CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

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CHARMM: the biomolecular simulation program.

Preface Acknowledgements Notation Part I. Overview and Background Topics: 1. Introduction 2. Overview 3. Theoretical background 4. Periodic solids and electron bands 5. Uniform electron gas and simple metals Part II. Density Functional Theory: 6. Density functional theory: foundations 7. The Kohn-Sham ansatz 8. Functionals for exchange and correlation 9. Solving the Kohn-Sham equations Part III. Important Preliminaries on Atoms: 10. Electronic structure of atoms 11. Pseudopotentials Part IV. Determination of Electronic Structure, The Three Basic Methods: 12. Plane waves and grids: basics 13. Plane waves and grids: full calculations 14. Localized orbitals: tight binding 15. Localized orbitals: full calculations 16. Augmented functions: APW, KKR, MTO 17. Augmented functions: linear methods Part V. Predicting Properties of Matter from Electronic Structure - Recent Developments: 18. Quantum molecular dynamics (QMD) 19. Response functions: photons, magnons ... 20. Excitation spectra and optical properties 21. Wannier functions 22. Polarization, localization and Berry's phases 23. Locality and linear scaling O (N) methods 24. Where to find more Appendixes References Index.

Electronic Structure: Basic Theory and Practical Methods

First-principles calculations have evolved from mere aids in explaining and supporting experiments to powerful tools for predicting new materials and their properties. In the first part of this review we describe the state-of-the-art computational methodology for calculating the structure and energetics of point defects and impurities in semiconductors. We will pay particular attention to computational aspects which are unique to defects or impurities, such as how to deal with charge states and how to describe and interpret transition levels. In the second part of the review we will illustrate these capabilities with examples for defects and impurities in nitride semiconductors. Point defects have traditionally been considered to play a major role in wide-band-gap semiconductors, and first-principles calculations have been particularly helpful in elucidating the issues. Specifically, calculations have shown that the unintentional n-type conductivity that has often been observed in as-grown GaN cannot be a...

First-principles calculations for defects and impurities: Applications to III-nitrides

The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

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The Atomic Simulation Environment - A Python library for working with atoms

Combined quantum-mechanics/molecular-mechanics (QM/MM) approaches have become the method of choice for modeling reactions in biomolecular systems. Quantum-mechanical (QM) methods are required for describing chemical reactions and other electronic processes, such as charge transfer or electronic excitation. However, QM methods are restricted to systems of up to a few hundred atoms. However, the size and conformational complexity of biopolymers calls for methods capable of treating up to several 100,000 atoms and allowing for simulations over time scales of tens of nanoseconds. This is achieved by highly efficient, force-field-based molecular mechanics (MM) methods. Thus to model large biomolecules the logical approach is to combine the two techniques and to use a QM method for the chemically active region (e.g., substrates and co-factors in an enzymatic reaction) and an MM treatment for the surroundings (e.g., protein and solvent). The resulting schemes are commonly referred to as combined or hybrid QM/MM methods. They enable the modeling of reactive biomolecular systems at a reasonable computational effort while providing the necessary accuracy.

QM/MM Methods for Biomolecular Systems

We present a theoretical study of the relative formation energies for possible models of the reconstruction found under Ga-rich growth conditions at the surface. The energetically most favourable model has fourfold-coordinated Ga atoms on the surface, exhibiting metallic bonding character. This structure differs as regards the electron-counting rule (ECR) from the well accepted models for the (100), (110) and (111) surfaces of GaAs. Our results suggest that it is still possible to explain the stability of the metallic surface with a rule similar to the ECR.

Structural models for the reconstruction of the surface and their relative stabilities

Self-consistent-charge density-functional tight-binding (SCC-DFTB) calculations have been performed to survey the potential-energy surface for a single interstitial carbon atom introduced into GaAs. The results provided a possible model for the diffusion of carbon through GaAs with an activation energy of less than 1 eV. The carbon atom moves via split-interstitial and bond-centered configurations. Subsequently, the energetics of the model reaction were refined using a fully self-consistent density-functional method, AIMPRO. These calculations were found to be in good agreement with the more approximate SCC-DFTB results. Experimental studies have also found an activation energy of ∼1 eV for carbon migration in heavily doped material.

https://epubs.surrey.ac.uk/741010/2/latham-PRB-60-15117.pdf

Density-functional calculations of carbon diffusion in GaAs

There is a need to understand the degradation mechanism which results in the loss of substitutional CAs shallow acceptors in AlAs and GaAs containing high concentrations (≳1020 cm−1) of carbon. The activation energy for the migration of interstitial carbon atoms, Ci, in AlAs and GaAs is calculated using a local-density functional-based method, AIMPRO, to be ≲1 eV. This model is consistent with a ‘kick-out’ mechanism being responsible for the observed loss of CAs, and formation of dicarbon defects. Three local minima separated by only a few tenths of an eV are encountered by a diffusing carbon atom along its path through the crystal. These have (C–As)As split interstitial, bond-centred M–C–As, and (C–M)M split interstitial structures (M=Al, Ga). Interstitial dicarbon defects, (C–C)i, or substitutional dicarbon defects, (C–C)As, are produced depending on whether a Ci meets a (C–M)M split interstitial or a CAs. These possess Raman-active C–C vibrational modes near to that for a free dicarbon molecule, C2.

Mechanism for dicarbon defect formation in AlAs and GaAs

We present a theoretical study of atomic structures, electrical properties and formation energies for a variety of possible reconstructions with 1×1 and 2×2 periodicity of the GaN(0001) and % MathType!MTEF!2!1!+- % feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqr1ngB % PrgifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaqFr0x % c9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qqQ8fr % Fve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaadaqada % qaaiaaicdacaaIWaGaaGimamaanaaabaGaaGymaaaaaiaawIcacaGL % Paaaaaa!3C2E!
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 surfaces. We find that during MBE growth in the (0001) direction 2×2 structures become stable under N rich growth conditions while Ga rich environment should yield structures with 1×1 periodicity. Considering MBE growth on % MathType!MTEF!2!1!+- % feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqr1ngB % PrgifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaqFr0x % c9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qqQ8fr % Fve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaadaqada % qaaiaaicdacaaIWaGaaGimamaanaaabaGaaGymaaaaaiaawIcacaGL % Paaaaaa!3C2E!
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 surfaces, among the investigated structures only those with 1×1 periodicity are predicted to be stable. During MOCVD growth, where H terminated surfaces may occur, only structures with lx1 periodicity are found to be stable for both growth directions.

M. Haugk

Papers

Structural models for the reconstruction of the surface and their relative stabilities

Density-functional calculations of carbon diffusion in GaAs

Mechanism for dicarbon defect formation in AlAs and GaAs

Theory of Ga, N and H terminated GaN (0001)/(0001) surfaces

Irradiation experiment revisited - Stability and positron lifetime of large vacancy clusters in silicon