computational chemistry introduction and application basic concept. which is for new students to take concept of computational chemistry in very simple and easy way.
molecular mechanics and quantum mechnicsRAKESH JAGTAP
This document discusses molecular modeling methods, including quantum mechanics and molecular mechanics approaches. It describes the differences between quantum mechanics methods like ab initio, semi-empirical, and density functional theory in terms of system size, accuracy, and computational cost. Molecular mechanics is described as using empirical parameters and energy terms for bonding and non-bonding interactions. The document also discusses the hybrid QM/MM approach and applications of molecular modeling like structure-based drug design.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses using computational methods like molecular mechanics and molecular dynamics to minimize the energy of ligands bound to cyclin-dependent kinase 2 (CDK2) proteins. Two ligands (from 1H1S and 1OIT proteins) were selected and their minimum energy states calculated using different molecular mechanics algorithms and force fields. Fletcher-Reeves algorithm with MM+ force field produced the lowest energies of 34.32 kcal/mol for 1H1S ligand and 47.90 kcal/mol for 1OIT ligand. Molecular dynamics simulations at varying runtimes also minimized ligand energies further.
In the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result
Quantum Mechanics in Molecular modelingAkshay Kank
This slides gives you the information related to computer aided drug design and its application in drug discovery. Also you learn the Quantum mechanics related to the molecular mechanics. Theory related to molecular modeling and how the molecular modeling helps in drug discovery.
This document discusses the foundations of chemical kinetic modeling and reaction models. It outlines the key pillars of knowledge required, including general chemistry, thermodynamics, chemical kinetics, and quantum chemistry. It then describes the step-wise process for constructing detailed chemical kinetic models, including determining elementary reactions, estimating thermo-chemical data and rate coefficients, validating models experimentally, and applying the models to reactor scale-up and design. Reactor scale-up requires satisfying similarity parameters between small and large-scale systems. The goal is to develop accurate predictive models and design safe, commercial-scale chemical reactors.
This thesis examines calculations of binding free energies between cucurbit[7]uril and small ligands using end-state molecular modeling methods. The author conducted molecular dynamics simulations using AMBER with implicit solvent models to calculate binding free energies, which were then compared to experimental values from the SAMPL4 challenge. Various error metrics including R2 correlation, root mean squared error, and linear regression slope were used to evaluate the results. The author found some improvement over previous methods, suggesting further refinement of conformational sampling could enhance the accuracy of binding free energy predictions.
molecular mechanics and quantum mechnicsRAKESH JAGTAP
This document discusses molecular modeling methods, including quantum mechanics and molecular mechanics approaches. It describes the differences between quantum mechanics methods like ab initio, semi-empirical, and density functional theory in terms of system size, accuracy, and computational cost. Molecular mechanics is described as using empirical parameters and energy terms for bonding and non-bonding interactions. The document also discusses the hybrid QM/MM approach and applications of molecular modeling like structure-based drug design.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses using computational methods like molecular mechanics and molecular dynamics to minimize the energy of ligands bound to cyclin-dependent kinase 2 (CDK2) proteins. Two ligands (from 1H1S and 1OIT proteins) were selected and their minimum energy states calculated using different molecular mechanics algorithms and force fields. Fletcher-Reeves algorithm with MM+ force field produced the lowest energies of 34.32 kcal/mol for 1H1S ligand and 47.90 kcal/mol for 1OIT ligand. Molecular dynamics simulations at varying runtimes also minimized ligand energies further.
In the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result
Quantum Mechanics in Molecular modelingAkshay Kank
This slides gives you the information related to computer aided drug design and its application in drug discovery. Also you learn the Quantum mechanics related to the molecular mechanics. Theory related to molecular modeling and how the molecular modeling helps in drug discovery.
This document discusses the foundations of chemical kinetic modeling and reaction models. It outlines the key pillars of knowledge required, including general chemistry, thermodynamics, chemical kinetics, and quantum chemistry. It then describes the step-wise process for constructing detailed chemical kinetic models, including determining elementary reactions, estimating thermo-chemical data and rate coefficients, validating models experimentally, and applying the models to reactor scale-up and design. Reactor scale-up requires satisfying similarity parameters between small and large-scale systems. The goal is to develop accurate predictive models and design safe, commercial-scale chemical reactors.
This thesis examines calculations of binding free energies between cucurbit[7]uril and small ligands using end-state molecular modeling methods. The author conducted molecular dynamics simulations using AMBER with implicit solvent models to calculate binding free energies, which were then compared to experimental values from the SAMPL4 challenge. Various error metrics including R2 correlation, root mean squared error, and linear regression slope were used to evaluate the results. The author found some improvement over previous methods, suggesting further refinement of conformational sampling could enhance the accuracy of binding free energy predictions.
The document discusses software for quantum-pharmacological investigations and calculations of drug properties. It describes molecular modeling programs like HyperChem and ChemOffice that can calculate properties through molecular mechanics, semi-empirical, and ab initio methods. The document also discusses molecular descriptors that can be calculated, like partial charges, electrostatic potential, HOMO/LUMO orbitals, and docking algorithms to model protein-ligand interactions.
During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum-mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time-consuming QM calculations can then be applied, if necessary, to one or more of the 'best' structures to confirm the suggested solution.
1) The document compares the accuracy of empirical (HOSE code, neural network) and quantum-mechanical (QM) methods for predicting 13C NMR chemical shifts.
2) It analyzes 205 molecules where experimental and QM-calculated 13C shifts were published, and calculates shifts using HOSE code, neural network, and QM methods.
3) The mean absolute errors (MAE) were 1.58 ppm for HOSE code, 1.91 ppm for neural network, and 3.29 ppm for QM methods, indicating that the empirical methods provided more accurate predictions for this data set on average.
Computational chemistry uses theoretical chemistry calculations incorporated into computer programs to calculate molecular structures and properties. It can calculate properties such as structure, energy, charge distribution, and spectroscopic quantities using methods that range from highly accurate ab initio methods to less accurate semi-empirical and molecular mechanics methods. Computational chemistry allows medicinal chemists to use computer power to measure molecular geometry, electron density, energies, and more for applications such as conformational analysis, docking ligands in receptor sites, and comparing ligands.
- The physical-mathematical model of the actual natural or technological phenomena can include
different variables, the finite amount of which is defined by a researcher/conscious observer. The a priori
overall error inherent this model due its finiteness could be compared with the actual experimental measurement
error and should be useful in guiding future investigations. In this context, we propose a strategy relying on
thermodynamic theory of information processes, to estimate this error that cannot be done an arbitrarily small.
For the considered assumptions, the calculated error of the main researched variable, measured in conventional
field studies, should not be less than the error caused by the limited number of dimensional variables of the
physical-mathematical model. Examples of practical application of the proposed concept for spacecraft heating,
climate prediction, thermal energy storage and food freezing are discussed
Computational chemistry uses computers to simulate chemical systems and solve equations that model their properties. It is considered a third pillar of scientific investigation, along with theory and experiment. There are several computational methodologies including quantum mechanics, molecular mechanics, and molecular dynamics. Computational chemistry software can be used to optimize molecular geometries, map potential energy surfaces, perform conformational analyses, and calculate many other molecular properties and reaction kinetics. These methods have improved significantly with increasing computer power over the past few decades.
This document discusses molecular modeling, which uses computational tools to develop simplified models of molecular systems and chemical reactions in order to describe and predict their properties. It provides an overview of why molecular modeling is useful for chemistry, important characteristics of models, common molecular modeling tools and strategies, properties that can be modeled like molecular mechanics and quantum mechanics, molecular simulation methods like molecular dynamics, and applications such as generating chemical structures, visualizing molecular structures, and modeling drug-receptor interactions.
This document describes the development of process simulation software for the polymer industry using object-oriented design and CAPE-OPEN standards. It discusses refactoring an existing Fortran code for simulating methyl methacrylate polymerization into logical objects and developing a wrapper to allow integration with other equipment models according to CAPE-OPEN. The conclusions highlight benefits like reduced code redundancy and improved maintenance, while suggestions focus on optimizing numerical computations for speed without sacrificing flexibility.
This document describes updates made to the IEEC Database for Electronic Packaging Materials. The database contains a wide range of information on electronic packaging materials obtained through various chemical, mechanical, physical and thermal testing methods. The document provides an overview of the types of tests in the database and contact information for submitting new materials or discussing materials data. It also presents two new sections - a standard template for submitting materials and an online modeling tools section with an example creep model for predicting behavior of epoxy adhesives. Finally, it lists some new materials that have been added to the database.
Molecular modeling is a technique used to develop simplified models and simulations of molecular systems and chemical reactions. It allows scientists to investigate, interpret, and discover new phenomena that are difficult to study experimentally. Molecular modeling uses parameterized potential functions to describe molecular interactions and can be performed with specialized software. It has various applications including aiding drug design through direct and indirect methods, analyzing experimental data, and predicting molecular properties.
This document provides an overview of molecular modeling software and computational methods for modeling molecules, including molecular mechanics, semiempirical quantum mechanics modeling, and CaCHE modeling software. It discusses using molecular modeling to visualize molecules, match molecular structures, determine molecular properties using force fields, and correlate molecular properties with electronic structure calculations. Finally, it describes options for semiempirical quantum mechanics methods that can be used to compute properties like bond orders, dipole moments, and potential energy maps.
Coal-Fired Boiler Fault Prediction using Artificial Neural Networks IJECEIAES
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.
This document describes a study that estimated and correlated the partition coefficient (LogP) values of some benzoglycolamide ester compounds using experimental and computational methods. Eight benzoglycolamide ester compounds were synthesized and their LogP values were experimentally determined. Computational software programs HyperChem 5.0, CAChe Pro 5.0, and CLOGP 1.0.0 were used to calculate LogP values, which were then compared to the experimental values. LogP values calculated from HyperChem 5.0 showed the best correlation with experimental values, with an average R2 value of 0.465. The study demonstrated that quantitative structure-property relationship analyses can be used to predict physicochemical properties based on a molecule's
Call for Papers- Special Issue: Recent Trends, Innovations and Sustainable So...Christo Ananth
Christo Ananth, Special Issue on Recent Trends, “Innovations and Sustainable Solutions for Next Gen Renewable Energy Systems”, International Journal of Electrical and Electronic Engineering & Telecommunications, ISSN: 2319-2518 (Online), indexed in Scopus
A guide to molecular mechanics and quantum chemical calculationsSapna Jha
This document provides an introduction and guide to molecular mechanics and quantum chemical calculations. It is divided into four main sections. The first section defines various theoretical models used for quantum chemical and molecular mechanics calculations. The second section evaluates the performance of different theoretical models for predicting properties such as geometries, reaction energies, vibrational frequencies, and more. The third section discusses practical strategies for carrying out calculations. The fourth section presents case studies that illustrate how calculations can provide insight into chemistry. The guide aims to help chemists select appropriate computational methods for different applications.
Call for Papers- Special Issue: Recent Trends, Innovations and Sustainable So...Christo Ananth
Energy Systems Modelling is growing in relevance on providing insights and strategies to plan a carbon-neutral future. The implementation of an effective energy transition plan faces multiple challenges, spanning from the integration of the operations of different energy carriers and sectors to the consideration of multiple spatial and temporal resolutions. Demand-side management has to be applied to multi-carrier energy system models lacks; prosumers is explored only in a limited manner; In General, multi-scale modelling frameworks should be established and considered both in the dimensions of time, space, technology and energy carrier; long term energy system models tend to address uncertainty scarcely; there is a lack of studies modelling uncertainties related to emerging technologies and; modelling of energy consumer behaviour is one of the major aspect of future research. The increased pressure in decarbonizing the energy system has renewed the interest in energy system modelling, with several reviews trying to convey a comprehensive description of the utilized methodologies as well as providing new insights on how they can be used to answer new questions
This document provides an overview of computational chemistry and computational tools used in different fields. It defines computational chemistry and computational quantum chemistry. It discusses the concept of computational models, including ab initio quantum chemistry methods that do not include empirical parameters. It describes different computational models from most to least accurate, including ab initio, semi-empirical, and molecular mechanics methods. It provides details on molecular mechanics and how it estimates molecular energetics without using quantum mechanics. The document also discusses semi-empirical and ab initio methods and levels of theory. It states that high-speed supercomputers and suitable software programs are needed for computational chemistry. It provides examples of commonly used computational chemistry software.
The document discusses software for quantum-pharmacological investigations and calculations of drug properties. It describes molecular modeling programs like HyperChem and ChemOffice that can calculate properties through molecular mechanics, semi-empirical, and ab initio methods. The document also discusses molecular descriptors that can be calculated, like partial charges, electrostatic potential, HOMO/LUMO orbitals, and docking algorithms to model protein-ligand interactions.
During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum-mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time-consuming QM calculations can then be applied, if necessary, to one or more of the 'best' structures to confirm the suggested solution.
1) The document compares the accuracy of empirical (HOSE code, neural network) and quantum-mechanical (QM) methods for predicting 13C NMR chemical shifts.
2) It analyzes 205 molecules where experimental and QM-calculated 13C shifts were published, and calculates shifts using HOSE code, neural network, and QM methods.
3) The mean absolute errors (MAE) were 1.58 ppm for HOSE code, 1.91 ppm for neural network, and 3.29 ppm for QM methods, indicating that the empirical methods provided more accurate predictions for this data set on average.
Computational chemistry uses theoretical chemistry calculations incorporated into computer programs to calculate molecular structures and properties. It can calculate properties such as structure, energy, charge distribution, and spectroscopic quantities using methods that range from highly accurate ab initio methods to less accurate semi-empirical and molecular mechanics methods. Computational chemistry allows medicinal chemists to use computer power to measure molecular geometry, electron density, energies, and more for applications such as conformational analysis, docking ligands in receptor sites, and comparing ligands.
- The physical-mathematical model of the actual natural or technological phenomena can include
different variables, the finite amount of which is defined by a researcher/conscious observer. The a priori
overall error inherent this model due its finiteness could be compared with the actual experimental measurement
error and should be useful in guiding future investigations. In this context, we propose a strategy relying on
thermodynamic theory of information processes, to estimate this error that cannot be done an arbitrarily small.
For the considered assumptions, the calculated error of the main researched variable, measured in conventional
field studies, should not be less than the error caused by the limited number of dimensional variables of the
physical-mathematical model. Examples of practical application of the proposed concept for spacecraft heating,
climate prediction, thermal energy storage and food freezing are discussed
Computational chemistry uses computers to simulate chemical systems and solve equations that model their properties. It is considered a third pillar of scientific investigation, along with theory and experiment. There are several computational methodologies including quantum mechanics, molecular mechanics, and molecular dynamics. Computational chemistry software can be used to optimize molecular geometries, map potential energy surfaces, perform conformational analyses, and calculate many other molecular properties and reaction kinetics. These methods have improved significantly with increasing computer power over the past few decades.
This document discusses molecular modeling, which uses computational tools to develop simplified models of molecular systems and chemical reactions in order to describe and predict their properties. It provides an overview of why molecular modeling is useful for chemistry, important characteristics of models, common molecular modeling tools and strategies, properties that can be modeled like molecular mechanics and quantum mechanics, molecular simulation methods like molecular dynamics, and applications such as generating chemical structures, visualizing molecular structures, and modeling drug-receptor interactions.
This document describes the development of process simulation software for the polymer industry using object-oriented design and CAPE-OPEN standards. It discusses refactoring an existing Fortran code for simulating methyl methacrylate polymerization into logical objects and developing a wrapper to allow integration with other equipment models according to CAPE-OPEN. The conclusions highlight benefits like reduced code redundancy and improved maintenance, while suggestions focus on optimizing numerical computations for speed without sacrificing flexibility.
This document describes updates made to the IEEC Database for Electronic Packaging Materials. The database contains a wide range of information on electronic packaging materials obtained through various chemical, mechanical, physical and thermal testing methods. The document provides an overview of the types of tests in the database and contact information for submitting new materials or discussing materials data. It also presents two new sections - a standard template for submitting materials and an online modeling tools section with an example creep model for predicting behavior of epoxy adhesives. Finally, it lists some new materials that have been added to the database.
Molecular modeling is a technique used to develop simplified models and simulations of molecular systems and chemical reactions. It allows scientists to investigate, interpret, and discover new phenomena that are difficult to study experimentally. Molecular modeling uses parameterized potential functions to describe molecular interactions and can be performed with specialized software. It has various applications including aiding drug design through direct and indirect methods, analyzing experimental data, and predicting molecular properties.
This document provides an overview of molecular modeling software and computational methods for modeling molecules, including molecular mechanics, semiempirical quantum mechanics modeling, and CaCHE modeling software. It discusses using molecular modeling to visualize molecules, match molecular structures, determine molecular properties using force fields, and correlate molecular properties with electronic structure calculations. Finally, it describes options for semiempirical quantum mechanics methods that can be used to compute properties like bond orders, dipole moments, and potential energy maps.
Coal-Fired Boiler Fault Prediction using Artificial Neural Networks IJECEIAES
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.
This document describes a study that estimated and correlated the partition coefficient (LogP) values of some benzoglycolamide ester compounds using experimental and computational methods. Eight benzoglycolamide ester compounds were synthesized and their LogP values were experimentally determined. Computational software programs HyperChem 5.0, CAChe Pro 5.0, and CLOGP 1.0.0 were used to calculate LogP values, which were then compared to the experimental values. LogP values calculated from HyperChem 5.0 showed the best correlation with experimental values, with an average R2 value of 0.465. The study demonstrated that quantitative structure-property relationship analyses can be used to predict physicochemical properties based on a molecule's
Call for Papers- Special Issue: Recent Trends, Innovations and Sustainable So...Christo Ananth
Christo Ananth, Special Issue on Recent Trends, “Innovations and Sustainable Solutions for Next Gen Renewable Energy Systems”, International Journal of Electrical and Electronic Engineering & Telecommunications, ISSN: 2319-2518 (Online), indexed in Scopus
A guide to molecular mechanics and quantum chemical calculationsSapna Jha
This document provides an introduction and guide to molecular mechanics and quantum chemical calculations. It is divided into four main sections. The first section defines various theoretical models used for quantum chemical and molecular mechanics calculations. The second section evaluates the performance of different theoretical models for predicting properties such as geometries, reaction energies, vibrational frequencies, and more. The third section discusses practical strategies for carrying out calculations. The fourth section presents case studies that illustrate how calculations can provide insight into chemistry. The guide aims to help chemists select appropriate computational methods for different applications.
Call for Papers- Special Issue: Recent Trends, Innovations and Sustainable So...Christo Ananth
Energy Systems Modelling is growing in relevance on providing insights and strategies to plan a carbon-neutral future. The implementation of an effective energy transition plan faces multiple challenges, spanning from the integration of the operations of different energy carriers and sectors to the consideration of multiple spatial and temporal resolutions. Demand-side management has to be applied to multi-carrier energy system models lacks; prosumers is explored only in a limited manner; In General, multi-scale modelling frameworks should be established and considered both in the dimensions of time, space, technology and energy carrier; long term energy system models tend to address uncertainty scarcely; there is a lack of studies modelling uncertainties related to emerging technologies and; modelling of energy consumer behaviour is one of the major aspect of future research. The increased pressure in decarbonizing the energy system has renewed the interest in energy system modelling, with several reviews trying to convey a comprehensive description of the utilized methodologies as well as providing new insights on how they can be used to answer new questions
This document provides an overview of computational chemistry and computational tools used in different fields. It defines computational chemistry and computational quantum chemistry. It discusses the concept of computational models, including ab initio quantum chemistry methods that do not include empirical parameters. It describes different computational models from most to least accurate, including ab initio, semi-empirical, and molecular mechanics methods. It provides details on molecular mechanics and how it estimates molecular energetics without using quantum mechanics. The document also discusses semi-empirical and ab initio methods and levels of theory. It states that high-speed supercomputers and suitable software programs are needed for computational chemistry. It provides examples of commonly used computational chemistry software.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
2. Introduction of computational chemistry
Involves using high-powered computers to do
calculations that would normally take human
beings way too long to do on paper, or even for
the examination of molecules that are difficult to
observe in real life.
On a semi-basic level, we could calculate things
like entropy, enthalpy, specific heat capacity,
vibrational frequencies, ground-state energy, etc.
3. Define Computational Chemistry
Computational Chemistry is a branch of
chemistry that uses the products of
Theoretical chemistry that is translated into CP
to calculate molecular properties and its changes
Perform simulation to macromoleculer systems
4. Importance of computational chemistry
.
Computational chemistry software will strongly
advance research by providing insight in reactivity and
properties, and by predicting new molecules, materials
or solvent mixtures.
Especially by combining experimental with modeling
efforts, experimental costs and time can be reduced and
better performance achieved.
5. Applications of computational chemistry
Calculating the ground-state energy of many types of
molecules
Determining the ground-state electron geometry of a
molecule
Modeling the (3D) potential energy surface for many
types of molecules
Designing protein sequences and predicting protein
structure
Modeling the transition states of complicated reaction
mechanisms to better understand how the reaction works
7. Applications of computational chemistry
Various applications of computational chemistry is described as below :
Drug discovery in the the field of pharmaceutical industry
pictures through satellite
Medicines
Free energy
Calculation of rate of reaction
Molecular structures
8. Conti…..
• Mountain of data
• Productive process
• Bulk properties like entropy ,enthapy etc
• Drug designing through computer modeling
• Removal of mercury from from flue gas
9. Future aspects of computational chemistry
• When experimentalists have ideas, they first come to
ask computer.
• Computational chemistry is a bridge between theory
and experiment. It used to give experiments reasonable
explanations by elucidating the mechanism of
chemical transformations.
• In recent years, it's becoming a powerful tool in
designing new catalysts, materials, and enzymes, en
route to numerous experimental possibilities.
10. Conclusion
Therefore, the main goal of computational
chemistry is to consistently calculate molecular
properties in as efficient and quick,
yet accurate a way as possible.
11. Any
Question??
“Quote of the day’’
Success is not final; failure is not
fatal: It is the courage to continue
that counts."