computational-chemistry

Congratulations to Matt Sigman on being elected to the American Academy of Arts and Sciences!  Dr. Sigman's work focuses on a "machine‑learning–driven approach that predicts chemical reactions with far fewer experiments" …

Understanding Li-ion transport through the solid electrolyte interphase (SEI) is essential for improving the stability of lithium-metal batteries, as nonuniform ionic transport through the inorganic SEI can promote spatially localized Li deposition and dendrite formation. In this work, we develop a deep-learning-assisted framework to model Li-ion transport across the inorganic SEI by combining de…

Artificial intelligence is helping scientists discover new medicines, create advanced materials, and now even design better batteries. Researchers at the University of Chicago have developed a new AI system called “ElectrolyteGPT” that can create complete battery electrolyte formulas, potentially speeding up the search for longer-lasting and higher-performing batteries. The research, published in…

AI delivers breathtaking benchmark results in molecular design and reaction prediction. But when confronted with genuinely new chemical spaces or cross-laboratory conditions, performance drops off a cliff. We must ask: Are our models learning chemical principles, or just memorizing datasets?

Atomis, a Japanese startup advised by 2025 Nobel laureate Susumu Kitagawa, is bringing Metal Organic Frameworks (MOFs) to applications such as CO2 capture, refrigerant recycling and next-generation deodorization and coatings. One tool they are using to accomplish this is an AI-powered simulation platform from Matlantis.  MOFs have struggled to move beyond the lab due to… The post How Atomis is us…

Costas Maranas, Robert V. & Gloria H. Waltemeyer Chair and Donald B. Broughton Professor of Chemical Engineering at Penn State, is developing computer models and algorithms to help scientists better understand, analyze and redesign biological organisms, including plants. In this Q&A, Maranas discussed how recent breakthroughs in modeling, artificial intelligence and systems biology are accelerati…

Awarded for contributions to the field of digital chemistry and the chemical applications of artificial intelligence, machine learning and automation. Chemical Science is delighted to announce the winner of our 2026 Lectureship, Professor Alán Aspuru-Guzik! The Chemical Science Lectureship celebrates leading, independent researchers at all career stages who have made exceptional discoveries and i…

By combining electronic-structure calculations with non-adiabatic and Born–Oppenheimer molecular dynamics simulations, we uncover the molecular mechanisms governing the photoinduced chemistry of aza-diarylethene photoswitches and explain their distinct reaction outcomes.

My very first post. I am excited to be here. I retired from the software industry years ago and now work from a small back office in Oklahoma City. Most people think retirement means stepping away from building things. I saw it differently. So, about fifteen months ago, I started a project that eventually became QuantumCURE Pro™. ( https://citizenscientist.org/ ) The goal was ambitious but simple…

Nature Communications, Published online: 30 May 2026; doi:10.1038/s41467-026-73643-7 Optical pooled screening links genetic perturbations to cellular phenotypes but lacks standardized computational analysis tools. Here, authors present Brieflow, an integrated pipeline for end-to-end analysis of optical pooled screening data with automated biological interpretation.

A research team led by Associate Professor Noriyuki Kurita from the Department of Computer Science and Engineering at Toyohashi University of Technology and by Associate Professor Pornpan Pungpo from Ubon Ratchathani University in Thailand has proposed a novel therapeutic agent for tuberculosis, using high-precision molecular simulation techniques.

Machine learning has become an increasingly powerful tool for many aspects of society and everyday life.  The application of machine learning to the scientific community, specifically the design of materials at an electronic structure level, is still a rapidly evolving branch of research.  Reliable development and production of these machine learning applications is of great importance if physics…

Do we need quantum computers to fully understand complex chemical reactions? A new result, decades in the making, shows the surprising power of ordinary “classical” machines. The post Key Chemistry Question Answered, No Quantum Computer Required first appeared on Quanta Magazine

Nature Communications, Published online: 28 May 2026; doi:10.1038/s41467-026-73566-3 The authors present ByteFF-Pol, a force field trained solely on quantum data to accurately predict liquid and electrolyte properties. It bridges the gap between microscopic calculations and materials design without experimental calibration.

The present work deals with the theoretical estimation of ion-pair binding energies and the energetic properties of four ion pairs formed by combining the 1-butyl-2,4-dinitro-3-methyl imidazolium ion with nitrate (I), perchlorate(II), dinitramide (III), or 3,5-dinitro-1,2,4-triazolate (IV) anions.

Argonne National Laboratory has been awarded $2.77 million by ARPA-E to speed the discovery of industrial catalysts using AI, automation and rapid testing through the CATALCHEM-E program.