In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low-precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to rigorously compensate for numerical inaccuracies due to low-accuracy arithmetic operations, yet still obtaining exact expectation values using a properly modified Langevin-type equation.

Predicting the chemical stability of yet-to-be-discovered materials is an important aspect of the discovery and development of virtual materials. The conventional approach for computing the enthalpy of formation based on ab initio methods is time consuming and computationally demanding. In this regard, alternative machine learning approaches are proposed to predict the formation energies of different classes of materials with decent accuracy. In this paper, one such machine learning approach, a novel two-step method that predicts the formation energy of ternary compounds, is presented. In the first step, with a classifier, we determine the accuracy of heuristically calculated formation energies in order to increase the size of the training dataset for the second step. The second step is a regression model that predicts the formation energy of the ternary compounds. The first step leads to at least a 100% increase in the size of the dataset with respect to the data available in the Materials Project database. The results from the regression model match those from the existing state-of-the-art prediction models. In addition, we propose a slightly modified version of the Adam optimizer, namely centered Adam, and report the results from testing the centered Adam optimizer.