Phase diagram of TiO2; e_above_hull: 0.190601 eV/atom; predicted_stable: False

(Space group: P1 #1, Crystal system: triclinic, Point group: 1)

Supercell 3x3x3 of Fe4N (Space group: P4/mmm, 432 symmetry operations)

Phase diagram of Fe4N; e_above_hull: 0.095540 eV/atom; predicted_stable: False

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -41.9630 eV; energy change = -0.0786 eV; symmetry: P4/mmm → P4/mmm

(Space group: P4/mmm #123, Crystal system: tetragonal, Point group: 4/mmm)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -169.4704 eV; energy change = -146.2408 eV; symmetry: P1 → P1

(Space group: P1 #1, Crystal system: triclinic, Point group: 1)

Phase diagram of Fe5N; e_above_hull: 0.179151 eV/atom; predicted_stable: False

(Space group: C2/m #12, Crystal system: monoclinic, Point group: 2/m)

Phase diagram of Fe3Ni; e_above_hull: 0.090971 eV/atom; predicted_stable: False

(Space group: Pmmm #47, Crystal system: orthorhombic, Point group: mmm)

(Space group: P4/mmm #123, Crystal system: tetragonal, Point group: 4/mmm)

Phase diagram of FeNi; e_above_hull: 0.000000 eV/atom; predicted_stable: True

FeNi (Space group: P4/mmm #123, Crystal system: tetragonal, Point group: 4/mmm)

2 unique crystal structures for composition FeNi

4 unique crystal structures for composition Fe12Bi2S

Phase diagram of Fe6N; e_above_hull: 0.272291 eV/atom; predicted_stable: False

Supercell 2x2x2 of Fe6N (Space group: P1, 8 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -114.7984 eV; energy change = -1.2879 eV; symmetry: P1 → P1

Phase diagram of Ba2YCu3O7; e_above_hull: 0.024753 eV/atom; predicted_stable: False

Supercell 2x2x2 of TiO2 (Space group: P-3m1, 96 symmetry operations)

Phase diagram of TiO2; e_above_hull: 0.190769 eV/atom; predicted_stable: False

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -26.3454 eV; energy change = -0.0003 eV; symmetry: P-3m1 → P-3m1

(Space group: P-3m1 #164, Crystal system: trigonal, Point group: -3m)

This paper introduces a model, named Chemeleon, designed to generate chemical compositions and crystal structures by learning from both textual descriptions and three-dimensional structural data.

Phase diagram of Fe16N3; e_above_hull: 0.224104 eV/atom; predicted_stable: False

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -144.1474 eV; energy change = -0.1651 eV; symmetry: I4/mmm → I4/mmm

Fe16N2 (Space group: I4/mmm #139, Crystal system: tetragonal, Point group: 4/mmm)

Phase diagram of Fe8N; e_above_hull: 0.317927 eV/atom; predicted_stable: False

Fe16N2 (Space group: I4/mmm #139, Crystal system: tetragonal, Point group: 4/mmm)

4 generated crystal structures for the chemical system Y-Ba-Cu-O

Phase diagram of TiO2; e_above_hull: 0.003695 eV/atom; predicted_stable: False

Phase diagram of Fe4Bi5S3; e_above_hull: 0.345894 eV/atom; predicted_stable: False

Phase diagram of Fe3GeH; e_above_hull: 0.015549 eV/atom; predicted_stable: False

Phase diagram of FeBiS2; e_above_hull: 0.136538 eV/atom; predicted_stable: False

Phase diagram of Fe3Ir; e_above_hull: 0.028427 eV/atom; predicted_stable: False

Relaxed with Orb v3; 0.01 eV/Å threshold; final energy = -107.6264 eV; energy change = -0.0026 eV; symmetry: I4_1/amd → I4_1/amd

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -107.6264 eV; energy change = -0.0026 eV; symmetry: I4_1/amd → I4_1/amd

Supercell 3x3x3 of Fe3GeH (Space group: Pm-3m, 1296 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -32.9520 eV; energy change = 0.0000 eV; symmetry: Pm-3m → Pm-3m

Supercell 3x3x3 of Fe3Ir (Space group: R-3m, 972 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -100.8543 eV; energy change = 0.0000 eV; symmetry: R-3m → R-3m

4 generated crystal structures with magnetic density 0.15

Supercell 4x4x4 of FeBiS2 (Space group: P2_1/m, 256 symmetry operations)

Supercell 2x2x2 of FeBiS2 (Space group: P2_1/m, 32 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -42.9355 eV; energy change = -0.1362 eV; symmetry: P2_1/m → P2_1/m

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -193.6525 eV; energy change = -0.7593 eV; symmetry: R3 → R3

Generated crystal structures for Fe-Bi-S - with corrected symmetry

MatterGen generated crystal structures for Fe-Bi-B - with corrected symmetry

(P1 symmetry - symmetry correction failed)

(P1 symmetry - symmetry correction failed)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -67.0941 eV; energy change = -0.0037 eV; symmetry: P1 → P1

Supercell 4x4x4 of NaCl (Space group: P-6m2, 768 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -6.6382 eV; energy change = 0.0000 eV; symmetry: P-6m2 → P-6m2

(Space group: P-6m2 #187, Crystal system: hexagonal, Point group: -6m2)

(Space group: P6/mmm #191, Crystal system: hexagonal, Point group: 6/mmm)

(Space group: P-6m2 #187, Crystal system: hexagonal, Point group: -6m2)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -149.8910 eV; energy change = -0.6824 eV; symmetry: P1 → P1

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -130.0506 eV; energy change = -0.1019 eV; symmetry: P1 → P1

Supercell 3x3x3 of Fe6Co2N (Space group: I4/mmm, 864 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -141.9409 eV; energy change = -0.5000 eV; symmetry: I4/mmm → Cm

Fe12Co4N2 (Space group: I4/mmm #139, Crystal system: tetragonal, Point group: 4/mmm)

Supercell 3x3x3 of Fe6Co2N (Space group: P1, 54 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -140.0058 eV; energy change = -15.5831 eV; symmetry: C2/m → P1

Supercell 4x4x4 of FeNi (Space group: P4/mmm, 1024 symmetry operations)

MatterGen generated crystal structures for Fe-Bi-S

Supercell 2x2x2 of Fe5Bi (Space group: P1, 8 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -43.3359 eV

MatterGen generated crystal structures for Fe-Bi-N

Supercell 2x2x2 of Fe13N5 (Space group: P1, 8 symmetry operations)

MatterGen generated crystal structures for Fe-N

Generated crystal structure for Fe-N

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -143.6773 eV

Supercell 2x2x2 of Ba2YCu3O7 (Space group: Pmmm, 64 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -79.7708 eV

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -125.0215 eV

Supercell 2x2x2 of Fe6N (Space group: Cm, 32 symmetry operations)

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -111.5653 eV

Supercell 2x2x2 of FeBiB (Space group: P-6m2, 96 symmetry operations)

Supercell 3x3x3 of CBiFe4S

Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -44.2005 eV

Forecasts for Bitcoin Price with 12-period horizon

Forecasts for Oil Price with 12-period horizon

Forecasts for Gold Price with 12-period horizon

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn–Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate machine learning models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moiré twisted bilayer heterostructure, and metal-organic frameworks. Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GNoME datasets, identifying 3940 crystals with direct band gaps and 5109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible. This paper corresponds to HamGNN v1 and the universal model weights released in 2024. https://iopscience.iop.org/article/10.1088/0256-307X/41/7/077103

The accurate modeling of spin-orbit coupling (SOC) effects in diverse complex systems remains a significant challenge due to the high computational demands of density functional theory (DFT) and the limited transferability of existing machine-learning frameworks. This study addresses these limitations by introducing Uni-HamGNN, a universal SOC Hamiltonian graph neural network that is applicable across the periodic table. By decomposing the SOC Hamiltonian into spin-independent and SOC correction terms, our approach preserves SU(2) symmetry while significantly reducing parameter requirements. Based on this decomposition, we propose a delta-learning strategy to separately fit the two components, thereby addressing the training difficulties caused by magnitude discrepancies between them and enabling efficient training. The model achieves remarkable accuracy (mean absolute error of 0.0025 meV for the SOC-related component) and demonstrates broad applicability through high-throughput screening of the GNoME dataset for topological insulators, as well as precise predictions for 2D valleytronic materials and transition metal dichalcogenide (TMD) heterostructures. This breakthrough eliminates the need for system-specific retraining and costly SOC-DFT calculations, paving the way for rapid discovery of quantum materials.

~1900 materials collected from NovoMag and Novamag datasets, cleaned CSV with CIF file and MAE value

This paper describes the open Novamag database that has been developed for the design of novel Rare-Earth free/lean permanent magnets. The database software technologies, its friendly graphical user interface, advanced search tools and available data are explained in detail. Following the philosophy and standards of Materials Genome Initiative, it contains significant results of novel magnetic phases with high magnetocrystalline anisotropy obtained by three computational high-throughput screening approaches based on a crystal structure prediction method using an Adaptive Genetic Algorithm, tetragonally distortion of cubic phases and tuning known phases by doping.

This dataset includes 3819 materials scraped from https://magmat.herokuapp.com/, the Magnetic Materials Database. See https://www.novomag.physics.iastate.edu/structure-database for citations and more resources. I've cleaned the dataset to include the available magnetic materials (in CIF format) and their properties: magnetic_ordering, total_magnetic_moment [μ_B/cell], averaged_magnetic_moment [μ_B/atom], magnetic_polarization [T], formation_energy_(vs._elemental_phases) [meV/atom], formation_energy_above_hull [meV/atom], magnetic_curie_temperature [K], magnetic_anisotropy_constant,_k_^a-c [MJ/m^3], magnetic_easy_axis, magnetic_hardness_parameter,_κ, magnetic_anisotropy_constant,_k_^b-c [MJ/m^3], magnetic_anisotropy_constant,_k_^b-a [MJ/m^3], magnetic_anisotropy_constant,_k_^d-a [MJ/m^3]

A Computational High-Throughput Study Lorenzo A. Mariano, Vu Ha Anh Nguyen, Valerio Briganti, and Alessandro Lunghi Journal of the American Chemical Society 2024 146 (49), 34158-34166 DOI: 10.1021/jacs.4c14076

Interactive phase diagram showing stability of ZrFe12Si2B

Interactive phase diagram showing stability of ZrFe12Si2B

About 250mb when unzipped. Columns are mp-id and Orb v2 features of the material at ground state. Generated with https://github.com/ourofoundation/materials/blob/main/experiments/magnetism/dim-red/features_dataset.py

Forecasts for Gold Price with 52-period horizon

Forecasts for Bitcoin Price with 52-period horizon

, a ferrimagnetic material from Materials Project. https://next-gen.materialsproject.org/materials/mp-21666

CIF file for ZrFe12Si2B, a ferrimagnetic materials from Materials Project. https://next-gen.materialsproject.org/materials/mp-653838

This dataset has a set of 34,000 ferro/ferrimagnetic materials from Materials Project, their formula, if they include rare earth elements, magnetic moment, volume, magnetic density, a predicted Curie temperature, and cosine distances to some known permanent magnets like NdFeB. Distances are based on a 256 dimension embedding from Orb v2 latent space.

Forecasts for Gold Price with 52-period horizon

Forecasts for Copper Price with 52-period horizon

Observed and forecasted housing market data for April 2025. Includes monthly data and forecasts projecting 12 months into the future.

A 3D interactive scatter plot of magnetic materials from Materials Project. Points are colored by the materials estimated magnetic density and poitioned by UMAP reduction of Orb v2 model latent space

A collection of 5020 magnetic materials from Materials Project, with estimated magnetic density and predicted Curie temperatures.

Forecasted fred-cbbtcusd-festive-ride from 2025-04-12 to 2025-12-30

Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Forecasted fred-cbbtcusd-tender-shirley from 2025-04-09 to 2025-12-30

Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

This is a first draft of a compiled Curie temperature dataset mapping crystal structure (from Materials Project) to Curie temperature. Builds on the work of https://github.com/Songyosk/CurieML. Dataset includes ~6,800 unique materials representing 3,284 unique chemical families.

The crystal structure of a neodymium magnet. It is a permanent magnet made from an alloy of neodymium, iron, and boron to form the Nd2Fe14B tetragonal crystalline structure. They are the most widely used type of rare-earth magnet.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, the authors explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases.

Atomistic modelling of magnetic materials provides unprecedented detail about the underlying physical processes that govern their macroscopic properties, and allows the simulation of complex effects such as surface anisotropy, ultrafast laser-induced spin dynamics, exchange bias, and microstructural effects. Here the authors present the key methods used in atomistic spin models which are then applied to a range of magnetic problems. They detail the parallelization strategies used which enable the routine simulation of extended systems with full atomistic resolution.

Forecasted fred-cbbtcusd-nervous-feynman from 2025-03-14 to 2025-12-30

Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Generated image from "Crowded dance floor seen from above, with clusters of dancers all performing identical synchronized movements within their groups. The dance moves are visibly spreading from dancer to dancer like a wave, with clear boundaries between different dance styles." using DALL-E 3 from OpenAI.

Generated image from "A time-lapse of a stadium doing increasingly energetic waves. In the first frame, a perfect grid of glowing points shows almost perfect alignment. As the wave intensifies in subsequent frames, the points become increasingly chaotic and misaligned, eventually showing completely random orientations at the height of the wave's energy." using DALL-E 3 from OpenAI.

Generated image from "A bookshelf with various books - thin paperbacks laying flat, tall encyclopedias standing upright, and a few books precariously balanced on their edges or covers. An invisible force appears to be trying to rotate the books, with the encyclopedias strongly resisting the rotation while the paperbacks easily change orientation." using DALL-E 3 from OpenAI.

Generated image from "A political map showing a country divided into distinct districts, each colored either red or blue. Some areas show large unified blocks of a single color, while boundaries between differently colored regions are clearly visible. A giant hand is holding a magnet above the map, causing more districts to align to the same color" using DALL-E 3 from OpenAI.

Generated image from "Only visualize this idea. No text. Imagine a dance floor with a simple rule: dancers (electrons) with the same moves (spins) need more space between them due to social etiquette (Pauli exclusion principle). In ferromagnetic materials: When two dancers meet, it's energetically favorable for them to dance the same way (parallel spins) As one dancer starts doing a specific move, nearby dancers naturally follow along This creates "dance neighborhoods" (magnetic domains) where everyone is synchronized The "dance style" spreads from one dancer to the next - this propagation is the exchange interaction. Some dance floors (crystal structures) naturally encourage everyone to dance the same way, creating strong magnets." using DALL-E 3 from OpenAI.

Generated image from "A stadium filled with people, each holding a flashlight. In a magnet, something special happens - everyone agrees to point their flashlights in the same direction. Suddenly, that side of the stadium becomes brilliantly bright. This coordinated alignment is what creates a magnet's strength. Each flashlight is like an electron's magnetic moment, and when aligned, they create a powerful cumulative effect." using DALL-E 3 from OpenAI.

Generated image from "Imagine a stadium filled with people, each holding a flashlight. In normal materials, people are pointing their flashlights in random directions, so the overall stadium appears dim from above because the light is scattered in all directions." using DALL-E 3 from OpenAI.

This paper presents MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, the authors introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice.

This paper introduces LLaDA, a diffusion model trained from scratch under the pre-training and supervised finetuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. https://arxiv.org/abs/2502.09992

Domains registered within days of this post. Not a dataset - just a wordlist

Here we showcase a few key latent features and their relationships to each other, points colored by their Tc. We animate from 0 K to ~130 K.

Superconductor candidates sampled at a target Tc of 130k

Generated image from "A hairy frog" using DALL-E 3 from OpenAI.

Generated model from an image using the StabilityAI API.

Generated image from "A marble sculpture of a human male with white background" using the StabilityAI API.

Evaluation results for the MatterGen fine-tuned model candidates, with new superconducting families labeled.

400 .cif files of candidate structures property condition generated by MatterGen where tc = 298.15K

3DSC dataset grouped by chemical composition, with Tc as our target. For use with MatterGen and the chemical system sampling.

Interactive plot of predicted vs. true Tc on the evaluation set.

Visualizing the counts of materials in the training and evaluation dataset by their Tc. First bin is non-superconductors, the rest are ranges of 20 K increments.

Using the 256 dimensional latent space output from the Orb model, we visualize the 3DSC(MP) dataset using UMAP with direction from Tc labels. Hover a point to see Tc, formula, and Material Project identifier.

Using the 256 dimensional latent space output from the Orb model, we visualize the 3DSC(MP) dataset using t-SNE and UMAP. The UMAP projection has been given the target for learning a manifold that keeps similar Tc materials close together.

Authors introduce Orb, a family of universal interatomic potentials for atomistic modeling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. https://arxiv.org/abs/2410.22570

Authors present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures up to 1000 GPa. https://arxiv.org/abs/2405.04967

Not exactly the most rigorous test, but this side-by-side comparison shows the difference between running MD locally (M2 Macbook Air) and on a proper server (g4dn.2xl with T4 GPU). Each log is actually 100 simulation steps too.

Molecular dynamics simulation temperature ramping NaCl 3x3x3 supercell from 0 K to 300 K

Molecular dynamics simulation temperature ramping H2O 3x3x3 supercell from 0 K to 300 K

Simulating ice into water

https://next-gen.materialsproject.org/materials/mp-697111

Langevin temperature ramp over 10ps from 0 K to 300 K on a 3x3x3 supercell of NaCl

Langevin temperature ramp over 10ps from 0 K to 300 K on a 3x3x3 supercell of NaCl

https://next-gen.materialsproject.org/materials/mp-22851

Visualization created using .traj outputs of ASE MD simulation

Since the announcement in 2011 of the Materials Genome Initiative by the Obama administration, much attention has been given to the subject of materials design to accelerate the discovery of new materials that could have technological implications. Although having its biggest impact for more applied materials like batteries, there is increasing interest in applying these ideas to predict new superconductors. This is obviously a challenge, given that superconductivity is a many body phenomenon, with whole classes of known superconductors lacking a quantitative theory. Given this caveat, various efforts to formulate materials design principles for superconductors are reviewed here, with a focus on surveying the periodic table in an attempt to identify cuprate analogues. https://arxiv.org/abs/1601.00709

Tibetan-style vajra/dorje. Based on references from https://www.himalayanart.org/search/set.cfm?setID=563. https://sketchfab.com/3d-models/vajra-1bdaddab069f424a95ad9a9a9c8c17ed

Authors make use of a new optical device to drive metallic K3C60 with mid-infrared pulses of tunable duration, ranging between one picosecond and one nanosecond. The same superconducting-like optical properties observed over short time windows for femtosecond excitation are shown here to become metastable under sustained optical driving, with lifetimes in excess of ten nanoseconds. https://www.nature.com/articles/s41567-020-01148-1

The study of superconductivity in compressed hydrides is of great interest due to measurements of high critical temperatures (Tc) in the vicinity of room temperature, beginning with the observations of LaH10 at 170-190 GPa. However, the pressures required for synthesis of these high Tc superconducting hydrides currently remain extremely high. Here we show the investigation of crystal structures and superconductivity in the La-B-H system under pressure with particle-swarm intelligence structure searches methods in combination with first-principles calculations. https://arxiv.org/abs/2107.02553

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). https://arxiv.org/abs/2005.07609

Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. Chi Chen & Shyue Ping Ong https://www.nature.com/articles/s43588-022-00349-3 Preprint version from arXiv

This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.

For 4000 “low-Tc” superconductors (i.e., non-cuprate and non-iron-based), Tc is plotted vs. the a) average atomic weight, b) average covalent radius, and c) average number of d) valence electrons. Having low average atomic weight and low average number of d) valence electrons are necessary (but not sufficient) conditions for achieving high Tc in this group. d) Scatter plot of Tc for all known superconducting cuprates vs. the mean number of unfilled orbitals. c), d) suggest that the values of these predictors lead to hard limits on the maximum achievable Tc

a) Histogram of materials categorized by Tc (bin size is 2 K, only those with finite Tc are counted). Blue, green, and red denote low-Tc, iron-based, and cuprate superconductors, respectively. In the inset: histogram of materials categorized by ln(Tc) restricted to those with Tc > 10 K. b) Performance of different classification models as a function of the threshold temperature (Tsep) that separates materials in two classes by Tc. Performance is measured by accuracy (gray), precision (red), recall (blue), and F1 score (purple). The scores are calculated from predictions on an independent test set, i.e., one separate from the dataset used to train the model. In the inset: the dashed red curve gives the proportion of materials in the above-Tsep set. c) Accuracy, precision, recall, and F1 score as a function of the size of the training set with a fixed test set. d) Accuracy, precision, recall, and F1 as a function of the number of predictors

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. https://www.nature.com/articles/s41524-018-0085-8

Machine-learned force fields have transformed the atomistic modeling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials.

The 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable method for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 2 x 107 hybrid DFT level of theory quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with “exotic” element-organic bonding.

Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. https://www.nature.com/articles/s42256-023-00716-3

This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. https://www.nature.com/articles/s41467-022-29939-5

Training in 1.58b With No Gradient Memory. Preprint paper by wbrickner

Forecasted yfinance-btc-usd-quirky-hertz from 2024-12-22 to 2024-12-31

Dataset BTC-USD downloaded from yfinance: 2019-12-01 to present

Forecasted yfinance-btc-usd-sad-hopper from 2024-12-22 to 2024-12-31

Dataset BTC-USD downloaded from yfinance: 2019-01-01 to present

Forecasted yfinance-btc-usd-adoring-chandrasekhar from 2024-12-19 to 2024-12-31

Dataset BTC-USD downloaded from yfinance: 2019-01-01 to present