William H. Green
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Accurately Predicting Solubility Curves via a Thermodynamic Cycle, Machine Learning, and Solvent Ensembles Open
Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essent…
A health economics assessment of self-care with over-the-counter ibuprofen in dysmenorrhoea, migraine and acute rhinosinusitis in the United Kingdom Open
Background Increased appropriate use of self-care for minor conditions can reduce the number of healthcare professional appointments and, hence, provide opportunity cost savings to the National Health Service (NHS). The receipt of over-the…
RIGR: Resonance Invariant Graph Representation for Molecular Property Prediction Open
Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules exhibiting resonance, where multiple val…
The $L^p$-continuity of wave operators for fractional order Schrödinger operators Open
We consider fractional Schrödinger operators $H=(-Δ)^α+V(x)$ in $n$ dimensions with real-valued potential $V$ when $n>2α$, $α>1$. We show that the wave operators extend to bounded operators on $L^p(\mathbb R^n)$ for all $1\leq p\leq\infty$…
Dispersive estimates for fractional order Schrödinger operators Open
We prove dispersive bounds for fractional Schrödinger operators on $\mathbb R^n$ of the form $H=(-Δ)^α+V$ with $V$ a real-valued, decaying potential and $α\notin\mathbb N$. We derive pointwise bounds on the resolvent operators for all $0<α…
Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction Open
Accurate prediction of molecular properties is essential for computational design in many areas of chemistry. Deep learning has been used in these prediction tasks for a wide variety of molecular properties, and the availability of user-fr…
Accurately predicting solubility curves via a thermodynamic cycle, machine learning, and solvent ensembles Open
Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essent…
Precontact Use of Balsam Fir (Abies balsamea) in Iowa, USA Open
Excavation of a cave in eastern Iowa (USA) revealed a feature containing charred wood of balsam fir (Abies balsamea) dating to ca. AD 300–400. Taxon identification was based on wood anatomy and species distribution. Balsam fir, a boreal fo…
PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction Open
Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constr…
Solvation free energies of anions: from curated reference data to predictive models Open
Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited…
Alternative cool‐season turfgrass species for overseeding dormant bermudagrass Open
Bermudagrass is often overseeded with cool‐season turfgrass in the winter to provide an actively growing green surface during the winter and early spring. Perennial ryegrass ( Lolium perenne L.) is the most widely used cool‐season turfgras…
Organic Solubility Prediction at the Limit of Aleatoric Uncertainty Open
Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process …
Solvation free energies of anions: from curated reference data to predictive models Open
Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited…
New modified Arrhenius equation to describe the temperature dependence of liquid phase reaction rates Open
Chemical reactions in subcritical or near-critical solvents hold significant promise for numerous industrial and environmental applications. The Arrhenius equation is typically used to describe the temperature dependence of reaction rates,…
Toward Accurate Quantum Mechanical Thermochemistry: (2) Optimal Methods for Enthalpy Calculations from Comprehensive Benchmarks of 284 Model Chemistries Open
Accurate and efficient computations of standard enthalpies of formation (Hf) for small organic molecules are crucial for diverse chemical engineering and scientific applications. Building on part 1 of this work [J. Phys. Chem. A 2024, 128,…
RIGR: Resonance Invariant Graph Representation for Molecular Property Prediction Open
Graph neural networks, which rely on Lewis structure representations, have emerged as a powerful tool for predicting molecular and reaction properties. However, a key limitation arises with molecules exhibiting resonance, where multiple va…
Corrections to “Thermodynamic and Chemical Kinetic Parameters in Ammonia Oxidation: A Comparison of Recent Studies and Parameter Recommendations” Open
[This corrects the article DOI: 10.1021/acs.energyfuels.4c03352.].
Solvation free energies of anions: from new reference data to predictive models Open
Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited…
Solvation free energies of anions: from new reference data to predictive models Open
Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited…
Battery Electric Long-Haul Trucking in the United States: A Comprehensive Costing and Emissions Analysis Open
This work presents a costing and emissions analysis of long-haul battery electric trucks (BETs) with overnight charging for the U.S. market. First, we compute the energy requirements of a long-haul truck for a 600-mile (966 km) real-world …
Toward Accurate Quantum Mechanical Thermochemistry: (2) Optimal Methods for Enthalpy Calculations from Comprehensive Benchmarks of 284 Model Chemistries Open
Accurate and efficient computations of standard enthalpies of formation (Hf) for small organic molecules are crucial for diverse chemical engineering and scientific applications. Building upon our earlier work [J. Phys. Chem. A 2024, 128, …
ASKCOS: an open source software suite for synthesis planning Open
The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest ve…
<span>p<i>K</i><sub>a</sub></span> prediction in non‐aqueous solvents Open
Acid dissociation constants () are widely measured and studied, most typically in water. Comparatively few datasets and models for non‐aqueous values exist. In this work, we demonstrate how the in one solvent can be accurately determined u…
Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions Open
Electrochemical C−H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. Here, we report an integrated approach combining machine lear…
View article: Pooling Solvent Mixtures for Solvation Free Energy Predictions
Pooling Solvent Mixtures for Solvation Free Energy Predictions Open
Solvation free energy is an important design parameter in reaction kinetics and separation processes, making it a critical property to predict during process development. In previous research, directed message passing neural networks (D-MP…