just added:
> For forest monitoring, one wishes to infer the distribution among tree species in an area blended into a single pixel in an aerial image \cite{malcolm}.
> Thanks to Tyson Wepprich for pointing out the related inverse blending problem for tree distribution mapping in ecology.
ok with u?

oh very interesting! so the pixel covers an area containing many trees of different species. the pixel’s shade of green reflects the blend of species in that area. you have a paper on this?

reverse-engineering a wine blend.

🍷 new preprint on reverse-engineering a wine blend! feedback wanted.

❓given a wine blend and samples of each pure-varietal wine that went into it, can we infer the unknown make-up of the blend based on chemical fingerprints?

(~ a tutorial on total least squares.)

🔗 chemrxiv.org/doi/full/10....

Adaptive, Bayesian Experimental Design to Efficiently Determine the Critical Micelle Concentration of a Surfactant Surfactants are widely used for industrial applications, yet more environmentally friendly surfactants with enhanced properties are demanded. A key thermodynamic property governing the behavior of a surfactant in an aqueous solution is its critical micelle concentration (CMC). Below the CMC, increasing the surfactant concentration reduces the surface tension of the solution; above the CMC, the water–air interface becomes saturated with adsorbed surfactant, leading excess surfactant to self-assemble into micelles and the surface tension to plateau. Many physicochemical properties of a surfactant solution exhibit sharp changes at the CMC. The conventional experimental protocol to determine the CMC of a surfactant is labor-intensive and time-consuming: (1) prepare many surfactant solutions spanning a wide concentration range and then (2) measure the surface tension of each solution. Herein, we adopt Bayesian experimental design (BED) to determine the CMC of a surfactant more efficiently─even without prior knowledge of its order of magnitude. BED follows an experiment-model-design feedback loop: (1) prepare a surfactant solution and measure its surface tension; (2) use all surface tension data thus far to obtain a posterior distribution over thermodynamic models of the surface tension isotherm of the surfactant; and (3) pick the surfactant concentration for the next experiment to maximize expected information gain about the CMC. We show that BED efficiently gathers information about the CMC using two surfactants (octyl-β-d-thioglucopyranoside and Triton X-100) as test cases. Broadly, BED can reduce the time, effort, cost, and chemical waste to determine the CMC of surfactants and drive an autonomous laboratory for surfactant discovery and characterization.

check out our new paper on Bayesian experimental design for surfactant characterization!

pubs.acs.org/doi/10.1021/...

‘Reimagining matter’: Nobel laureate invents machine that harvests water from dry air Omar Yaghi’s invention uses ambient thermal energy and can generate up to 1,000 litres of clean water every day A Nobel laureate’s environmentally friendly invention that provides clean water if central supplies are knocked out by a hurricane or drought, could be a life saver for vulnerable islands, its founder says. The invention, by the chemist Prof Omar Yaghi, uses a type of science called reticular chemistry to create molecularly engineered materials, which can extract moisture from the air and harvest water even in arid and desert conditions. Continue reading...

‘Reimagining matter’: Nobel laureate invents machine that harvests water from dry air

Adaptive Allocation of Monte Carlo Samples for Efficient, Multifidelity Computational Screening of Metal–Organic Frameworks For applications in gas sensing, purification, and capture, we often wish to search a large set of metal–organic frameworks (MOFs) for the top-K in terms of their Henry coefficients for an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample consists of inserting an adsorbate in the MOF at a random position, orientation, and configuration, then calculating the MOF–adsorbate interaction energy. Our idea is to leverage top-K arm identification algorithms, developed for the multi-armed bandit problem in reinforcement learning, to sequentially and adaptively allocate adsorbate insertions among the MOFs, in a data-driven manner, to obtain the most accurate top-K subset under a fixed insertion budget. By analogy, each MOF is a slot machine in a casino that, upon pulling its arm (inserting an adsorbate), offers a stochastic reward (a noisy estimate of its Henry coefficient) sampled from a static, unknown probability distribution. Each adaptive allocation algorithm (1) proceeds in a feedback loop of (i) allocate adsorbate insertions to MOF(s), (ii) update the running estimates of the Henry coefficients of the MOF(s), then (iii) judiciously allocate adsorbate insertions to the next MOF(s); (2) sequentially dials-up the fidelities of ongoing molecular simulations in the MOFs, giving a multifidelity computational screening; and (3) circumvents the need to hand-craft structural or chemical features of the MOFs for decision making. As a case study, we implement, benchmark, and analyze the sequential halving, successive accepts and rejects, and narrowing exploration (our proposed heuristic) algorithms to adaptively allocate xenon insertions to screen a set of ca. 300 MOFs for the top-K Xe Henry coefficient subset over differing insertion budgets. Provided with a sufficient budget, we find that these adaptive insertion algorithms can significantly reduce (by a factor of 2–3) the simple regret (sum of true minus empirical top-K true Henry coefficients) and error in the top-K subset of MOFs output by a computational screening. By another metric, adaptive insertion allocation provided a ca. 60% discount on the computational cost to identify the top-K MOFs with less than 5% error. We thereby demonstrate that top-K arm identification algorithms may generally be useful for more efficiently screening materials for various properties via Monte Carlo molecular simulations. This efficiency improvement is especially important when adopting more computationally expensive, sophisticated force fields or even ab initio calculations for the potential energy of configurations to lend higher-fidelity screenings.

check out our new paper on adaptively allocating Monte Carlo samples of MOF-adsorbate configurations for efficient, multi-fidelity computational screening of MOFs for an adsorption property using molecular simulations.

pubs.acs.org/doi/full/10....

I'll see you at the AIChE conference in 2075!

the singular value decomposition is my favorite matrix factorization by far.
if I were to get a tattoo, it would be “A = UΣVᵀ".

cliché for a professor teaching SVD, but in my grad-level “math for chemical engineers” class, I compressed a photo of my dog using the SVD in Julia. 🐶

interesting point! beauty/simplicity/convenience => finds more applications. thinking of where I've encountered symmetric matrices: kernels (Gram matrix), adjacency matrix for an undirected graph, Hessian matrix, description of ellipse... there, the symmetry seems natural. SVD/PCA, less.

"it is no exaggeration to say that symmetric matrices are the most important matrices the world will ever see."

"if symmetry makes a matrix important, [the] extra property [of having all positive eigenvalues] makes it truly special."

- Gilbert Strang

thank you for the spotlight! 😀

Conductive Covalent Organic Frameworks as Chemiresistive Sensor Arrays for the Detection and Differentiation of Gasotransmitters This paper describes a chemiresistive sensor array using four structurally analogous, but chemically distinct, conductive covalent organic frameworks (COFs) (M-COF-DC-8, M = Fe, Co, Ni, and Cu) capable of detecting and differentiating four important gaseous analytes: nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H2S), and ammonia (NH3). The COFs were synthesized from the condensation of 2,3,9,10,16,17,23,24-octaamino-metallophthalocyanine precursors with pyrenetetraone linkers resulting in chemically robust and electrically conductive materials. Chemiresistive sensing experiments, together with machine learning to parse the response pattern of the sensor array, show that the M-COF-DC-8 (M = Fe, Co, Ni, Cu) materials can detect and differentiate this suite of oxidizing and reducing gases at parts-per-million concentrations, with theoretical limits of detection (LOD) in the parts-per-billion range in dry N2. Importantly, the COF array containing M-COF-DC-8 (M = Co, Ni, Cu) retains its ability to detect and differentiate these analytes in air and humidity under low power consumption. Spectroscopic investigations reveal that the synthetic control over the identity of the metallophthalocyanine core efficiently tunes material–analyte interactions and, therefore, emergent device performance. The use of highly tunable COFs as the active material in sensor arrays enables low-power, sensitive, and real-time gas detection with future applications in healthcare and personal protection.

a sensor array of conductive COFs, made by Prof. Kat Mirica's group at Dartmouth, can distinguish between NO, CO, NH₃, and H₂S. cool for us to contribute with PCA and k-NN. 😀

pubs.acs.org/doi/10.1021/...

it'd be a special X-mas seminar! jello molds served! j.k.

cool! (if I remember correctly, you are from Oregon, right? if so, please reach out next time you’re back home, to visit Oregon State and give a seminar!)

CC @rociomer.bsky.social @bessvlai.bsky.social

after eight years as a ChemE prof., I had a fantastic day when my PhD advisor Prof. Berend Smit visited Oregon State University! 😁

Optimizing Mixtures of Metal–Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting Atmospheric water harvesting (AWH) is a method to obtain clean water in remote or underdeveloped regions including, but not limited to, those with an arid or desert climate. For passive (i.e., relying...

💦 in our latest research (with @chemashlee.bsky.social), we framed an optimization problem (a linear program) for designing bespoke mixtures of metal-organic frameworks (MOFs) for robust, passive atmospheric water harvesting.

pubs.acs.org/doi/10.1021/...

“TF is that?!” -Oslo

my PhD student G. Fabusola trained and tested machine learning algorithms to parse the response pattern of a conductive-MOF sensor array from K. Mirica's group!

👃 the electronic nose could detect and differentiate toxic gases and H₂S/SO₂ mixtures at ppm-levels.

pubs.acs.org/doi/10.1021/...

We interrupt our regular programming to announce…

pretty cool!

in a “it’s a small world” moment, I ran into @bessvlai.bsky.social at Case Western Reserve University in Cleveland, OH. she was giving a seminar in the chemistry department; me, in chemical engineering. great to see you, Bess!

new preprint,
"adaptive allocation of Monte Carlo samples for efficient, multi-fidelity computational screening of metal-organic frameworks"

feedback welcome!

chemrxiv.org/engage/chemr...

"guidelines for multi-fidelity Bayesian optimization of molecules and materials"

our News & Views article in Nature Computational Science.

rdcu.be/ext6h

How a Puzzle About Fractions Got Brain Scans Rolling (Gift Article) A story of bowling pins, patterns and medical miracles.

My latest for @nytimes.com -- please repost so your followers can see this for free. www.nytimes.com/interactive/...

beavers are cool. glad our mascot is a beaver.

> The fur trade transformed North America but it nearly destroyed the population of several fur-bearers like muskrats and beavers who are critically important to their ecosystem.
🥺

🍷solving a linear program for optimal wine blending in Julia

simonensemble.github.io/pluto_nbs/wi...

😅 yeah, I think he wanted to go on a walk!

a post-fermentation blend of *nine* white wines from Oregon! and a linear program for wine blending.

🚰 "Optimizing mixtures of metal–organic frameworks for robust and bespoke passive atmospheric water harvesting" by C. Harriman, Q. Ke, T. Vlugt, A. Howarth, C. Simon.

feedback welcome on our ChemRxiv preprint:

chemrxiv.org/engage/chemr...