The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research. We introduce TensorNet, an innovative O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. By using Cartesian tensor atomic embeddings, feature mixing is simplified through matrix product operations. Furthermore, the cost-effective decomposition of these tensors into rotation group irreducible representations allows for the separate processing of scalars, vectors, and tensors when necessary. Compared to higher-rank spherical tensor models, TensorNet demonstrates state-of-the-art performance with significantly fewer parameters. For small molecule potential energies, this can be achieved even with a single interaction layer. As a result of all these properties, the model's computational cost is substantially decreased. Moreover, the accurate prediction of vector and tensor molecular quantities on top of potential energies and forces is possible. In summary, TensorNet's framework opens up a new space for the design of state-of-the-art equivariant models.

Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $Δ$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.

Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations. Graph neural networks (GNNs) have recently shown great successes for this task, overtaking classical methods based on fixed molecular kernels. However, they still appear very limited from a theoretical perspective, since regular GNNs cannot distinguish certain types of graphs. In this work we close this gap between theory and practice. We show that GNNs with directed edge embeddings and two-hop message passing are indeed universal approximators for predictions that are invariant to translation, and equivariant to permutation and rotation. We then leverage these insights and multiple structural improvements to propose the geometric message passing neural network (GemNet). We demonstrate the benefits of the proposed changes in multiple ablation studies. GemNet outperforms previous models on the COLL, MD17, and OC20 datasets by 34 %, 41 %, and 20 %, respectively, and performs especially well on the most challenging molecules. Our implementation is available online. 1

A machine learning model is traditionally considered robust if its prediction remains (almost) constant under input perturbations with small norm. However, real-world tasks like molecular property prediction or point cloud segmentation have inherent equivariances, such as rotation or permutation equivariance. In such tasks, even perturbations with large norm do not necessarily change an input's semantic content. Furthermore, there are perturbations for which a model's prediction explicitly needs to change. For the first time, we propose a sound notion of adversarial robustness that accounts for task equivariance.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. While beautiful, Bradford pear trees also stink. Breakthroughs, discoveries, and DIY tips sent six days a week. The sun is out, the streets are humming, the days are getting longer, and the air smells like like um say, can anyone else smell that? All over America, spring is getting smellier every year, and the culprit is the Bradford pear, a tree that gained popularity in the mid-20 century for its ornamental properties.

How the data center boom plunged one small Pennsylvania town into chaos. Valley View Estates is set to be surrounded by data centers. Get your news from a source that's not owned and controlled by oligarchs. "I don't like to see anyone upset," said Nick Farris of Provident Real Estate Advisors. He was sitting in the front of a crowd of roughly 150 inside Valley View High School's auditorium in Archbald, a town of about 7,500, huddled between two mountain ranges in Pennsylvania's Lackawanna Valley. Farris was there to represent the developer for Project Scott, one of many data center campuses coming to town. "I think that this is the best data center site in this area of the country, by far." The audience had been fairly quiet, bundled in thick coats against the late January cold. But as Farris spoke about data centers as a boon for communities, they began to laugh, drawing a rebuke from town officials. "What about the children?" someone shouted from the crowd. The children were watching from the walls; long banners of Valley View Performing Arts students hanging around the auditorium like championship pennants. Project Scott and four other data facilities will sit just a few thousand feet from the middle and high schools. He was referring to Lockheed Martin's 350,000-square-foot Missiles and Fire Control facility directly next to the high school, parts of which are highly contaminated . "That sucks too!" another attendee yelled back.

Firewire makes the most innovative surfboards in the industry. This winter, I tried the Neutrino, Machado, and Revo Max to see if they're worth the hype. For decades, the process of making a surfboard has more or less been the same: Cut a piece of foam. Put a wooden stringer down the middle to provide structure and strength. Shape it, then wrap it in fiberglass, sand it, and leave holes for the leash and fins. That was until Firewire Surfboards came along.

In a world of plastic water filter pitchers, I tested two of the new generation of stainless-steel filter systems. I will admit that the popularity of those giant, stainless steel, gravity-fed water filters remained a mystery to me for some years--even as multi-gallon water filter systems from brands like British Berkefeld and Berkey seemed to proliferate equally among lovers of doomsday prepping and holistic wellness retreats. I have been testing much different breeds of water filters for more than a year now, including reverse osmosis filters and water pitchers. But often, the big water filter tanks have seemed as much like status symbols as functional items. If you see a big gravity-fed filter, you know the person in question is serious about wellness, survival, or both. What changed my mind about these big stainless steel filters was microplastics . Most water filter pitchers are made of BPA-free plastic. But as new research shows that bottled-water drinkers ingest tens of thousands of excess microplastic particles, wellness lovers have begun to look askance at water filters that are themselves made of plastic.

How men were left to starve in a South African gold mine. This image was created by Mohamed Hussein using the artificial intelligence (AI) tool Midjourney. Ayanda Ndabeni watched the faint glow from his headlamp fight the vast darkness 1,500 metres (4,920 feet) below ground. His miner's lamp had lasted for more than a week after he was lowered down into the shaft of the gold mine. But now the batteries were dying. He gently flipped the plastic switch of his lamp, turning it off, and the trapped men around him became shadows. In the stifling heat and humidity, their anxiety pressed in from all sides. Ayanda had descended into Shaft 10 of the Buffelsfontein mine in late September 2024, lowered by a team of nearly 20 men operating ropes and a pulley above ground. That day, he'd spotted police vehicles near the mine's entrance. The 36-year-old assumed it was just routine patrols around the mine system, which is 2km (1.2 miles) deep. But then the rope pulley, via which food, water, batteries and other items arrived, stopped moving. The shouting that usually indicated the rope operators were sending down a man or supplies also fell silent. When huge rocks came crashing down the shaft, they knew it was a warning. The men whispered of their growing fears that something was very wrong on the surface. Patrick Ntsokolo was also in Shaft 10. He was a few hundred metres higher up than Ayanda and had arrived in late July. Patrick was new to the mines. Tasked by the leaders of the artisanal miners with collecting the food, water and alcohol lowered down by the rope pulley, he hauled supplies along the slippery tunnels to small shops.