# Tag Info

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Electronic Structure Theory Much of the behavior we observe from molecules/materials arises from electronic interactions. These interactions are fundamentally quantum mechanical as are most of the approaches used to model them. To study electronic properties of a system, we typically solve some approximation of the electronic time in/dependent Schrodinger ...

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One mistake you already described is using virtualization. Nothing is more reliable than running your code directly on your hardware, without intermediaries (even when the virtualization software and Windows are evolving each day). It will be better to have a dual-boot system if you really need Windows than virtualization. To avoid mistakes, you need to ...

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"Are there critical mistakes to avoid when creating a matter modeling workstation (32-128 cores)?" 32-128 is quite a big range since at 128 you'd be limited to non-mainstream chips like the Ampere and as far as I know, the most cores in an Intel processor apart from the Xeon Phi line (which was discontinued in 2020 and wasn't a conventional ...

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Monte Carlo In this case there is no one answer, and perhaps no individual best suited to write the full list. I can contribute few words about Monte Carlo methods. What is Monte Carlo? Monte Carlo (MC) is a name that refers to a broad range of computational techniques that rely on random numbers. MC is very broadly applicable anywhere you need to do a high-...

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I would suggest to stay away from niche solutions and start with one mainstream machine. The most direct reason is that you get much more computing power for the same money if you buy mass-produced hardware. A more indirect reason is that everything you buy next year will be 20% cheaper or faster, your choice, cumulative for each year. If you want optimal ...

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I assume when you are asking about mesoscopic modeling you are looking for a scale higher than molecular dynamics and quantum mechanical simulations and a scale smaller than macroscopic finite element or finite volume modeling that typically mechanical engineers do for designing for example aircraft, reactors, etc. In this regime of mesoscopic modeling, ...

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Force fields These calculations are based on interatomic potentials and lattice energy minimization. As an example, lets take the DREIDING force field. This force field uses general force constants, and the parameters are defined for all possible combinations of atoms (J. Phys. Chem. 1990, 94, 8897-8909). The total potential energy, $U_T$, of an arbitrary ...

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There is a long history of correlation between ab initio calculations for nuclear physics and ab initio calculations for quantum chemistry/materials. Take coupled cluster for example, remembering that a lot of people used to say "CCSD(T) is the gold standard in quantum chemistry": The method was initially developed by Fritz Coester and Hermann ...

9

To invest in new hardware, just to have it crippled by Intel MKL afterwards. This was object of a question I asked here previously. In matter modeling, it's usual for people to compile software themselves, instead of relying in precompiled binaries, so it can be tuned to the particular processor model in their machine, for example, taking advantage of more ...

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Buying an Intel CPU would be one, for sure. (unless you are buying an Ice Lake server) I think the biggest decision to make is to build a CPU box or a GPU box. For atom-centered Gaussian basis set quantum chemistry you probably going to want a CPU box, with lots of RAM and a very fast and a high endurance SSD.

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I don't think this is something that you can model accurately in an ab initio manner, as there is no good method for predicting the thermal conductivity of amorphous materials yet, let alone materials that are both amorphous and have macroscopic inhomogeneities. IMHO the way to go is to collect experimental thermal conductivity data on soil samples with ...

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Here is a list of molecular/chemistry/materials modeling software packages designed to run on classical computers: VASP, MOLCAS, CFOUR, GAUSSIAN, LAMMPS, CP2K, DIRAC, Turbomole, MOLPRO, ORCA, MRCC, ADF, PySCF, PSI4, DALTON, QuantumEspresso, QChem, CASTEP, CPMD, ABINT, DFTB+, ABAQUS, OpenBabel, Amber, CHARMM, GAMESS, Gromacs, NAMD, Omnia, OpenCalphad, OpenMM, ...

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Calphad techniques should also be of interest. Calphad is based on thermodynamic models of the Gibbs energy starting from describing the pure elements in gas, liquid and different crystalline structures (also meta-stable ones) and more or less complex models describing solutions with model parameters fitted to experimental data and (for the last 20 years or ...

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Tensorflow and FireWorks are different kinds of software. The "workflow management" features of Tensorflow are primarily designed to manage running Tensorflow itself, while FireWorks and related tools are designed to manage running other software. Tensorflow is a library for machine learning. It can be used to develop, train and evaluate machine ...

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Our Method We have created a machine learning model for estimating the thermal conductivity of any soil, which we have made publicly available at soilconductivity.com. Our model is significantly more accurate than any other existing method that we are aware of (MAE of around 0.08 W/mK), besides direct physical measurement of the soil, of course. (here is a ...

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One critical mistake is to invest heavily in GPUs before doing a cost/benefit analysis. A single GPU may cost $2000, which can already get you 2-3 good compute nodes. Many codes (e.g. https://gaussian.com/g16/gpus.pdf) advertise speedups that are underwhelming: the GPU speedup should be at least an order of magnitude, when the CPU code is already close to ... 5 p-DMRG Perturbatively corrected DMRG by Sheng Guo, Zhendong Li, and Garnet Chan (in 2018). Motivation: DMRG scales poorly with respect to the number of basis functions. the above paper says that DMRG's cost is$\mathcal{O}\left(M^3D^3\right)$for$M$basis functions and a bond dimension of$D$, and that$D$often has to scale as$\mathcal{O}\left(M\right)\$, ...

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