computational-chemistry

[cs.LG] Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalysis to combustion […] The post ReactionAtlas: Ab Origine Exploration Of Chemical Reaction Networks With Machine Learning appeared first on Astrobiology .

Scientific Reports, Published online: 05 September 2026; doi:10.1038/s41598-026-68990-w A fuzzy graph-based Kulli–Basava descriptor for isomer discrimination and physicochemical property prediction

[astro-ph.EP] Observations increasingly reveal the coupled radiative, chemical, and dynamical processes that shape exoplanet atmospheres. Interpreting these atmospheres requires models that can capture this complexity. However, multidimensional models remain fundamentally […] The post Accelerating Chemical Kinetics For Exoplanet Atmospheres Using Neural Networks appeared first on Astrobiology .

While most believe artificial intelligence is changing science, researchers at the University of Notre Dame are exploring the reverse.

[NiFe] hydrogenases reversibly catalyze hydrogen oxidation and proton reduction at a Ni–Fe active site coordinated by four cysteine (Cys) residues in two CXXC motifs within the catalytic large subunit. A subset of these enzymes contains selenocysteine (Sec) in place of Cys in the C-terminal CXXC motif, forming [NiFeSe] hydrogenases with enhanced oxygen tolerance and hydrogen production activity. …

Develop and benchmark quantum algorithms for solving Chemical Reaction Networks (CRNs), focusing on annealing-based optimisation methods and graph-based encodings for neutral atom quantum computers. Conduct numerical simulations of quantum and hybrid quantum-classical approaches, and contribute to publications and coll...

Stefano Polla joined QuSoft April 1, 2026, to lead research developing quantum algorithms for chemistry, bridging computational chemistry & quantum simulation.

From reducing administrative workload to navigating ‘alien intelligence,’ this month’s column takes a practical look at integrating LLMs and LWMs in the lab

Ordinary Portland cement has high energy and CO₂ footprint, motivating the use of geopolymer concrete as a lower-carbon structural binder. However, geopolymer mix design still relies on slow trial-and-error that does not achieve target compressive and flexural strengths. Prior studies used machine learning to predict geopolymer strength, but mostly with few input variables, compressive-strength-o…

Previously confined to simplified models or computationally intensive approximations, accurate simulation of resonance Raman spectra for complex molecules is now achievable. A new technique employing Hagedorn wavepackets offers a practical *ab initio* solution validated against established methods in two dimensions before being applied successfully to anthracene with its 66-dimensional harmonic p…

Nature Communications, Published online: 01 September 2026; doi:10.1038/s41467-026-77117-8 The authors use machine-learning molecular dynamics simulations to map the hydrogen-helium immiscibility boundary under gas-giant interior conditions, constraining models of planetary structure and evolution.

Scientific Reports, Published online: 31 August 2026; doi:10.1038/s41598-026-60916-w Structural insights and computational stability analysis of thiophene-derived inhibitors targeting human 5-lipoxygenase

Scientists know the machine learning models can make predictions based on the vast reams of data it is trained on, but can it take it a step further and massively scale up testing out those predictions? Researchers at WashU are testing that idea for chemical compound and polymer discovery.

By partitioning complex molecular systems, a new hybrid approach combines the strengths of classical and quantum computation to overcome longstanding limitations in predicting electronic behaviour. This DFT-Quantum Embedding (QDFT) framework treats chemically relevant portions with a quantum solver, while classical HPC handles the remainder, offering a pathway beyond the approximations inherent i…

Nature Communications, Published online: 27 August 2026; doi:10.1038/s41467-026-77263-z A theory of Cartesian tensors gives rise to CarNet, a unified machine learning framework that predicts the properties of molecules and materials with leading accuracy, from interatomic potential energies to complex, high-rank elastic tensors.