President Trump this week continued to make his environmental priorities clear by vowing to open up hundreds of coal power plants in the United States in an effort to advance competition against China. "After years of being held captive by Environmental Extremists, Lunatics, Radicals, and Thugs, allowing other Countries, in particular China, to gain tremendous Economic advantage over us by opening up hundreds of all Coal Fire Power Plants, I am authorizing my Administration to immediately begin producing Energy with BEAUTIFUL, CLEAN COAL," Trump wrote in a post on social media Monday. Though the post was not linked to any particular policy plans or documents, it arrives as the White House takes aim at various environmental agencies and clean-energy initiatives. In the last week alone, the administration has announced plans to significantly roll back regulations that govern coal production and to potentially lay off up to 65% of scientists and researchers at the Environmental Protection Agency, among other actions. Coal accounts for about 16% of the country's electricity generation, according to the U.S. Energy Information Administration -- down from about 50% in 2000 as natural gas and nuclear and renewable energy have grown.

Materials synthesis is vital for innovations such as energy storage, catalysis, electronics, and biomedical devices. Yet, the process relies heavily on empirical, trial-and-error methods guided by expert intuition. Our work aims to support the materials science community by providing a practical, data-driven resource. We have curated a comprehensive dataset of 17K expert-verified synthesis recipes from open-access literature, which forms the basis of our newly developed benchmark, AlchemyBench. AlchemyBench offers an end-to-end framework that supports research in large language models applied to synthesis prediction. It encompasses key tasks, including raw materials and equipment prediction, synthesis procedure generation, and characterization outcome forecasting. We propose an LLM-as-a-Judge framework that leverages large language models for automated evaluation, demonstrating strong statistical agreement with expert assessments. Overall, our contributions offer a supportive foundation for exploring the capabilities of LLMs in predicting and guiding materials synthesis, ultimately paving the way for more efficient experimental design and accelerated innovation in materials science.

The impressive capabilities of Large Language Models (LLMs) across diverse tasks are now well-established, yet their effective deployment necessitates careful hyperparameter optimization. Through extensive empirical studies involving grid searches across diverse configurations, we discover universal scaling laws governing these hyperparameters: optimal learning rate follows a power-law relationship with both model parameters and data sizes, while optimal batch size scales primarily with data sizes. Our analysis reveals a convex optimization landscape for hyperparameters under fixed models and data size conditions. This convexity implies an optimal hyperparameter plateau. We contribute a universal, plug-and-play optimal hyperparameter tool for the community. Its estimated values on the test set are merely 0.09% away from the globally optimal LLM performance found via an exhaustive search. These laws demonstrate remarkable robustness across variations in model sparsity, training data distribution, and model shape. To our best known, this is the first work that unifies different model shapes and structures, such as Mixture-of-Experts models and dense transformers, as well as establishes optimal hyperparameter scaling laws across diverse data distributions. This exhaustive optimization process demands substantial computational resources, utilizing nearly one million NVIDIA H800 GPU hours to train 3,700 LLMs of varying sizes and hyperparameters from scratch and consuming approximately 100 trillion tokens in total. To facilitate reproducibility and further research, we will progressively release all loss measurements and model checkpoints through our designated repository https://step-law.github.io/

In motion simulation, motion cueing algorithms are used for the trajectory planning of the motion simulator platform, where workspace limitations prevent direct reproduction of reference trajectories. Strategies such as motion washout, which return the platform to its center, are crucial in these settings. For serial robotic MSPs with highly nonlinear workspaces, it is essential to maximize the efficient utilization of the MSPs kinematic and dynamic capabilities. Traditional approaches, including classical washout filtering and linear model predictive control, fail to consider platform-specific, nonlinear properties, while nonlinear model predictive control, though comprehensive, imposes high computational demands that hinder real-time, pilot-in-the-loop application without further simplification. To overcome these limitations, we introduce a novel approach using deep reinforcement learning for motion cueing, demonstrated here for the first time in a 6-degree-of-freedom setting with full consideration of the MSPs kinematic nonlinearities. Previous work by the authors successfully demonstrated the application of DRL to a simplified 2-DOF setup, which did not consider kinematic or dynamic constraints. This approach has been extended to all 6 DOF by incorporating a complete kinematic model of the MSP into the algorithm, a crucial step for enabling its application on a real motion simulator. The training of the DRL-MCA is based on Proximal Policy Optimization in an actor-critic implementation combined with an automated hyperparameter optimization. After detailing the necessary training framework and the algorithm itself, we provide a comprehensive validation, demonstrating that the DRL MCA achieves competitive performance against established algorithms. Moreover, it generates feasible trajectories by respecting all system constraints and meets all real-time requirements with low...

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end-to-end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint-state method. Since neural operators do not involve the Born approximation, when used for full waveform inversion they have the potential to include additional phases and alleviate cycle-skipping problems present in traditional adjoint-state formulations. In this study, we demonstrate the application of neural operators for full waveform inversion on a real seismic dataset, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.

We propose Diffusion-Informed Model Predictive Control (D-I MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable stochastic systems by integrating diffusion-based time series forecasting models in Model Predictive Control algorithms. In our approach, a diffusion-based time series forecasting model is used to probabilistically estimate the evolution of the system's stochastic components. These forecasts are then incorporated into MPC algorithms to estimate future trajectories and optimize action selection under the uncertainty of the future. We evaluate the framework on the task of energy arbitrage, where a Battery Energy Storage System participates in the day-ahead electricity market of the New York state. Experimental results indicate that our model-based approach with a diffusion-based forecaster significantly outperforms both implementations with classical forecasting methods and model-free reinforcement learning baselines.

The extent to which foundation models can disclose novel chemical, biological, radiation, and nuclear (CBRN) threats to expert users is unclear due to a lack of test cases. I leveraged the unique opportunity presented by an upcoming publication describing a novel catastrophic biothreat - "Technical Report on Mirror Bacteria: Feasibility and Risks" - to conduct a small controlled study before it became public. Graduate-trained biologists tasked with predicting the consequences of releasing mirror E. coli showed no significant differences in rubric-graded accuracy using Claude Sonnet 3.5 new (n=10) or web search only (n=2); both groups scored comparably to a web baseline (28 and 43 versus 36). However, Sonnet reasoned correctly when prompted by a report author, but a smaller model, Haiku 3.5, failed even with author guidance (80 versus 5). These results suggest distinct stages of model capability: Haiku is unable to reason about mirror life even with threat-aware expert guidance (Stage 1), while Sonnet correctly reasons only with threat-aware prompting (Stage 2). Continued advances may allow future models to disclose novel CBRN threats to naive experts (Stage 3) or unskilled users (Stage 4). While mirror life represents only one case study, monitoring new models' ability to reason about privately known threats may allow protective measures to be implemented before widespread disclosure.

The integration of extra-robotic limbs/fingers to enhance and expand motor skills, particularly for grasping and manipulation, possesses significant challenges. The grasping performance of existing limbs/fingers is far inferior to that of human hands. Human hands can detect onset of slip through tactile feedback originating from tactile receptors during the grasping process, enabling precise and automatic regulation of grip force. The frictional information is perceived by humans depending upon slip happening between finger and object. Enhancing this capability in extra-robotic limbs or fingers used by humans is challenging. To address this challenge, this paper introduces novel approach to communicate frictional information to users through encoded vibrotactile cues. These cues are conveyed on onset of incipient slip thus allowing users to perceive friction and ultimately use this information to increase force to avoid dropping of object. In a 2-alternative forced-choice protocol, participants gripped and lifted a glass under three different frictional conditions, applying a normal force of 3.5 N. After reaching this force, glass was gradually released to induce slip. During this slipping phase, vibrations scaled according to static coefficient of friction were presented to users, reflecting frictional conditions. The results suggested an accuracy of 94.53 p/m 3.05 (mean p/mSD) in perceiving frictional information upon lifting objects with varying friction. The results indicate effectiveness of using vibrotactile feedback for sensory feedback, allowing users of extra-robotic limbs or fingers to perceive frictional information. This enables them to assess surface properties and adjust grip force according to frictional conditions, enhancing their ability to grasp, manipulate objects more effectively.

This paper presents a comprehensive study leveraging Support Vector Machine (SVM) regression and Principal Component Regression (PCR) to analyze carbon dioxide emissions in a global dataset of 62 countries and their dependence on idiosyncratic, country-specific parameters. The objective is to understand the factors contributing to carbon dioxide emissions and identify the most predictive elements. The analysis provides country-specific emission estimates, highlighting diverse national trajectories and pinpointing areas for targeted interventions in climate change mitigation, sustainable development, and the growing carbon credit markets and green finance sector. The study aims to support policymaking with accurate representations of carbon dioxide emissions, offering nuanced information for formulating effective strategies to address climate change while informing initiatives related to carbon trading and environmentally sustainable investments.

Computational Fluid Dynamics (CFD) simulations are essential in product design, providing insights into fluid behavior around complex geometries in aerospace and automotive applications. However, high-fidelity CFD simulations are computationally expensive, making rapid design iterations challenging. To address this, we propose TripNet, Triplane CFD Network, a machine learning-based framework leveraging triplane representations to predict the outcomes of large-scale, high-fidelity CFD simulations with significantly reduced computation cost. Our method encodes 3D geometry into compact yet information-rich triplane features, maintaining full geometry fidelity and enabling accurate aerodynamic predictions. Unlike graph- and point cloud-based models, which are inherently discrete and provide solutions only at the mesh nodes, TripNet allows the solution to be queried at any point in the 3D space. Validated on high-fidelity DrivAerNet and DrivAerNet++ car aerodynamics datasets, TripNet achieves state-of-the-art performance in drag coefficient prediction, surface field estimation, and full 3D flow field simulations of industry-standard car designs. By utilizing a shared triplane backbone across multiple tasks, our approach offers a scalable, accurate, and efficient alternative to traditional CFD solvers.