41 research outputs found
Fine Tuning Classical and Quantum Molecular Dynamics using a Generalized Langevin Equation
Generalized Langevin Equation (GLE) thermostats have been used very
effectively as a tool to manipulate and optimize the sampling of thermodynamic
ensembles and the associated static properties. Here we show that a similar,
exquisite level of control can be achieved for the dynamical properties
computed from thermostatted trajectories. By developing quantitative measures
of the disturbance induced by the GLE to the Hamiltonian dynamics of a harmonic
oscillator, we show that these analytical results accurately predict the
behavior of strongly anharmonic systems. We also show that it is possible to
correct, to a significant extent, the effects of the GLE term onto the
corresponding microcanonical dynamics, which puts on more solid grounds the use
of non-equilibrium Langevin dynamics to approximate quantum nuclear effects and
could help improve the prediction of dynamical quantities from techniques that
use a Langevin term to stabilize dynamics. Finally we address the use of
thermostats in the context of approximate path-integral-based models of quantum
nuclear dynamics. We demonstrate that a custom-tailored GLE can alleviate some
of the artifacts associated with these techniques, improving the quality of
results for the modelling of vibrational dynamics of molecules, liquids and
solids
Sampling Free Energy Surfaces as Slices by Combining Umbrella Sampling and Metadynamics
Metadynamics (MTD) is a very powerful technique to sample high-dimensional
free energy landscapes, and due to its self-guiding property, the method has
been successful in studying complex reactions and conformational changes. MTD
sampling is based on filling the free energy basins by biasing potentials and
thus for cases with flat, broad and unbound free energy wells, the
computational time to sample them becomes very large. To alleviate this
problem, we combine the standard Umbrella Sampling (US) technique with MTD to
sample orthogonal collective variables (CVs) in a simultaneous way. Within this
scheme, we construct the equilibrium distribution of CVs from biased
distributions obtained from independent MTD simulations with umbrella
potentials. Reweighting is carried out by a procedure that combines US
reweighting and Tiwary-Parrinello MTD reweighting within the Weighted Histogram
Analysis Method (WHAM). The approach is ideal for a controlled sampling of a CV
in a MTD simulation, making it computationally efficient in sampling flat,
broad and unbound free energy surfaces. This technique also allows for a
distributed sampling of a high-dimensional free energy surface, further
increasing the computational efficiency in sampling. We demonstrate the
application of this technique in sampling high-dimensional surface for various
chemical reactions using ab initio and QM/MM hybrid molecular dynamics
simulations. Further, in order to carry out MTD bias reweighting for computing
forward reaction barriers in ab initio or QM/MM simulations, we propose a
computationally affordable approach that does not require recrossing
trajectories
The importance of nuclear quantum effects for NMR crystallography
The resolving power of solid-state nuclear magnetic resonance (NMR)
crystallography depends heavily on the accuracy of the computational prediction
of NMR chemical shieldings of candidate structures, which are usually taken to
be local minima in the potential energy surface. To test the limits of this
approximation, we perform a systematic study of the role of finite-temperature
and quantum nuclear fluctuations on H, C, and N chemical
shieldings in molecular crystals -- considering the paradigmatic examples of
the different polymorphs of benzene, glycine, and succinic acid. We find the
effect of quantum fluctuations to be comparable in size to the typical errors
of predictions of chemical shieldings for static nuclei with respect to
experimental measurements, and to improve the match between experiments and
theoretical predictions, translating to more reliable assignment of the NMR
spectra to the correct candidate structure. Thanks to the use of integrated
machine-learning models trained on both first-principles configurational
energies and chemical shieldings, the accurate sampling of thermal and quantum
fluctuations of the structures can be achieved at an affordable cost, setting a
new standard for the calculations that underlie solid-state structural
determination by NMR
Quantum dynamics using path integral coarse-graining
Vibrational spectra of condensed and gas-phase systems containing light
nuclei are influenced by their quantum-mechanical behaviour. The quantum
dynamics of light nuclei can be approximated by the imaginary time path
integral (PI) formulation, but still at a large computational cost that
increases sharply with decreasing temperature. By leveraging advances in
machine-learned coarse-graining, we develop a PI method with the reduced
computational cost of a classical simulation. We also propose a simple
temperature elevation scheme to significantly attenuate the artefacts of
standard PI approaches and also eliminate the unfavourable temperature scaling
of the computational cost.We illustrate the approach, by calculating
vibrational spectra using standard models of water molecules and bulk water,
demonstrating significant computational savings and dramatically improved
accuracy compared to more expensive reference approaches. We believe that our
simple, efficient and accurate method could enable routine calculations of
vibrational spectra including nuclear quantum effects for a wide range of
molecular systems.Comment: 9 pages; 4 figure
Quantum dynamics using path integral coarse-graining
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum dynamics are possible thanks to the imaginary time path-integral (PI) formulation of quantum statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artifacts of standard PI approaches as well as eliminate the unfavorable temperature scaling of the computational cost. We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. Our simple, efficient, and accurate method has prospects for routine calculations of vibrational spectra for a wide range of molecular systems - with an explicit treatment of the quantum nature of nuclei
Hands-On Projects and Exercises to Strengthen Understanding of Basic Computer Engineering Concepts
The Introduction to Computer Engineering course at the University of Missouri-Rolla provides a thorough understanding of basic digital logic analysis and design. The course covers: digital numbering systems, Boolean algebra, function minimization using Karnaugh maps (K-maps), memory elements, and sequential logic design. Students\u27 grades are determined by their performance on homework assignments, quizzes, and in-class examinations. A laboratory course (optional for all but EE and CpE majors) supplements the lecture by providing experiments that include analysis and design using Mentor Graphics and FPGAs. While the laboratory is a very useful supplement to the lecture, almost half the students taking the lecture are not required to take the laboratory and there is not sufficient time in the laboratory schedule to introduce significant design elements. In Fall 2004, hands-on group projects, for all students, were introduced to the lecture course. The goal was for students to develop a more practical understanding and appreciation of hardware design and to improve motivation. Two projects were introduced that involve design of simple digital systems (based on practical applications), design optimization, and physical realization of the system using logic gates and/or memory elements. Two surveys, conducted during the semester, show the benefit of hands-on projects in gaining experience on basic digital hardware design
MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules
Classical empirical force fields have dominated biomolecular simulation for
over 50 years. Although widely used in drug discovery, crystal structure
prediction, and biomolecular dynamics, they generally lack the accuracy and
transferability required for predictive modelling. In this paper, we introduce
MACE-OFF23, a transferable force field for organic molecules created using
state-of-the-art machine learning technology and first-principles reference
data computed with a high level of quantum mechanical theory. MACE-OFF23
demonstrates the remarkable capabilities of local, short-range models by
accurately predicting a wide variety of gas and condensed phase properties of
molecular systems. It produces accurate, easy-to-converge dihedral torsion
scans of unseen molecules, as well as reliable descriptions of molecular
crystals and liquids, including quantum nuclear effects. We further demonstrate
the capabilities of MACE-OFF23 by determining free energy surfaces in explicit
solvent, as well as the folding dynamics of peptides. Finally, we simulate a
fully solvated small protein, observing accurate secondary structure and
vibrational spectrum. These developments enable first-principles simulations of
molecular systems for the broader chemistry community at high accuracy and low
computational cost
i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the BornâOppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives