arXiv subjects

Felix Adams

Publications and source records attributed to Felix Adams.

5 recordsLinked to original sources

arXiv2026

FPBench: Application-Oriented Error Decomposition for Foundation Potentials

Foundation potentials (FPs) have emerged as a new basis for atomistic modeling. While their evaluation using average energy and force errors often indicates near-DFT accuracy, their performance in practical computational studies remains inconsistent. Here, we present FPBench, an application-oriented benchmark that evaluates FPs on representative computational tasks. Multiple state-of-the-art FPs are assessed on three fundamental tasks: force prediction for atomistic simulations, energy ranking for substitutional and vacancy orderings, and ion/vacancy migration. Benchmarking these FPs shows that average force and energy errors often fail to predict task performance, revealing substantial differences in practical reliability. FPBench introduces application-oriented error decomposition through metrics that resolve performance according to the physically consequential quantities and configurations governing computational tasks, including the fractions of highly accurate and large-force-error atoms, far-from-equilibrium atoms, relative phase-stability and convex-hull agreement, and along-path errors in ion migration. These error-decomposition metrics identify where FP errors arise within specific computational tasks, providing targeted guidance for model development. FPBench provides an open benchmark, evaluation code, and a public leaderboard for rigorous FP assessment and development.

cond-mat.mtrl-sci↗

arXiv2026

First-Principles Thermodynamic Analysis of Ternary Chalcogenide Phase Change Materials

Chalcogenide phase-change materials (PCMs) are important for nonvolatile memory and reconfigurable photonic technologies. The GeTe-Sb2Te3 system, commonly referred to as GST, is the best-known PCM family, but new PCMs are needed to broaden the accessible property space while retaining fast and reversible switching. Here, we propose a thermodynamic framework, motivated by Ostwald's rule, for understanding and identifying PCMs, since direct modeling of phase-transition dynamics is computationally expensive. Using first-principles calculations, we systematically evaluate the energetics of ternary chalcogenide mixtures and their polymorphs along binary-binary tie lines. By comparing ground-state and metastable structures, we assess phase stability, miscibility, and the likelihood of GST-like polymorph-mediated crystallization pathways across a broad range of ternary chalcogenide mixtures. The calculations reproduce known behavior in GST and related systems and identify several promising candidate mixtures with similar features. These results provide insight into why some PCM systems are more favorable than others and establish a thermodynamic framework for future PCM discovery.

cond-mat.mtrl-sci↗

arXiv2026

Quantum Kernel Machine Learning for Autonomous Materials Science

Autonomous materials science, where active learning is used to navigate large compositional phase space, has emerged as a powerful vehicle to rapidly explore new materials. A crucial aspect of autonomous materials science is exploring new materials using as little data as possible. Gaussian process-based active learning allows effective charting of multi-dimensional parameter space with a limited number of training data, and thus is a common algorithmic choice for autonomous materials science. An integral part of the autonomous workflow is the application of kernel functions for quantifying similarities among measured data points. A recent theoretical breakthrough has shown that quantum kernel models can achieve similar performance with less training data than classical models. This signals the possible advantage of applying quantum kernel machine learning to autonomous materials discovery. In this work, we compare quantum and classical kernels for their utility in sequential phase space navigation for autonomous materials science. Specifically, we compute a quantum kernel and several classical kernels for x-ray diffraction patterns taken from an Fe-Ga-Pd ternary composition spread library. We conduct our study on both IonQ's Aria trapped ion quantum computer hardware and the corresponding classical noisy simulator. We experimentally verify that a quantum kernel model can outperform some classical kernel models. The results highlight the potential of quantum kernel machine learning methods for accelerating materials discovery and suggest complex x-ray diffraction data is a candidate for robust quantum kernel model advantage.

cond-mat.mtrl-sci↗

arXiv2025

Incorporating Si into Sb2Se3: Tailoring Optical Phase Change Materials via Nanocomposites

Chalcogenide-based optical phase change materials (OPCMs) exhibit a large contrast in refractive index when reversibly switched between their stable amorphous and crystalline states. OPCMs have rapidly gained attention due to their versatility as nonvolatile amplitude or phase modulators in various photonic devices. However, open challenges remain, such as achieving reliable response and transparency spanning into the visible spectrum, a combination of properties in which current broadband OPCMs (e.g., Ge2Sb2Se4Te1, Sb2Se3, or Sb2S3) fall short. Discovering novel materials or engineering existing ones is, therefore, crucial in extending the application scope of OPCMs. Here, we use magnetron co-sputtering to study the effects of Si doping into Sb2Se3. We employ ellipsometry, X-ray diffraction, Raman spectroscopy, and scanning and transmission electron microscopy to investigate the effects of Si doping on the optical properties and crystal structure and compare these results with those from first principles calculations. Moreover, we study the crystallization and melt-quenching of thin films via nano-differential scanning calorimetry (NanoDSC). Our experiments demonstrate that 20% Si doping increases the transparency window in both states, specifically to 800 nm (1.55 eV) in the amorphous phase, while reducing power consumption by lowering the melting temperature. However, this reduction comes at the cost of reducing the refractive index contrast between states and slowing the kinetics of the phase transition.

cond-mat.mtrl-sci↗

arXiv2023

Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping

Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input.

cond-mat.mtrl-sci↗