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La mayoría de túmulos del este de Norteamérica han sido destruidos-¿o no? Nosotros revisamos los métodos geofísicos para evaluar si los túmulos nivelados retienen depósitos o rasgos intactos. Una encuesta magnética da esperanzas de encontrar y evaluar los túmulos nivelados porque es rápida y sensitiva a las variaciones magnéticas asociadas con rasgos anticipados como hoyos y depósitos de relleno de túmulos. Como estudio de caso, hablamos sobre nuestro estudio magnético del sitio de Gast Farm (13LA12) en el este de Iowa. La encuesta abarcó 8,64 ha, cubriendo tanto como sitios de un túmulo previamente reportado y posibles movimientos de tierra geométricos, así como áreas de habitación de los períodos Middle y Late Woodland. La interpretación de los resultados de la encuesta incorporó la diferenciación cuantitativa de los tipos de anomalías magnéticas utilizando técnicas del sistema de información geográfica (SIG), junto con la inspección visual estándar. No encontramos ninguna evidencia de movimientos de tierra geométricos, pero identificamos al menos seis túmulos nivelados. El relleno del túmulo desplazado parece explicar las características de movimiento de tierras. Concluimos que los túmulos nivelados son detectables y pueden retener la integridad del subsuperficie. Sus características asociadas, incluidos los entierros, pueden ser identificables incluso cuando la evidencia sobre la tierra ha desaparecido. 

Bio-derived isobutanol has been approved as a gasoline additive in the US, but our understanding of its combustion chemistry still has significant uncertainties. Detailed quantum calculations could improve model accuracy leading to better estimation of isobutanol's combustion properties and its environmental impacts. This work examines 47 molecules and 38 reactions involved in the first oxygen addition to isobutanol's three alkyl radicals located α, β, and γ to the hydroxide. Quantum calculations are mostly done at CCSD(T)-F12/cc-pVTZ-F12//B3LYP/CBSB7, with 1-D hindered rotor corrections obtained at B3LYP/6-31G(d). The resulting potential energy surfaces are the most comprehensive isobutanol peroxy networks published to date. Canonical transition state theory and a 1-D microcanonical master equation are used to derive high-pressure-limit and pressure-dependent rate coefficients, respectively. At all conditions studied, the recombination of γ-isobutanol radical with O 2 forms HO 2 + isobutanal. The recombination of β-isobutanol radical with O 2 forms a stabilized hydroperoxy alkyl radical below 400 K, water + an alkoxy radical at higher temperatures, and HO 2 + an alkene above 1200 K. The recombination of β-isobutanol radical with O 2 results in a mixture of products between 700–1100 K, forming acetone + formaldehyde + OH at lower temperatures and forming HO 2 + alkenes at higher temperatures. The barrier heights, high-pressure-limit rates, and pressure-dependent kinetics generally agree with the results from previous quantum chemistry calculations. Six reaction rates in this work deviate by over three orders of magnitude from kinetics in detailed models of isobutanol combustion, suggesting the rates calculated here can help improve modeling of isobutanol combustion and its environmental fate. 

Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications.