Breakthroughs, discoveries, and DIY tips sent every weekday. A smelly, sometimes toxic "killer belt of seaweed" might put a damper on Floridians' Memorial Day weekend plans. Sargassum is back just in time for the unofficial start of summer and this year's influx of the brown algae would be record breaking at 31 million tons. Sargassum is a genus of large brown seaweed. As a seaweed, it is also a type of algae.

A Russian drone attack has inflicted tens of millions of pounds of damage to the site of the Chornobyl nuclear power plant, according to experts. The photographer Julia Kochetova has gained access to the area

A pair of studies analyzing the effects of AI on our planet have been released and the news is fairly grim. Greenpeace studied the emissions generated from the production of the semiconductors used in AI chips and found that there was a fourfold increase in 2024. This analysis was completed using publicly available data. Many of the big chipmakers like NVIDIA rely on companies like Taiwan Semiconductor Manufacturing Co and SK Hynix Inc. for the components of GPUs and memory units. Most of this manufacturing happens in Taiwan, South Korea and Japan, where power grids are primarily reliant on fossil fuels.

With upcoming sample return missions across the solar system and the increasing availability of mass spectrometry data, there is an urgent need for methods that analyze such data within the context of existing astrobiology literature and generate plausible hypotheses regarding the emergence of life on Earth. Hypothesis generation from mass spectrometry data is challenging due to factors such as environmental contaminants, the complexity of spectral peaks, and difficulties in cross-matching these peaks with prior studies. To address these challenges, we introduce AstroAgents, a large language model-based, multi-agent AI system for hypothesis generation from mass spectrometry data. AstroAgents is structured around eight collaborative agents: a data analyst, a planner, three domain scientists, an accumulator, a literature reviewer, and a critic. The system processes mass spectrometry data alongside user-provided research papers. The data analyst interprets the data, and the planner delegates specific segments to the scientist agents for in-depth exploration. The accumulator then collects and deduplicates the generated hypotheses, and the literature reviewer identifies relevant literature using Semantic Scholar. The critic evaluates the hypotheses, offering rigorous suggestions for improvement. To assess AstroAgents, an astrobiology expert evaluated the novelty and plausibility of more than a hundred hypotheses generated from data obtained from eight meteorites and ten soil samples. Of these hypotheses, 36% were identified as plausible, and among those, 66% were novel. Project website: https://astroagents.github.io/

Secretary of Energy Chris Wright discusses the economic impact of lowering energy prices, why energy is essential for artificial intelligence dominance, American LNG exports and possible U.S. operation of Ukrainian nuclear plants. Rep. August Pfluger, R-Texas, said that his constituents are feeling optimistic once again about the future of the oil and gas industry in his district and beyond. The Republican represents parts of central Texas that are critical to the industry, including the Permian Basin, as the Trump administration has famously promised to "drill, baby, drill." "Think about the hardworking men and women of the Permian Basin, or the Bakken or the Marcellus, or any other producing area. President Biden said, 'What you do is evil. You producing oil and gas is evil.' I mean, they basically demonized them," he told Fox News Digital in a recent interview.

Energy Secretary Chris Wright discusses the economic impact of lowering energy prices, why energy is essential for artificial intelligence dominance, American LNG exports and possible U.S. operation of Ukrainian nuclear plants. Six House Democrats broke from their party on Thursday to pass a pair of bills blocking Biden administration-era green energy rules. One resolution, led by Rep. Stephanie Bice, R-Okla., seeks to overturn regulations imposed by former President Joe Biden's Department of Energy (DOE) for new clean energy standards targeting walk-in freezers and coolers. Biden speaks during the United Auto Workers union conference at the Marriott Marquis in Washington on Jan. 24, 2024. "I have fought every step of the way to prevent egregious rules from taking effect. These regulations will impose significant financial burdens on small businesses, which will have to absorb major upgrade costs to meet these new, aggressive standards," Bice told Fox News Digital.

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and lack adaptability. We introduce UniGuide, a framework for controlled geometric guidance of unconditional diffusion models that allows flexible conditioning during inference without the requirement of extra training or networks. We show how applications such as structure-based, fragment-based, and ligand-based drug design are formulated in the UniGuide framework and demonstrate on-par or superior performance compared to specialised models. Offering a more versatile approach, UniGuide has the potential to streamline the development of molecular generative models, allowing them to be readily used in diverse application scenarios.

LLMs with improved chemical reasoning capabilities in the other two sub-tasks.

The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space.

Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites. However, such predictive tools are still limited as they occupy one of two extremes, either operating (a) by fragmenting molecules combinatorially with overly rigid constraints on potential rearrangements and poor time complexity or (b) by decoding lossy and nonphysical discretized spectra vectors. In this work, we use a new intermediate strategy for predicting mass spectra from molecules by treating mass spectra as sets of molecular formulae, which are themselves multisets of atoms. After first encoding an input molecular graph, we decode a set of molecular subformulae, each of which specify a predicted peak in the mass spectrum, the intensities of which are predicted by a second model. Our key insight is to overcome the combinatorial possibilities for molecular subformulae by decoding the formula set using a prefix tree structure, atom-type by atom-type, representing a general method for ordered multiset decoding. We show promising empirical results on mass spectra prediction tasks.