An end-to-end machine learning workflow for the development of potential energy surfaces at the DLPNO-CCSD(T)/aug-cc-pVTZ level is described and applied to hydroxide-water complexes with two to five water molecules. This workflow utilizes a two-part machine learning process, achieving both accuracy and efficiency by combining a molecular orbital-based machine learning (MOB-ML) model with a GPU-accelerated equivariant graph neural network (EGNN) model. For both machine learning models trained in this workflow, the training data is obtained through the combination of small diffusion Monte Carlo (DMC) simulations initiated at each of the optimized structures of interest for a given system size. A farthest point sampling algorithm based on a principal component analysis is employed to generate a diverse set of geometries that spans configuration space, including transition states. By transforming Cartesian coordinates into a graph-based representation, the EGNN model eliminates additional preprocessing of molecular configurations, provides an unbiased treatment of multiple isomers, and allows for isomerization processes among the isomers. An error analysis is performed on the final EGNN models, comparing the potential energies predicted for optimized structures, transition states, and structures that are obtained from snapshots of the ground state wave function sampled by large-scale DMC simulations to those evaluated at the DLPNO-CCSD(T)/aug-ccpVTZ level of theory and basis. Finally, production-run DMC simulations are performed using the validated potentials for both the OH(H 2 O) 2-5 and OD(D 2 O) 2-5 systems, and an analysis of the relative anharmonic zero-point energies is performed. It is found that deuteration does not change the relative energy ordering of the isomers of the OH(H 2 O) 3-5 complexes. Evidence of proton delocalization is identified in the prism isomer of OH(H 2 O) 5 in which a hydrogen atom in one of the solvating water molecules is found to be, on average, equidistant from the oxygen atoms in the hydroxide ion and the hydrogen-bonded water molecule. This delocalization is found to be retained with full deuteration of this complex.
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 citations
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.