Abstract: The disclosed embodiments, collectively referred to as the “Message Ordering Buffer” or “MOB”, relate to an improved messaging platform, or processing system, which may also be referred to as a message processing architecture or platform, which routes messages from a publisher to a subscriber ensuring related messages, e.g., ordered messages, are conveyed to a single recipient, e.g., processing thread, without unnecessarily committing resources of the architecture to that recipient or otherwise preventing message transmission to other recipients. The disclosed embodiments further include additional features which improve efficient and facilitate deployment in different application environments. The disclosed embodiments may be deployed as a message oriented middleware component directly installed, or accessed as a service, and accessed by publishers and subscribers, as described herein, so as to electronically exchange messages therebetween.

Abstract: Embodiments of the present invention provide, among other things, methods, apparatus, and systems for tuning a semiempirical process for predicting energy for different molecular configurations. In an example method, an energy value and an energy gradient are determined for each of a plurality of molecular configurations using an accurate method. A functional form of the semiempirical process is optimized using the determined energy values and energy gradients via multiobjective optimization. The functional form relates one or more parameters to energy values and energy gradients.

Abstract: Embodiments of the present invention provide, among other things, methods, apparatus, and systems for tuning a semiempirical process for predicting energy for different molecular configurations. In an example method, an energy value and an energy gradient are determined for each of a plurality of molecular configurations using an accurate method. A functional form of the semiempirical process is optimized using the determined energy values and energy gradients via multiobjective optimization. The functional form relates one or more parameters to energy values and energy gradients.