vast chemical space. In these ongoing efforts, the computational community currently faces theoretical and technical challenges.

Computational studies of chemical processes taking place over extended size and time scales must balance computational cost and accuracy: electronic structure methods are very accurate but computationally expensive, while atomistic models such as force fields—although computationally affordable—lack transferability to new systems.

In Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning , Smith et al. 2 discuss that an ideal solution to achieve the best of both approaches lies in developing a general purpose neural network potential that approaches CCSD(T) accuracy (coupled cluster considering single, double, and perturbative triple excitations), the gold standard in quantum chemistry, yet exhibits transferability over a broad chemical space. Most importantly for practical calculations, the resulting potential is an attractive alternative to DFT approaches and standard force fields: it is broadly applicable for conformational searches, molecular dynamics, and the calculation of reaction energies and is billions of times faster than CCSD(T) calculations.

Within traditional DFT modelling, seeking to increase the non-locality of the exchange and correlation functional in the effort to achieve more accurate approximations comes at a steep increase in computational cost, making related computational efforts impractical. A different approach in this area is to develop specialized ML functionals, whose overall accuracy does not significantly degrade when used outside their training scope.

Dick and Fernandez-Serra in Machine learning accurate exchange and correlation functionals of the electronic density tackle this problem by introducing a fully machine-learned functional that depends explicitly on the electronic density and implicitly on the atomic positions 3 . It approaches the accuracy of high level quantum chemistry methods at an affordable computational cost. Although these functionals were created for a specific dataset and hence are not universal, they exhibit promising transferability from the gas to condensed phase and from small to larger molecules within the same type of chemical bonding.

One common feature of machine learning approaches used in molecular simulations is that since the electronic properties are learned from quantum chemistry data, each individual model is typically limited to exploring these specific properties. Since all the physical and chemical features of a hypothetical compound can be derived by its ground-state electronic wavefunction, one way to circumvent this problem is to establish a direct link between ML and quantum chemistry with a ML model that predicts the ground-state wavefunction, as discussed by Schütt et al. in Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions 4 . The deep learning approach introduced by these authors provides full access to the electronic properties needed for practical calculations of reactive chemistry, such as charge populations, bond orders, and dipole and quadrupole moments, at a force-field-like efficiency. Moreover, the approach may enable property-driven chemical structure exploration, suggesting promise towards inverse-chemical design.

Although acknowledging the rapid evolution of computational techniques is exciting, this is not to suggest that traditional deep quantum chemistry expertise is obsolete: on the contrary, standard high-level theoretical approaches are still indispensable for solving fundamental problems in computational chemistry. A nice example is shown by Liu et al. 5 in The electronic structure of benzene from a tiling of the correlated 126-dimensional wavefunction. Using high-level correlated wavefunction theory, the authors revisit the electronic structure of benzene, which has been a test bed for competing theories throughout the years. In alternative to the traditional description of the electronic structure in terms of molecular orbital (MO) theory, the authors rely on a method to identify and visualise wavefunction tiles, known as dynamic Voronoi Metropolis sampling. The use of such high-level theory enables them to reveal the fundamental effect of electron correlation in benzene and show its manifestation in the preference for staggered Kekulé structures, whereas the interpretation of electronic structure in terms of MO theory ignores that the wavefunction is anti-symmetric upon interchange of like-spins.

ML algorithms and natural language processing approaches also offer new possibilities in optimizing and automating reaction procedures. On-demand synthesis of small drugs is of key interest in this area, where both the forward synthesis (given a set of reactants, predict the products) and the retrosynthesis (given a target, predict reactant and reagents) can strongly benefit from recent modelling advances. Reaction predictions are usually considered a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. The ultimate goal is to implement human-refined chemical recipe files to feed a robotic platform, which then execute the actual synthesis in an automated manner. A challenge here revolves around the need to extract chemical instructions from patents and the scientific literature, where they are reported in prose, and convert them to a machine-readable format. In Automated extraction of chemical synthesis actions from experimental procedures , Vaucher et al. 6 make a first important step towards implementing the automated execution of arbitrary reactions with robotic systems by developing a deep-learning model that performs the conversion of chemical instructions for organic synthesis reactions.

Although data-driven computational approaches clearly hold promise towards speeding up the discovery of new molecules and materials, at the moment current applications are only at the beginning of the exploration phase. The reliability of any ML approach depends on the availability of extensive datasets for model training, the bottleneck in cases where data is not abundant or difficult to generate. Along with the need for extensive curated data sets of microscopic and macroscopic molecular properties, future work should target the development of more transferable models with universal approximations that can treat local chemical bonding and non-local interactions on the same foot.

As an ultimate goal, the hope is to develop ML approaches that can not only provide predictive models but also interpretable models to stimulate the formation of novel scientific concepts and deeper understanding of a given research field, as Häse et al. suggest in their Perspective piece Designing and understanding light-harvesting devices with machine learning 7 .

We at Nature Communications are eager to continue our contribution to this exciting and fast-developing field. While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties. We strongly believe that a synergistic effort across disciplines—involving computational chemists, computer scientists, experimental chemists and material scientists—will play a crucial role for enhancing the rational design of new molecules and materials.

“While we acknowledge the importance of standard high-level computational frameworks, we recognize the tremendous potential of data-driven ML schemes towards accelerating the discovery of material systems with target properties.”

Tkatchenko, A. Machine learning for chemical discovery. Nat. Commun. 11 , 4125 (2020).

ADS   CAS   PubMed   PubMed Central   Google Scholar  

Smith, J. S. et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10 , 2903 (2019).

ADS   PubMed   PubMed Central   Google Scholar  

Dick, S. & Fernandez-Serra, M. Machine learning accurate exchange and correlation functionals of the electronic density. Nat. Commun. 11 , 3509 (2020).

Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R. & Maurer, R. J. Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nat. Commun. 10 , 5024 (2019).

Liu, Y., Kilby, P., Frankcombe, T. J. & Schmidt, T. W. The electronic structure of benzene from a tiling of the correlated 126-dimensional wavefunction. Nat. Commun. 11 , 1210 (2020).

Vaucher, A. C., Zipoli, F., Geluykens, J., Nair, V. H., Schwaller, P. & Laino, T. Automated extraction of chemical synthesis actions from experimental procedures. Nat. Commun. 11 , 3601 (2020).

Häse, F., Roch, L. M., Friederich, P. & Aspuru-Guzik, A. Designing and understanding light-harvesting devices with machine learning. Nat. Commun. 11 , 4587 (2020).

PubMed   PubMed Central   Google Scholar  

Download references

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Computation sparks chemical discovery. Nat Commun 11 , 4811 (2020). https://doi.org/10.1038/s41467-020-18651-x

Download citation

Published : 28 September 2020

DOI : https://doi.org/10.1038/s41467-020-18651-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

research paper on computational chemistry

research paper on computational chemistry

Physical Chemistry Chemical Physics

Computational investigation of single and multiple boron atom doped ws 2 monolayers for superior electrocatalytic reduction of nitrogen †.

ORCID logo

* Corresponding authors

a School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China E-mail: [email protected] Tel: +86-010-68914863

The efficient conversion of nitrogen into ammonia plays a significant role in our modern society. Therefore, the design and development of associated catalysts have become an area of major research interest. Nowadays, an increasing number of studies have been exploring single-atom or double-atom metal-free electrocatalysts for the N 2 reduction reaction, where regulating the precise number of catalyst atoms anchored on the substrate posed a real challenge. Herein, with density functional theory (DFT) simulations, this study investigated the activity of single and multiple B atom doped monolayer WS 2 catalysts and observed superior efficiencies for nitrogen fixation and reduction. Computational results reveal that these novel catalysts have excellent thermodynamic stability, suitable adsorption of N 2 , superior catalytic activity and high selectivity for the nitrogen reduction reaction. Notably, this study clearly illustrates that the steric hindrance arising from the adjacent atoms of catalytic sites can be an effective route for manipulating the catalytic performance, offering new insights for the synthesis of high efficiency catalysts. In summary, this series of novel boron doped monolayer WS 2 catalysts does not require precise control of the number of catalytic atoms on the substrate, making their preparation easier.

Graphical abstract: Computational investigation of single and multiple boron atom doped WS2 monolayers for superior electrocatalytic reduction of nitrogen

Supplementary files

  • Supplementary information PDF (982K)

Article information

Download citation, permissions.

research paper on computational chemistry

Computational investigation of single and multiple boron atom doped WS 2 monolayers for superior electrocatalytic reduction of nitrogen

Z. Yin, J. Cao, X. Li and N. Li, Phys. Chem. Chem. Phys. , 2024, Advance Article , DOI: 10.1039/D3CP05648A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page .

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page .

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements

IMAGES

  1. Free Download Introduction to Computational Chemistry (3rd Edition) By

    research paper on computational chemistry

  2. Lab 5 Computational Chemistry

    research paper on computational chemistry

  3. Journal of Computational Chemistry Template

    research paper on computational chemistry

  4. (PDF) Theoretical and computational chemistry in Spain

    research paper on computational chemistry

  5. (PDF) Computational Chemistry and Molecular Modelling Basics

    research paper on computational chemistry

  6. Journal of Computational Chemistry · OA.mg

    research paper on computational chemistry

VIDEO

  1. A LEVEL CHEMISTRY VIDEOS PLANNED FOR THIS WEEK AHEAD OF PAPER 2

  2. CC 5 Set -1 Question Paper

  3. A Computational Perspective on Consciousness (Paper Breakdown)

  4. 2022 A/l Chemistry Paper Analysing

  5. 02 Department of Computational chemistry

  6. Experimental study design

COMMENTS

  1. Computational chemistry

    Computational chemistry describes the use of computer modelling and simulation - including ab initio approaches based on quantum chemistry, and empirical approaches - to study the structures...

  2. Journal of Computational Chemistry

    The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Our scope encompasses all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, material, and theoretical. Announcements GDCh Awards 2020/DBG Awards 2020

  3. Combining Machine Learning and Computational Chemistry for Predictive

    Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems | Chemical Reviews C&EN

  4. Computation and Machine Learning for Chemistry

    In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods, applications to...

  5. Computational chemistry Home

    Here we assess recent computational research on the structural, electronic and spectroscopic properties of these complexes. From the themed collection: Computational chemistry The article was first published on 29 Mar 2018 RSC Adv., 2018,8, 12232-12259 https://doi.org/10.1039/C8RA00283E Download PDF Article HTML Review Article

  6. Computational and Theoretical Chemistry

    Computational and Theoretical Chemistry publishes high quality, original reports of significance in computational and theoretical chemistry including those that deal with problems of structure, properties, energetics, weak interactions, reaction mechanisms, catalysis, and reaction rates involving … View full aims & scope $3580

  7. Computational chemistry for all

    Computational chemistry for all Kaitlin McCardle & Jie Pan Nature Computational Science 2 , 134-136 ( 2022) Cite this article 2161 Accesses 2 Citations 33 Altmetric Metrics

  8. Journal of Computational Chemistry

    First Published: 15 February 2021. Computational cost of QM methods increases exponentially with system size. To dodge the steep computational cost of quantum chemical methods, we present "EE‐MIM (Electrostatically Embedded Molecules‐in‐Molecules: an incorporation of electrostatic embedding strategy into well‐established fragmentation ...

  9. Computational Chemistry: The Exciting Opportunities and the Boring

    Computational chemistry methods are now integrated elements in many research fields and are routine tools for non-experts. The plethora of different models, however, forms a bewildering jungle of choices, often resulting in practitioners defaulting to the tried and true.

  10. 215519 PDFs

    Mar 2024 Edgardo Jonathan Suarez-Dominguez Josue Francisco Perez-Sanchez Hugo Herrera-Pilotzi [...] Elena F. Izquierdo-Kulich Asphaltenes aggregation and waxes in petroleum were simulated...

  11. Theoretical and Computational Chemistry

    Explore the field of theoretical and computational chemistry across a suite of specialist journals, with world-leading research disseminated in the Journal of Chemical Theory and Computation and Journal of Chemical Information and Modeling. The Journal of Physical Chemistry family also shares new computational tools or methods that are of broad ...

  12. Computational Chemistry

    However, continuous challenges for computational chemistry are: (1) accounting for complex mathematics in a way to reduce the computational cost (memory, disk space, CPU time) efficiently even with modern computing power and (2) reliably predicting thermochemical properties within 1 kcal mol − 1 from well-established experimentally determined pr...

  13. Open-Source Machine Learning in Computational Chemistry

    Computational chemistry, Computational modeling, Machine learning, Optimization, Software Abstract The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms.

  14. Computational Chemistry

    Computational Chemistry serves as a complement to experimental chemistry where the tools are limited. Using computational programs to solve advanced problems is widely used in the design and analysis of for example new molecules, surfaces, drugs and materials. ... He has already published 260 research papers in peer-reviewed journals and he has ...

  15. WebMO: Web‐based computational chemistry calculations in education and

    WebMO uses a server-client architecture that installs on a single server or cluster computer and provides access to state-of-the-art computational chemistry programs from a standard web browser. The web interface provides a 3-D molecular editor, pre-defined calculations types, job submission and monitoring, visualization of results, and user ...

  16. Computational and Theoretical Chemistry

    Computational and Theoretical Chemistry. Supports open access. 3.4 CiteScore. 2.8 Impact Factor. Articles & Issues. About. Publish. Order journal. Menu. Articles & Issues. ... Research article Open access A computational investigation of twelve phenylurea herbicides including photoexcitation and structural relaxation. Feiling Vang, Varun V. Raj ...

  17. Journal of Computational Chemistry: Vol 42, No 7

    Pages: 516-521. First Published: 27 December 2020. The correlation between binding energy (BE) and electron density ρ(r) at bond critical point for 28 neutral hydrogen-bonded systems investigated by Emamian et al. in J. Comput. Chem ., 2019, 40, 2868 is reassessed with the local vibrational mode theory. Abstract.

  18. Basis Sets in Computational Chemistry

    This book addresses the construction and application of the major types of basis sets for computational chemistry calculations. In addition to a general introduction, it includes mathematical basics and a discussion of errors arising from incomplete or inappropriate basis sets. The different chapters introduce local orbitals and orbital ...

  19. Applications of computational chemistry, artificial ...

    Computer science and engineering, which has gained rapid advancement in recent years, has been widely applied in many other fundamental disciplines such as aquatic chemistry research. For example, computational chemistry, which uses first-principles or empirical methods, has been extensively applied to predict transformation behaviors of ...

  20. Perspective on the Current State-of-the-Art of Quantum Computing for

    Computational chemistry is an essential tool in the pharmaceutical industry. Quantum computing is a fast evolving technology that promises to completely shift the computational capabilities in many areas of chemical research by bringing into reach currently impossible calculations. This perspective illustrates the near-future applicability of quantum computation of molecules to pharmaceutical ...

  21. Computational Chemistry Research Papers

    Recent papers in Computational Chemistry Top Papers Most Cited Papers Most Downloaded Papers Newest Papers People Internal-to-Cartesian back transformation of molecular geometry steps using high-order geometric derivatives Download by Trygve Helgaker 2 Computational Chemistry , THEORETICAL AND COMPUTATIONAL CHEMISTRY

  22. Computational approach inspired advancements of solid-state

    The increasing demand for high-security, high-performance, and low-cost energy storage systems (EESs) driven by the adoption of renewable energy is gradually surpassing the capabilities of commercial lithium-ion batteries (LIBs). Solid-state electrolytes (SSEs), including inorganics, polymers, and composites, have

  23. A novel 2D VC4 as a promising Na-host material for Na-ion batteries

    The rapid growth of technologies has influenced our daily lives to build efficient energy storage systems for various electric vehicles and portable electronic devices. The abundant reserves, low cost, and low toxicity of sodium-ion batteries (SIBs) make them one of the most promising next-generation energy storage

  24. Computation sparks chemical discovery

    Computational chemistry methods with an optimal balance between predictive accuracy and computational cost hold major promise for accelerating the discovery of new molecules and materials. We at ...

  25. Computational Biology and Chemistry

    Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular… View full aims & scope $3050

  26. Theoretical & Computational Chemistry

    Benjamin G. Levine IACS Endowed Professor of Chemistry. Joint with the Institute for Advanced Computational Science. Theory and Simulation of Electronically Excited Molecules and Materials. Maria C. Nagan Professor of Practice. Computational studies of ribonucleic acid; molecular dynamics simulations, parameter development, and biomolecular ...

  27. Computational investigation of single and multiple boron atom doped WS2

    Computational results reveal that these novel catalysts have excellent thermodynamic stability, suitable adsorption of N 2, superior catalytic activity and high selectivity for the nitrogen reduction reaction. Notably, this study clearly illustrates that the steric hindrance arising from the adjacent atoms of catalytic sites can be an effective ...

  28. Water

    Therefore, this paper proposed a computational framework combing BIM and numerical simulation to calculate and analyze the large complex hydraulic radial steel structure. Firstly, the 3D model of the radial gate was established by MicroStation2020, then, the finite element model was output by using it.