Phase diagram of Nb6FeW; eabovehull: 0.030840 eV/atom; predicted_stable: False

22 meV above the hull

Supercell 3x3x3 of FeW3 (Space group: P4/nmm, 432 symmetry operations)

58 meV above the hull

An experimental 3D visualization to show the energy landscape of a ternary inter-metallic system, modeled after the classic compositional phase diagram. Consists of 858 source compounds generated by ggen.

OMatG predicted LiMnO4 crystal (space group: P1 #1, crystal system: triclinic)

The past few weeks, I've had my home lab running near 100% utilization running https://github.com/ourofoundation/ggen, searching for an ideal rare-earth-free permanent magnet. Many decent candidates h

Supercell 3x3x3 of Fe12BW4 (Space group: P4/mmm, 432 symmetry operations)

Phase diagram of Fe12BW4; eabovehull: 0.103858 eV/atom; predicted_stable: False

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); imaginary modes detected; min freq = -5.24 THz

73 meV above the hull

81 meV above the hull

Phase diagram of Fe5W2; eabovehull: 0.073006 eV/atom; predicted_stable: False

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); imaginary modes detected; min freq = -6.72 THz

Supercell 3x3x3 of Fe5W2 (Space group: P6/mmm, 648 symmetry operations)

51 meV above the hull

55meV above the hull

54 meV above the hull

For adult tobacco users in the United Arab Emirates seeking a cleaner, smoother alternative to traditional cigarettes, heat-not-burn technology offers an innovative and satisfying solution. Among the

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Generate a synthesis analysis from a chemical composition (e.g. Fe2O3). Returns markdown and an HTML report file for Ouro.

Generate a synthesis analysis from a CIF structure file. Send a file object with a URL to fetch. Returns markdown and an HTML report file for Ouro.

SKY is an LLM-powered synthesis exploration agent for inorganic materials. It performs composition- and structure-based similarity search on the Materials Project, retrieves neighbor synthesis recipes + metadata, and surfaces property/structure summaries. Ryan Nduma, Hyunsoo Park, Kinga Mastej - Imperial College London, Materials Design Group

Experiments with diffusion models to generate crystal structures, moving from noisy representations to concrete atomic arrangements. They describe learning how these models can learn structure without strict physical rules, and compare approaches that rely on fixed constraints to ones that let the model discover valid layouts. The author notes limitations in existing crystal generators, such as only producing tiny unit cells and struggling with complex, multi-atom systems like NdFeB. To address this, they explore modeling larger supercells with hundreds of atoms to improve detail and tolerance to errors, potentially revealing new properties through dopants. They keep a running experiment log in an AI notebook and plan to explore conditioning methods and the difference between flow and diffusion approaches in future work.

Copper is expected to consolidate with a mild upward bias into Week 5, 2026, with spot edging from $11,790.96/ton (2025‑12‑07) to around $11,813.9/ton by 2026‑03‑01 (+0.2%), masking a choppy intra‑period range after a >3% futures shakeout. The call: the recent selloff is a positioning‑driven flush, not the start of a structural downturn—prices stabilize and oscillate, but ultimately re‑anchor slightly above current levels as speculative excess is cleared while underlying demand and supply discipline keep deeper downside in check.

Forecasts for Copper Price with 12-period horizon (weekly)

WTI crude is poised for a shallow grind higher over the next 12 weeks, with prices projected to edge from $60.46 (1 Feb 2026) to $61.76 by 26 Apr 2026 (+2.2%) as a durable Middle East risk premium keeps the market biased upward despite chronic oversupply worries. Geopolitical tensions around the Strait of Hormuz and fragile non-OPEC output anchor several dollars of upside skew, while merely steady demand caps any breakout.

Forecasts for Crude Oil Price (WTI) with 12-period horizon (weekly)

Gold futures are projected to extend their powerful policy‑ and geopolitics‑driven bull run from 5,079.7 on 2026‑02‑01 to 5,877.97 by 2026‑04‑26—an anticipated gain of roughly 15.7%—with any drawdown toward 4,900 viewed as a corrective shakeout rather than the start of a top. A structurally weaker dollar, rising geopolitical risk tied to weaponized tariffs and energy flows, and a pro‑easier‑conditions policy bias together argue for a multi‑quarter upside regime rather than a fleeting risk‑off spike.

Silver is primed for an explosive upside continuation into late April, with futures projected to surge roughly 224% from 115.08 to 373.13 by the week of 2026‑04‑26, as the recent 25% plunge toward $84/oz marks a violent but corrective shakeout within a young secular bull rather than a top. A regime shift in macro and policy expectations under a Kevin Warsh Fed, compounded by escalating geopolitical and supply‑chain stress, is driving a step‑change repricing in silver where each new shock fuels asymmetric “fear bids” and sustained allocation from large capital pools.

Forecasts for Gold Futures with 12-period horizon (weekly)

Forecasts for Silver Futures with 12-period horizon (weekly)

Magnets are materials that create a magnetic field strong enough to attract metals like iron, nickel, and cobalt. Permanent magnets stay magnetized and are found in many everyday items. The main types are Neodymium (NdFeB), Samarium Cobalt (SmCo), Alnico, and Ferrite. Ferrite magnets are cheap, but not very strong and are brittle; they resist corrosion well and come in several grades. Alnico magnets stay stable at high temperatures and resist corrosion, made by sintering and casting. SmCo magnets resist heat and corrosion but are expensive and brittle, used in hot environments. Neodymium magnets are very strong but can corrode without coating and are costly, so they are used selectively. Uses include motors and generators in devices like fans and electric vehicles, speakers, hard drives, household magnets, magnetic latches, and toy magnets. They also help in mining, lifting heavy metal objects, medical imaging, and some sensors in cars. Magents are also used in security and navigation, like magnetic strips and compasses.

A permanent magnet is a magnet that keeps its magnetism for a long time. This includes natural magnets like magnetite and man-made magnets. Their magnetic strength doesn’t change easily, but heat or a strong reverse magnetic field can weaken or erase it. Some magnets can be damaged by high temperatures, with different materials lasting to different limits (for example, Alnico up to about 540°C, ferrite around 300°C, and neodymium magnets about 140°C). There are two main groups of permanent magnets. Alloy magnets include rare earth types such as Nd2Fe14B, SmCo, and AlNiCo. Ferrite magnets are made from iron oxides and come in various production forms like sintered, bonded, or molded. Other alloys have been used in the past, but they are less common today. Permanent magnets are found in many devices, from TVs and speakers to hard drives and phone vibrations, making them integral to everyday life.

Permanent magnets come from the tiny magnetic moments inside atoms, not from moving charges on a large scale. These moments come mainly from electron spin, with some contribution from the way electrons move around the nucleus, and a very small input from the nuclei themselves. In most materials the moments cancel out or are random, so there is no permanent magnetism. Ferromagnetism happens when neighboring electron spins prefer to align, creating long-range magnetic order and magnetic domains. The strength of a permanent magnet is limited by how fully the spins can align, typically up to about 0.8 tesla (8,000 gauss). Stronger fields can be made with electromagnets, which use electrical current and special equipment to reach tens of teslas in research settings. This demonstrates how macroscopic magnetic behavior arises from quantum properties of atoms and how material limits and external devices influence magnetic strength.

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Generate a single crystal structure for a given composition using OMatG.

Generate completely novel crystal structures using OMatG. The model samples atomic species, positions, and lattice vectors from scratch to create new materials.

Predict crystal structures for given chemical compositions using OMatG. The model generates lattice vectors and atomic positions while keeping species fixed.

OMatG is a generative model for crystal structure prediction (CSP) and de novo generation of inorganic materials.

Determining whether low-energy P1 structures hide higher-symmetry configurations and if more sampling could find them. The team ran three experiments to see why many of the lowest-energy structures end up in P1 (triclinic) symmetry and whether better structures exist. They found that the main problem is not hidden symmetry in P1, but too little sampling: only about 15 trials per formula leaves much of the energy landscape unexplored. Overall, increasing trials and sampling breadth can reveal better, more stable phases.

Gold memberships are back on Ouro. This is just the starting point, and the perks will grow over time.

WTI crude is set for a modest grind higher, with prices forecast to reach $61.23/bbl by 2026-04-12 (up 3.1% from $59.39 on 2026-01-18) as the market shifts from a fading Iran war-premium spike to a tighter—but not crisis-level—fundamental balance. Rather than a new bull trend, this move reflects a gradual rebuilding of a moderate geopolitical risk premium and steady underlying demand tightening, which together anchor WTI in the low-$60s over the next three months.

Gold futures are poised for a controlled grind higher rather than a breakout, with prices expected to rise about 2.0% from 4,604.3 (18 Jan 2026) to 4,698.7 by 12 Apr 2026 as higher-for-longer US real yields cap upside but fail to trigger a deeper correction. With most hawkish Fed repricing already absorbed and gold holding near record levels, the balance of risks tilts modestly bullish, underpinned by sticky central‑bank and strategic demand amid structurally elevated geopolitical and fiat‑credibility concerns.

Forecasts for Crude Oil Price (WTI) with 12-period horizon (weekly)

Forecasts for Gold Futures with 12-period horizon (weekly)

Forecasts for Silver Futures with 12-period horizon (weekly)

Convert between image formats (PNG, JPG, WEBP)

while maintaining its current format

Extract audio track from MP4 video to MP3 or WAV format

Compress an MP4 video using FFmpeg with quality settings

Download a specific time range from a YouTube video as an MP4 clip

File operation and conversion endpoints

Forecasts for Crude Oil Price (WTI) with 52-period horizon (weekly)

Forecasts for Gold Futures with 52-period horizon (weekly)

Calculate Magnetic Anisotropy Energy (MAE) using DFT

🚀 The 7-Day Price Volatility Dataset: Market Dynamics Tracker 🔍 Dataset Description & Fields This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability. Interpretation: A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply. A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events. A value close to signifies strong price stability. 💡 Advanced Analytical Uses The simple structure of this data facilitates a wide range of analytical tasks: Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window. Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change. Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change. Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions. First time time it was scraped at 02-12-2025 and next time at 09-12-2025.

🚀 The 7-Day Price Volatility Dataset: Market Dynamics Tracker 🔍 Dataset Description & Fields This dataset is designed to capture short-term market liquidity and volatility by comparing the initial price of an item to its price exactly one week later. It is a powerful tool for analyzing pricing strategies, predicting short-term market trends, and identifying products with significant price instability. Interpretation: A positive percentage (e.g., ) indicates the item has experienced appreciation (price increase) over the week, potentially signaling rising demand or reduced supply. A negative percentage (e.g., ) indicates depreciation (price decrease), possibly due to overstocking, new competition, or sales events. A value close to signifies strong price stability. 💡 Advanced Analytical Uses The simple structure of this data facilitates a wide range of analytical tasks: Market Timing: Determine the optimal days or conditions under which prices are most likely to drop (or surge) over a 7-day window. Product Segmentation: Group products by their volatility index to understand which market segments are most sensitive to change. Pricing Strategy Audit: Assess the effectiveness of promotional campaigns by observing the seven-day rebound or stabilization after an initial price change. Risk Assessment: Use the change percentage as a risk indicator for inventory management or investment decisions. First time time it was scraped at 01-12-2025 and next time at 08-12-2025.

Forecasts for Bitcoin Price with 52-period horizon (weekly)

Forecasts for Crude Oil Price (WTI) with 52-period horizon (weekly)

Forecasts for Copper Price with 12-period horizon (monthly)

Forecasts for Silver Futures with 12-period horizon (monthly)

Forecasts for Gold Futures with 12-period horizon (monthly)

Observed and forecasted housing market data for December 2025 (monthly).

HubSpot Zimbabwe Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Zimbabwe. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

HubSpot Zambia Partner Companies Dataset (Scraped & Structured) This dataset contains clean, structured information scraped from public HubSpot Partner listings for companies operating within Zambia. It is designed to support research, analytics, automation workflows, market mapping, and CRM enrichment. 📌 Dataset Includes Company identifiers (ID, slug, name) Partner tier & partner type Website URLs Public descriptions Logo URLs Ratings & review counts Budget ranges (when available) Locations & regions Languages supported Catalog services offered Industries served Credential and accreditation IDs 🧩 Useful For Market analysis & segmentation Lead scoring and enrichment Competitive mapping Workflow automation & RPA pipelines Data-driven decision-making Building internal tools or dashboards ⚠️ Responsible Use Notice This dataset contains publicly available business information only. It must not be used for: Unsolicited spam campaigns Misrepresentation or harmful automation Violating HubSpot's terms of service or any platform's policies Use this dataset ethically and in compliance with all applicable laws.

API for first-principles calculations and properties

Interactive browser visualizations for materials science, by @janosh