What is missing in autonomous discovery: Open challenges for the community

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

The sustainable materials roadmap

Over the past 150 years, our ability to produce and transform engineered materials has been responsible for our current high standards of living, especially in developed economies. However, we must carefully think of the effects our addiction to creating and using materials at this fast rate will have on the future generations. The way we currently make and use materials detrimentally affects the planet Earth, creating many severe environmental problems. It affects the next generations by putting in danger the future of the economy, energy, and climate. We are at the point where something must drastically change, and it must change now. We must create more sustainable materials alternatives using natural raw materials and inspiration from nature while making sure not to deplete important resources, i.e. in competition with the food chain supply. We must use less materials, eliminate the use of toxic materials and create a circular materials economy where reuse and recycle are priorities. We must develop sustainable methods for materials recycling and encourage design for disassembly. We must look across the whole materials life cycle from raw resources till end of life and apply thorough life cycle assessments (LCAs) based on reliable and relevant data to quantify sustainability. We need to seriously start thinking of where our future materials will come from and how could we track them, given that we are confronted with resource scarcity and geographical constrains. This is particularly important for the development of new and sustainable energy technologies, key to our transition to net zero. Currently 'critical materials' are central components of sustainable energy systems because they are the best performing. A few examples include the permanent magnets based on rare earth metals (Dy, Nd, Pr) used in wind turbines, Li and Co in Li-ion batteries, Pt and Ir in fuel cells and electrolysers, Si in solar cells just to mention a few. These materials are classified as 'critical' by the European Union and Department of Energy. Except in sustainable energy, materials are also key components in packaging, construction, and textile industry along with many other industrial sectors. This roadmap authored by prominent researchers working across disciplines in the very important field of sustainable materials is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the sustainable materials community. In compiling this roadmap, we hope to aid the development of the wider sustainable materials research community, providing a guide for academia, industry, government, and funding agencies in this critically important and rapidly developing research space which is key to future sustainability.journal articl

Border Insecurity: Reading Transnational Environments in Jim Lynch’s Border Songs

This article applies an eco-critical approach to contemporary American fiction about the Canada-US border, examining Jim Lynch’s portrayal of the British Columbia-Washington borderlands in his 2009 novel Border Songs. It argues that studying transnational environmental actors in border texts—in this case, marijuana, human migrants, and migratory birds—helps illuminate the contingency of political boundaries, problems of scale, and discourses of risk and security in cross-border regions after 9/11. Further, it suggests that widening the analysis of trans-border activity to include environmental phenomena productively troubles concepts of nature and regional belonging in an era of climate change and economic globalization. Cet article propose une lecture écocritique de la fiction étatsunienne contemporaine portant sur la frontière entre le Canada et les États-Unis, en étudiant le portrait donné par Jim Lynch de la région frontalière entre la Colombie-Britannique et Washington dans son roman Border Songs, paru en 2009. L’article soutient que l’étude, dans les textes sur la frontière, des acteurs environnementaux transnationaux – dans ce cas-ci, la marijuana, les migrants humains et les oiseaux migratoires – jette un jour nouveau sur la contingence des limites territoriales politiques, des problèmes d’échelle et des discours sur le risque et la sécurité des régions transfrontalières après les évènements du 11 septembre 2001. Il suggère également qu’en élargissant l’analyse de l’activité transfrontalière pour y inclure les phénomènes environnementaux, on brouille de façon productive les concepts de nature et d’appartenance régionale d’une époque marquée par les changements climatiques et la mondialisation de l’économie

Building a “Hello World” for self-driving labs: The Closed-loop Spectroscopy Lab Light-mixing demo

Summary: Learn how to build a Closed-loop Spectroscopy Lab: Light-mixing demo (CLSLab:Light) to perform color matching via RGB LEDs and a light sensor for under 100 USD and less than an hour of setup. Our tutorial covers ordering parts, verifying prerequisites, software setup, sensor mounting, testing, and an optimization algorithm comparison tutorial. We use secure IoT-style communication via MQTT, MicroPython firmware on a pre-soldered Pico W microcontroller, and the self-driving-lab-demo Python package. A video tutorial is available at https://youtu.be/D54yfxRSY6s.For complete details on the use and execution of this protocol, please refer to Baird et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Build instructions for Closed-loop Spectroscopy Lab: Light-mixing Demo

Closed-loop Spectroscopy Lab: Light-mixing Demo (CLSLab:Light) is a teaching and prototyping platform for autonomous scientific discovery. It consists of a set of LEDs and a light sensor while encapsulating key principles for "self-driving" (i.e., autonomous) research laboratories, including sending commands, receiving sensor data, physics-based simulation, and advanced optimization. CLSLab:Light is a "Hello, World!" introduction to these topics, accessible by students, educators, hobbyists, and researchers for less than 100 USD, a small footprint, and under an hour of setup time

janosh/pymatviz: v0.5.3

What's Changed <ul> <li>Add Ruff pre-commit hook by @janosh in <a href="https://github.com/janosh/pymatviz/pull/68">https://github.com/janosh/pymatviz/pull/68</a></li> <li>Pyproject by @janosh in <a href="https://github.com/janosh/pymatviz/pull/69">https://github.com/janosh/pymatviz/pull/69</a></li> <li>0f2386a scatter_density() use x, y args as axis labels if strings</li> <li>c550332 rename add_mae_r2_box() to annotate_mae_r2()</li> <li>7da3c0c use redirect in layout.ts instead of ugly DOM href surgery to forward readme links to GH repo</li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/janosh/pymatviz/compare/v0.5.2...v0.5.3">https://github.com/janosh/pymatviz/compare/v0.5.2...v0.5.3</a></p&gt

janosh/pymatviz: v0.3.0

What's Changed <ul> <li>python-requires>=3.8 by @sgbaird in <a href="https://github.com/janosh/ml-matrics/pull/18">https://github.com/janosh/ml-matrics/pull/18</a></li> <li>Add <code>plot_structure_2d()</code> in new module <code>ml_matrics/struct_vis.py</code> in <a href="https://github.com/janosh/ml-matrics/pull/20">https://github.com/janosh/ml-matrics/pull/20</a><ul> <li>add ml_matrics/struct_vis.py with ase-inspired plot_structure_2d()</li> <li>plot_structure_2d() add annotate_sites: bool = True</li> <li>add example structure plots to readme</li> <li>fix GH workflow svgo compression</li> <li>rename annotate_sites kwarg to site_labels, can be dict or list for custom labels</li> <li>fix test_plot_structure_2d()</li> <li>add plot_structure_2d() example with 20 random MP structures</li> <li>plot_structure_2d() drop kwargs offset, bbox, maxwidth, simplifies function, add label_kwargs</li> <li>assert matplotlib compare_images() passes in test_plot_structure_2d()</li> <li>try fix compare_images() in CI by setting explicit plt figsize</li> <li>move save_fixture() to new tests/_helpers.py along with stuff in tests/<strong>init</strong>.py</li> <li>add convenience root import for plot_structure_2d() + comment crediting ASE</li> </ul> </li> <li>better handling of atomic numbers in count_elements() when outside range [1, 118] e46b2c4</li> <li>git mv data/{mp-n_elements\<2,mp-elements}.csv (closes #19) ad6197e</li> <li>support atomic numbers in count_elements(), only element symbols before, add kwarg text_color in ptable_heatmap ada57cc</li> <li>add kwargs {pre,suf}fix in add_mae_r2_box(), use pip cache in publish.yml 6f64c3b</li> </ul> New Contributors <ul> <li>@sgbaird made their first contribution in <a href="https://github.com/janosh/ml-matrics/pull/18">https://github.com/janosh/ml-matrics/pull/18</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/janosh/ml-matrics/compare/v0.2.6...v0.3.0">https://github.com/janosh/ml-matrics/compare/v0.2.6...v0.3.0</a></p&gt

janosh/pymatviz: v0.6.0

What's Changed <ul> <li>Add Ruff pre-commit hook by @janosh in <a href="https://github.com/janosh/pymatviz/pull/68">https://github.com/janosh/pymatviz/pull/68</a></li> <li>Pyproject by @janosh in <a href="https://github.com/janosh/pymatviz/pull/69">https://github.com/janosh/pymatviz/pull/69</a></li> <li>0f2386a scatter_density() use x, y args as axis labels if strings</li> <li>c550332 rename add_mae_r2_box() to annotate_mae_r2()</li> <li>7da3c0c use redirect in layout.ts instead of ugly DOM href surgery to forward readme links to GH repo</li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/janosh/pymatviz/compare/v0.5.2...v0.6.0">https://github.com/janosh/pymatviz/compare/v0.5.2...v0.6.0</a></p&gt

janosh/pymatviz: v0.5.2

What's Changed <ul> <li>b782ec4 v0.5.2</li> <li>25a80a9 add save_fig() to pymatviz/utils.py covered by test_save_fig()</li> <li>1b05792 add assets/make_api_docs.py</li> <li>e7cc488 remove google colab compat notices from example notebooks</li> <li>b27af1c Deploy demo site to GitHub pages (#64)</li> <li>531133c add citation.cff</li> <li>396bdf4 update examples/mp_bimodal_e_form.ipynb with MP r2SCAN beta release formation energies</li> <li>71f861a Configure <code>devcontainer</code> for running notebooks in Codespace (#63)</li> <li>a90c437 Customizable parity stats (#61)</li> <li>25d700c add docformatter pre-commit hook</li> <li>64b545d Support dataframes in <code>true_pred_hist()</code> (#60)</li> <li>6501627 Support dataframes in relevance and uncertainty plots (#59)</li> <li>40e9530 Allow passing in dataframes and x, y as column names in parity plots (#58)</li> <li>f81bd3e plot_structure_2d() doc str add "multiple structures in single figure example" (#57)</li> <li>5c8ccbe residual_hist() remove args y_true, y_pred, now takes y_res directly</li> <li>4335001 Revert "Python 3.7 support (#55)"</li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/janosh/pymatviz/compare/v0.5.1...v0.5.2">https://github.com/janosh/pymatviz/compare/v0.5.1...v0.5.2</a></p&gt