Method development

Latest Research and Reviews

Research
17 January 2023 | Open Access

Electronic excited states in deep variational Monte Carlo

Deep neural networks can learn and represent nearly exact electronic ground states. Here, the authors advance this approach to excited states, achieving high accuracy across a range of atoms and molecules, opening up the possibility to model many excited-state processes.
- M. T. Entwistle
- , Z. Schätzle
- & F. Noé
Nature Communications 14, 274
Research | 21 December 2022

Large-scale chemical language representations capture molecular structure and properties

Large language models have recently emerged with extraordinary capabilities, and these methods can be applied to model other kinds of sequence, such as string representations of molecules. Ross and colleagues have created a transformer-based model, trained on a large dataset of molecules, which provides good results on property prediction tasks.
- Jerret Ross
- , Brian Belgodere
- & Payel Das
Nature Machine Intelligence 4, 1256-1264
Research
12 December 2022 | Open Access

The dominant nature of Herzberg–Teller terms in the photophysical description of naphthalene compared to anthracene and tetracene
- Anjay Manian
- & Salvy P. Russo
Scientific Reports 12, 21481
Reviews
26 November 2022 | Open Access

Graph neural networks for materials science and chemistry

Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.
- Patrick Reiser
- , Marlen Neubert
- & Pascal Friederich
Communications Materials 3, 93
Research
17 November 2022 | Open Access

Machine learning the Hohenberg-Kohn map for molecular excited states

Density functional theory provides a formal map from the electron density to all observables of interest of a many-body system; however, maps for electronic excited states are unknown. Here, the authors demonstrate a data-driven machine learning approach for constructing multistate functionals.
- Yuanming Bai
- , Leslie Vogt-Maranto
- & William J. Glover
Nature Communications 13, 7044
Research
09 November 2022 | Open Access

An integral method for determining the molecular composition of lignin and its application
- Qingzhi Ma
- & Xuejin Zhang
Scientific Reports 12, 19136

All Research & Reviews

News and Comment

Research Highlights | 23 January 2023

Large language model for molecular chemistry
- Jie Pan
Nature Computational Science 3, 5
Editorial | 26 September 2022

A computational perspective on the Nobel Prize

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

Nature Computational Science 2, 527-528
Comments & Opinion | 26 September 2022

Curiosity-driven method development

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.
- Kaitlin McCardle
Nature Computational Science 2, 542-544
Comments & Opinion | 26 September 2022

A collective effort for building DFT

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.
- Kaitlin McCardle
Nature Computational Science 2, 539-541
Comments & Opinion | 26 September 2022

Quantum mechanics guided by simplicity

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.
- Kaitlin McCardle
Nature Computational Science 2, 548-549
Comments & Opinion
07 September 2022 | Open Access

A further step towards the practical application of quantum computing in chemistry

Adiabatic state preparation (ASP) represents an efficient way of generating correlated wave functions on quantum computers for subsequent quantum simulation. Here, the author discusses recent work that numerically studied the performance of ASP on strongly correlated molecules and presented several approaches of improving the quality of prepared ground state wave functions.
- Libor Veis
Communications Chemistry 5, 108