Method development articles from across Nature Portfolio

The first step in predicting material properties is the generation of a plausible crystal structure. Here, the authors introduce a large language model that can achieve this task given only the chemical composition of the material.

Quantum computing offers a promising approach to solving electronic-structure problems, but a quantitative description of chemical systems while minimizing computing resources is an essential challenge. Here, the authors provide a shortcut towards chemically accurate quantum computations by approaching the complete-basis-set limit through coupling the density-based basis-set corrections approach, applied to any given variational ansatz, to an on-the-fly crafting of basis sets specifically adapted to a given system and user-defined qubit budget.

Structure relaxation is an iterative process particularly demanding in large-scale material discovery campaigns. Here, the authors realize a deep generative model able to relax material structures in a single step while estimating its accuracy.

Predictive machine learning models, while powerful, are often seen as black boxes. Here, the authors introduce a thermodynamics-inspired approach for generating rationale behind their explanations across diverse domains based on the proposed concept of interpretation entropy.

The contributions of the computational science community to Nobel Prizes in chemistry and physics take center stage in this month’s Focus issue.

Dr Martin Head-Gordon, Distinguished Professor of Chemistry at the University of California, Berkeley, talks with Nature Computational Science about his research on electronic structure theory, quantum chemistry software development, applications in renewable energy, as well as his time working with John Pople, who was recognized by the 1998 Nobel Prize in Chemistry.

Dr Lu Sham, Distinguished Professor Emeritus of Physics at the University of California, San Diego, talks with Nature Computational Science about his current research, the density functional theory (DFT) work that was recognized by the 1998 Nobel Prize in Chemistry — awarded to his co-author Dr Walter Kohn — and where he thinks the field is heading.

Dr Arieh Warshel, distinguished professor of chemistry at the University of Southern California and 2013 Nobel laureate in chemistry, discusses with Nature Computational Science past and current research, his Nobel Prize, and the benefits and challenges of using computational modeling in his work.