Towards chemically accurate QM/MM simulations on GPUs.

Computational chemistry simulations are extensively used to model natural phenomena. To maintain performance similar to molecular mechanics, but achieve comparable accuracy to quantum mechanical calculations, many researchers are using hybrid QM/MM methods. In this article we evaluate our GPU-accelerated ONIOM implementation by measurements on the crambin and HIV integrase proteins with different size QM model systems. We demonstrate that by using a larger QM region, a better energy accuracy can be achieved at the expense of simulation time. This trade-off is important to consider for the researcher running QM/MM calculations. Furthermore, we show that the ONIOM energy monotonically approaches the pure quantum mechanical energy of the whole system. The experiments are made feasible by utilizing the cutting-edge BrianQC quantum chemistry module for Hartree-Fock level SCF and our GPU-accelerated MMFF94 force field implementation for molecular mechanics calculations.

Robust Memristor Networks for Neuromorphic Computation Applications.

One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.

Calculation of Quantum Chemical Two-Electron Integrals by Applying Compiler Technology on GPU.

In this article, we present an effective approach to calculate quantum chemical two-electron integrals over basis sets consisting of Gaussian-type basis functions on graphical processing unit (GPU). Our framework generates several different variants called routes to the same integral problem with different integral algorithms (McMurchie-Davidson, Head-Gordon-Pople, and Rys) and precision. Each route is benchmarked on more GPU architectures, and with this data, a model is fitted to select the best available route for an integral task given a GPU architecture. Moreover, this approach supports the computation of high angular momentum orbitals up to g effectively on GPU, tested up to cc-pVQZ-sized basis sets. Rigorous analysis is shown regarding the effectiveness of our method. Molecule simulations with several basis sets are measured using NVIDIA GTX 1080 Ti, NVIDIA P100, and NVIDIA V100 cards.