2025 New's Items

A framework to evaluate machine learning crystal stability predictions

Janosh Riebesell 
Rhys E. A. Goodall
Philipp Benner
Yuan Chiang
Bowen Deng
Gerbrand Ceder
Mark Asta
Alpha A. Lee
Anubhav Jain
Kristin A. Persson 
2025

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python...

Crystal structure prediction with host-guided inpainting generation and foundation potentials

Peichen Zhong
Xinzhe Dai
Bowen Deng
Gerbrand Ceder
Kristin A. Persson
2025

Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the crystal host-guided generation (CHGGen) framework to address this challenge through conditional generation using an inpainting method, which optimizes a fraction of atomic positions within a predefined and symmetrized host structure to improve the success rate for symmetric structure generation. By integrating inpainting structure generation with a foundation potential for structure optimization, we demonstrate the method...

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Luca Rettenberger
Nathan J. Szymanski
Andrea Giunto
Olympia Dartsi
Anubhav Jain
Gerbrand Ceder
Veit Hagenmeyer
Markus Reischl
2025

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge...

Cross-functional transferability in foundation machine learning interatomic potentials

Xu Huang
Bowen Deng
Peichen Zhong
Aaron D. Kaplan
Kristin A. Persson
Gerbrand Ceder
2025

The rapid development of foundation potentials (FPs) in machine learning interatomic potentials demonstrates the possibility for generalizable learning of the universal potential energy surface. The accuracy of FPs can be further improved by bridging the model from lower-fidelity datasets to highfidelity ones. In this work, we analyze the challenge of this transfer learning (TL) problem within the CHGNet framework. We show that significant energy scale shifts and poor correlations between GGA and r2 SCAN hinder cross-functional transferability. By benchmarking different TL approaches on...

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine-learning interatomic potentials

Peichen Zhong
Bowen Deng
Shashwat Anand
Tara Mishra
Gerbrand Ceder
2025

Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure (δ-phase) during electrochemical cycling. In this computational study, we use charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in LixMn0.8Ti0.1O1.9F0.1. Our results indicate that transition metal migration occurs and reorders to form the spinel-like ordering in an FCC anion framework. The transformed structure contains a higher concentration of nontransition metal (0-TM)...

Magnetically and optically active edges in phosphorene nanoribbons

Arjun Ashoka
Adam J. Clancy
Naitik A. Panjwani
Adam Cronin
Loren Picco
Eva S. Y. Aw
Nicholas J. M. Popiel
Alexander G. Eaton
Thomas G. Parton
Rebecca R. C. Shutt
Sascha Feldmann
Remington Carey
Thomas J. Macdonald
Cheng Liu
Marion E. Severijnen
Sandra Kleuskens
Loreta A. Muscarella
Felix R. Fischer
Hilton Barbosa de Aguiar
Richard H. Friend
Jan Behrends
Peter C. M. Christianen
Christopher A. Howard
Raj Pandya
2025

Nanoribbons, nanometre-wide strips of a two-dimensional material, are a unique system in condensed matter. They combine the exotic electronic structures of low-dimensional materials with an enhanced number of exposed edges, where phenomena including ultralong spin coherence times1,...

Coupling of Nondegenerate Topological Modes in Nitrogen Core-Doped Graphene Nanoribbons

Peter H. Jacobse
Michele Pizzochero
Ethan Chi Ho Wen
Gabriela Borin Barin
Xinheng Li
Zafer Mutlu
Klaus Müllen
Efthimios Kaxiras
Michael F. Crommie
Felix R. Fischer
2025

Nitrogen core-doping of graphene nanoribbons (GNRs) allows trigonal planar carbon atoms along the backbone of GNRs to be substituted by higher-valency nitrogen atoms. The excess valence electrons are injected into the π-orbital system of the GNR, thereby changing not only its electronic occupation but also its topological properties. We have observed this topological change by synthesizing dilute nitrogen core-doped armchair GNRs with a width of five atoms (N2-5-AGNRs). The incorporation of pairs of trigonal planar nitrogen atoms results in the emergence of...

Orbital Engineering Band Degeneracy in a Dual-Square Carbon-Oxide Framework

Fujia Liu
Yuyi Yan
Weichen Tang
Boyu Qie
Jieqi Chen
Ziyi Wang
Steven G. Louie
Felix R. Fischer
2025

Electron band degeneracies in momentum space give rise to exotic quantum phenomena that have sparked intense interest in condensed matter physics and materials science. Nodal-lines─isolines in k-space formed by the incidental touching of two bands that share the same energy but belong to discrete eigenstates─arise in the presence of symmetries that preclude effective hybridization. Despite recent advances in the design, bottom-up assembly, and engineering of exotic electronic states in graphene nanomaterials, the extension of this approach to access...

Kinetic Isotope Effect in the Radical Step-Growth Termination of On-Surface Synthesized Graphene Nanoribbons

Aidan P. Delgado
Michael C. Daugherty
Christina M. Dadich
Wyatt Ochs
Jonas Björk
Felix R. Fischer
2025

Bottom-up on-surface synthesis has emerged as a versatile tool to access and finely tune the electronic structure of nanographenes. The controlled generation of reactive intermediates catalyzed and stabilized by a supporting substrate has enabled the design, assembly, and characterization of a wide range of exotic tailor-made quantum materials. Even under these tightly controlled conditions, the growth of extended structures remains limited by termination processes and undesired side reactions. Here, we identify an H atom transfer as one principal contributor to the radical step...

Bidirectional Suzuki Catalyst Transfer Polymerization of Poly(p-phenylene)

Christina M. Dadich
Felix R. Fischer
2025

Suzuki catalyst transfer polymerization (SCTP) has emerged as an effective method for accessing length-controlled π-conjugated poly(p-phenylene). Regio-controlled functional group sequencing along the backbone and the chain ends of synthetic polymers remains a challenge for the successful integration of organic semiconductors in sensors and electronic devices. Here, we report a bidirectional SCTP system based on dinuclear palladium(II) initiators. Functional groups transferred during the initiation and termination steps unlock independently addressable synthetic handles...