arXiv subjects

Steven Bennett

Publications and source records attributed to Steven Bennett.

4 recordsLinked to original sources

arXiv2026

Facile Optimization of Combinatorial Sputtering Processes with Arbitrary Numbers of Components for Targeted Compositions

Combinatorial sputtering is a physical vapor deposition method that enables the high-throughput synthesis of compositionally varied thin films. Using this technique, the effects of stoichiometry on specific properties of alloy thin films with analog composition gradients can be mapped using high-throughput characterization. To obtain specific stoichiometries, such as those desired for an equiatomic, intermetallic, or doped compounds, the sputter power of each target must be simultaneously tuned to optimize the deposition rate of each component. This optimization problem increases in complexity with the number of components, which commonly leads to iterative guess-and-check processing and can limit the intrinsic high-throughput advantages of this synthesis method. To circumvent this challenge, this work introduces a composition optimization procedure that enables the facile synthesis of sputtered combinatorial films with targeted compositions. This procedure leverages the expeditious mapping of composition using wavelength dispersive x-ray fluorescence and is capable of optimizing processing for an arbitrary number of components. As a demonstration, this method is leveraged to sputter a combinatorial Cr$_{v}$Fe$_{w}$Mo$_{x}$Nb$_{y}$Ta$_{z}$ film with an equiatomic composition near the wafer center.

cond-mat.mtrl-sci↗

arXiv2025

Mofasa: A Step Change in Metal-Organic Framework Generation

Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ .

cs.LG↗

arXiv2024

Comparative Analysis of search Approaches to Discover Donor Molecules for Organic Solar Cells

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over $10^{14}$ molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.

physics.chem-ph↗

arXiv2024

Orb: A Fast, Scalable Neural Network Potential

We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. We explore several aspects of foundation model development for materials, with a focus on diffusion pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo and molecular dynamics simulations.

cond-mat.mtrl-sci↗