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Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design…
The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. Quickly generating and predicting inorganic crystal structures is important for…
We present some preliminary results obtained with a new galactic chemodynamical tool under development. In the framework of non-instantaneous recycling approach, we follow the interactions due to star formation and feedback processes. One…
We have coupled a chemical model with two dynamical models of collapsing low mass star-forming cores to predict abundances across the core of the commonly used infall tracers, CS and HCO$^+$, at various stages of the collapse. The models…
Cryogenic buffer gas sources are ubiquitous for producing cold, collimated molecular beams for quantum science, chemistry, and precision measurements. The molecules are typically produced by laser ablating a metal target in the presence of…
Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural…
Intercalation materials are promising candidates for reversible energy storage and are, for example, used as lithium-battery electrodes, hydrogen-storage compounds, and electrochromic materials. An important issue preventing the more…
The formation and growth of refractory matter on pre-existing interstellar dust grain surfaces was studied experimentally by annealing neon-ice matrices in which potential precursors of silicate grains (Mg and Fe atoms, SiO and SiO$_2$…
In this work, we conducted molecular dynamics simulations to study the fracture mechanism of ice crystals in a bulk phase and at ice-ice interfaces at the atomistic scale. We show that there exists a narrow disordered interfacial layer…
Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many different chemical elements and domains, these…
The isothermal compressibility of a general crystal is analyzed within classical density functional theory. Our approach can be used for homogeneous and unstrained crystals containing an arbitrarily high density of local defects. We start…
This work presents BioPykrete, a new sustainable bio-composite material created from ice, nano-crystalline cellulose (CNC), and a tailor-made chimera protein designed to bind the two together. We developed and produced the chimera protein…
Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We…
Biochars are an exciting class of environmental materials with a wide range of applications, including carbon storage and sequestration, soil enhancement, and pollution remediation. However, the limited knowledge of their molecular…
Atomistic simulations have become a powerful tool in materials research due to the extremely fine spatial and temporal resolution provided by such techniques. In order to understand the fundamental principles which govern material behavior…
The structure and properties of water or ice are of great interest to researchers due to their importance in the biological, cryopreservation and environmental fields. Hexagonal ice (Ih) is a common ice phase in nature and has been…
Cryo-electron microscopy (cryo-EM), the subject of the 2017 Nobel Prize in Chemistry, is a technology for determining the 3-D structure of macromolecules from many noisy 2-D projections of instances of these macromolecules, whose…
Ice nucleation and growth are critical in many fields, including atmospheric science, cryobiology, and aviation. However, understanding the detailed mechanisms of ice crystal growth remains challenging. In this work, crystallization at the…
Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior…
Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational diversity of dynamic biomolecular complexes, often requiring…