N M Anoop Krishnan is an Associate Professor in the Department of Civil and Environmental Engineering at Indian Institute of Technology, Delhi with a joint appointment in the Yardi School of Artificial Intelligence.

Indra Priyadarsini is a Research Scientist at IBM Research – Tokyo, where she focuses on developing models and algorithms for foundation models in materials discovery.

Melodie Christensen serves as Director of Data-Rich Experimentation in Process Research & Development Enabling Technologies at Merck & Co., Inc., leading the development and deployment of AI-driven automation technologies that accelerate pharmaceutical process research.

All our cover articles and more are #openaccess and free to read on the Digital Discovery #RSCDigital#RSCDigital website: pubs.rsc.org/dd/issu...

TAS-AI is redefining autonomous neutron spectroscopy to accelerate quantum materials characterisation! ⚛️Framing spectroscopy as 3 tasks: detection, inference, and refinement ✨Find it here: doi.org/10.1039/d6dd... #automation #spectroscopy #agnosticdiscovery

💡Looking for a tool to calculate spectral overlap between molecular absorption spectra and reference light sources? Read about overlap-calculator, a practical tool for transparent spectral-compatibility analysis in data-driven optoelectronic materials research. 👉 doi.org/10.1039/d6dd...

Want to perform DFT calculations accurately and resource-effectively?🤔Researchers at STFC have developed an ML app to predict the appropriate k-point sampling in input files for single-point, bulk-phase DFT simulation with the Quantum Espresso DFT code. 🔗Read it here: doi.org/10.1039/D5DD...

📢New #RSCDigital article online now! Gradient-enhanced neural networks used as efficient surrogate models for parameter estimation, cutting computing costs by a factor of 200,000 to preserve sensitivity, accuracy and enable MBDoE frameworks with real-time automated platforms doi.org/10.1039/D5DD...

📢 Digital Discovery and RCE are sponsoring prizes at the 9th Machine Learning and AI in (Bio)Chemical Engineering Conference 2026! 🎉 Registration window closes 1 July 2026, so register now! 📍University of Cambridge, UK 📆 06 - 07 July 2026 🔗 www.mabc-cambridge.ai/ #RSCEng #RSCDigital #MABC2026

🌞Happy Friday - new article out now!🔗doi.org/10.1039/D6DD... Machine learning can identify patterns to make high-throughput predictions of the Michaelis constant. This work introduces a new approach, AS4Km, incorporating enzyme-substrate interface information by encoding the enzyme’s active site

On the inside Front Cover: ✨ Ke Wang and colleagues present an AI-assisted workflow validating LLMs for scientific instrument control in a reproducible and safety-oriented template designed to mitigate risk to fragile instrumentation. 👉 Find it here: doi.org/10.1039/D6DD...

On the Front Cover: ✨ Andrew Cooper and colleagues introduce "RobInHood", the robotic arm in a fume hood to automate chemical syntheses. Its abilities are tested against two materials research problems and a phthalimide synthesis. 🤖 👉 Read the full story: doi.org/10.1039/D6DD...

T-REX is roaming #RSCDigital! 🦖 Kevlishvili and Dorabawila introduce a canonical, geometry-aware language for transition-metal complexes, opening new paths for generative design and machine learning in inorganic chemistry. 👉Read it now: doi.org/10.1039/D6DD... #GenerativeDesign #TMcomplexes

Digital Discovery is pleased to be sponsoring the RSC-BMCS Hot Topics online meeting on AI in Drug Discovery! The meeting will run on 3 November 2026, from 1300-1700 UK time. Registration is now open, find out more and sign up here: www.rscbmcs.org/events/hotto...

Congratulations Jan Gerit Brandenburg and team, winners of DigitalDiscovery’s Outstanding Early Career Research Award for “BayBE" Read the paper here doi.org/10.1039/D5DD... Find more about the winners and their work on our blog blogs.rsc.org/dd/oec... #EarlyCareerAward #DrugDiscovery #bayesian #RSC

🚨New HOT article presenting optimade-maker: a lightweight toolkit for taking raw atomistic structure and property data and generating OPTIMADE (Open Databases Integration for Materials Design)-compliant APIs ✨Read the Advance Article now: doi.org/10.1039/D6DD... #opendata #openscience

We invite readers of Digital Discovery to learn more about the launch of RSC FIRST 2026: AI in Chemistry, a pioneering forum in intelligent chemistry, taking place in November. Explore research at the forefront of AI in chemistry and join the conversation shaping the future: www.rscfirst.org.cn

Are molecular graphs really necessary to represent molecules? Research from Klein, Rudenko, Bushmarinov and Pidko argues that powerful vector representations can be learned from 3D geometries alone, via contrastive training on conformers. 👉doi.org/10.1039/D6DD... #openaccess #MolecularGraphs

Can a simple pH signal reveal hidden dynamics of mRNA medicines production? Kis et al. present a real-time soft sensor using routine pH measurements and computational models to estimate RNA yield, NTP depletion, and ~40 IVT species for next-generation RNA medicine production👉doi.org/10.1039/D5DD...

📢We are attending this exciting conference by the AIchemy Hub and the Leverhulme Research Centre See you there? ✨Registration deadline: 12 June 2026 📆 29 - 30 June 2026 📍 Spine Building, Liverpool, UK 🔗 aichemy.ac.uk/event/... Alchemy

🚨New HOT article alert Introducing Peak-Activated Binary Attention (PABA), a robust framework for reaction center prediction. Trained on chemical language models, the supervised PABA approach attains an MCC of 0.73 and delivers high prediction accuracy👉doi.org/10.1039/D6DD... #opendata #openaccess

How reliable is that explainable AI method for molecular property prediction? 🤔 Khasanova and Tetko from Helmholtz Munich investigate inconsistencies in #propertyprediction and the accuracy of explainable AI models 👉doi.org/10.1039/D5DD... #explainableAI #XAI #opendata #OpenAccess

☀️Happy Friday! Looking for some weekend reading?👇 Harb et al. introduce a framework for liquid organic hydrogen carrier (LOHC) candidate discovery using LLM-guided molecular generation and ML-based property prediction 🔗doi.org/10.1039/D6DD... #MachineLearning #AcceleratedDiscovery #OpenAccess

Also in this issue, find the Editorial for the first annual Digital Discovery #RSCDigital#RSCDigital#RSCDigital Emerging Investigators collection and read the full collection online now! 👉doi.org/10.1039/D6DD... 🔗pubs.rsc.org/en/jour...

The back cover features the upcoming AIchemy and Liverhulme Research Centre Conference taking place 29 - 30 June 2026 in Liverpool, UK which Digital Discovery is proud to be supporting.

On the back cover, Carolin Müller and colleagues at FAU Erlangen-Nürnberg introduce their toolkit for the analysis, filtering, and visualization of surface hopping data - shnitsel-tools 👉Get the full story: doi.org/10.1039/D5DD...

The inside front cover comes from Jaeyune Ryu, Yousung Jung and their colleagues with a new Perspective on self-driving laboratories in Korea and the new era of autonomous discovery. 👉Read it here: doi.org/10.1039/D6DD... #SelfDrivingLab #AutonomousLab

On the front cover, Niaz Abdolrahim et al. introduce OPENXRD - a benchmarking framework for evaluating LLMs and multimodal LLMs question answering in X-ray diffraction. 👉Read the full story: doi.org/10.1039/D5DD... #LLM #OpenData

How accurately can a robot scoop? 🤖 doi.org/10.1039/D5DD... Pizzuto et al. presents a vision-guided powder weighing system based on deep reinforcement learning and an adaptive scooping method. This article is part of the our Emerging Investigators 2026 themed collection! #automation #robotlabs

📢 DD and PCCP are sponsoring poster prizes at the Southeastern Theoretical Chemistry Association meeting 2026 with two RSC Books vouchers to be won!🎉 Registration closing soon 📍Georgia State University, Atlanta, USA 📆 28-30 May 2026 🔗 sites.gsu.edu/setca2... #RSCPhys #SETCA2026 #RSCDigital