Yes—an algorithm can uncover useful compounds that conventional screening misses by searching for unusual combinations of properties rather than optimizing a target chosen in advance. The method, called BoundLess Objective-free eXploration (Blox), uses machine learning and a novelty measure known as Stein discrepancy to identify molecules that sit outside the dominant patterns in a chemical database.
In a 2020 demonstration, researchers screened 100,000 molecules from the ZINC database for photoactive behavior. Blox selected eight “out-of-trend” candidates, whose properties were then checked with density-functional-theory calculations. The result is a way to find overlooked possibilities—including potential solar-cell materials—without assuming that the most valuable molecule must look like known examples.
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What the Blox algorithm does
Blox stands for BoundLess Objective-free eXploration. It is a machine-learning workflow for repurposing existing molecules: a compound synthesized for one purpose, such as drug development, may have useful optical, electrical or mechanical properties that were never measured.
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Most chemical-discovery systems begin with a human-defined objective. A team might ask an algorithm to maximize absorption at a specified wavelength, minimize toxicity, or stay within a fixed range of molecular properties. That focus is efficient when the desired outcome is already understood, but it can exclude materials that behave in an unexpected way.
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Blox takes a different approach. It models the properties available in a starting data set, then gives priority to candidates that are statistically unusual in the combined property space. A molecule can therefore be selected because one property is exceptional, because two properties occur together in an unusual way, or because it lies far from the main cluster of familiar compounds.
The work was led by Koji Tsuda of the RIKEN Centre for Advanced Intelligence. The underlying paper, by K. Terayama and colleagues, is “Pushing property limits in materials discovery via boundless objective-free exploration,” published in Chemical Science 11 (2020), pages 5959–5968, DOI 10.1039/D0SC00982B.
How Blox searches a chemical database
The workflow is iterative. It does not simply rank every molecule once; it uses each evaluation to improve its picture of the chemical space and choose the next informative candidate.
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1. Assemble an initial material set
Researchers begin with molecules or materials and whatever measured or simulated properties are available. The starting set can be relatively small compared with the full database because the goal is to learn a useful approximation of the property landscape.
This step is important because public databases contain many synthesized compounds whose properties were recorded only for their original purpose. A pharmaceutical molecule may have detailed biological measurements but no data on optical absorption, charge transport or excited-state behavior. Blox can use the molecules as candidates even when the property of interest is unknown.
2. Train a property-prediction model
A machine-learning model is trained on the known examples to estimate the properties of unmeasured molecules. In the photoactive-material demonstration, the relevant coordinates were the first singlet excited-state absorption wavelength and oscillator strength.
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The model is not treated as the final authority. Its role is to estimate where molecules are likely to fall in property space so that the search can select a candidate for a higher-fidelity calculation or experiment.
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Blox then calculates a novelty score based on Stein discrepancy. In practical terms, the score asks how far a predicted candidate is from the dominant probability distribution represented by the known data.
A conventional distance from the average molecule would not be enough. Chemical properties often form curved, clustered or otherwise structured distributions. Stein discrepancy is designed to detect departures from that structure, including a candidate that is ordinary on one axis but highly unusual in a joint combination of properties.
4. Evaluate the selected candidate
The highest-priority candidate is evaluated with an experiment or a more detailed simulation. That new result is added to the data set, the prediction model is updated, and the cycle repeats.
- Prepare molecules and available property values.
- Fit a model that predicts the properties of unmeasured molecules.
- Compute a novelty score using Stein discrepancy.
- Select the most out-of-distribution candidate.
- Measure or simulate it, then feed the result back into the model.
The loop is “objective-free” in the sense that it does not require a target value such as “maximize absorption at exactly 500 nanometres.” It still requires researchers to choose the property representation, database and evaluation method.
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“Out-of-trend” does not mean that a molecule is erroneous, unstable or automatically better. It means that the molecule does not follow the dominant relationship visible in the reference data.
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For example, suppose most molecules with strong oscillator strength absorb in a familiar wavelength range. A candidate with unusually strong absorption at a different wavelength would be out-of-trend in the two-dimensional distribution. Another candidate might have a normal wavelength but an oscillator strength that is unexpectedly high or low for that region.
This joint-space view matters because useful behavior can be hidden by one-property-at-a-time screening. A material selected for a solar-cell component may need an unusual combination of optical response, energy levels, stability and process compatibility. Optimizing one number can overlook combinations that do not resemble the known winners.
In Blox, novelty is therefore a search signal rather than a performance claim. The algorithm identifies where the data look surprising; chemistry and engineering determine whether the surprise is useful.
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The 100,000-molecule demonstration
The 2020 study applied Blox to 100,000 molecules from the ZINC database. The researchers were looking for photoactive behavior and represented each molecule using two properties: first singlet excited-state absorption wavelength and oscillator strength.
Blox selected eight molecules that were unusual in this two-property space. The team then checked the candidates with density-functional-theory calculations. This was a computational validation of the predicted molecular properties, not a demonstration that the compounds had already been synthesized for a solar device or achieved a measured efficiency.
The example shows why repurposing databases can be valuable. The molecules already existed as entries associated with prior synthesis and research, but the particular optical properties needed for the search had not necessarily been measured. A database can therefore contain “hidden treasures” without containing a ready-made list of application-qualified materials.
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How Blox differs from other chemical-search strategies
| Search strategy | Human objective required? | Search boundaries | What counts as promising | Main blind spot |
|---|---|---|---|---|
| Target optimization | Yes; a target value or scoring function is specified first | Usually limited to chosen ranges or constraints | Improvement against the selected objective | Can miss useful behavior outside the target definition |
| Random sampling | No | Depends on the database or sampling rules | Whatever happens to be sampled | Spends evaluations without directing them toward informative novelty |
| Standard active learning | Usually; acquisition rules often optimize a stated goal or uncertainty criterion | Often shaped by preset constraints | Expected gain, uncertainty reduction or objective improvement | May remain inside human-defined regions of interest |
| Blox | No single performance objective is required | Designed to explore beyond familiar property trends rather than a fixed boundary | Statistical novelty in joint property space, measured with Stein discrepancy | Novelty alone says nothing about synthesis, safety or device performance |
Computational-chemistry researchers have highlighted this distinction. Clemence Corminboeuf of EPFL described Blox as addressing the reliance on human-inputted boundary conditions in other exploration algorithms. Ganna Gryn’ova of Heidelberg University called the Stein-discrepancy component “refreshingly unconventional.”
The comparison does not make Blox a universal replacement. If a team has a clear engineering target and reliable constraints, objective-driven optimization may be more efficient. Blox is most valuable when the space is poorly understood or when the team suspects that important candidates lie outside established trends.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why drug and other public databases can contain solar-cell candidates
A molecule’s original label does not determine all of its physical properties. A compound synthesized to investigate a biological pathway may also absorb light, transport charge or form a useful film. Those measurements may never have been taken because they were irrelevant to the original project.
Blox treats the database as a reservoir of structures rather than as a catalog limited to one application. The 100,000-molecule ZINC screen illustrates how a database associated with drug-like compounds can be searched for optical behavior instead.
Potential uses mentioned for this style of discovery include photoactive compounds for solar cells and materials such as hole-transport layers. In the latter case, final performance can depend on several independent conditions, so an unusual combination of properties may matter more than a single record value.
Turning a computational hit into a working device would require additional steps: confirming that the molecule can be synthesized or sourced, measuring its stability and toxicity, processing it into the intended layer, and testing the complete device. Blox identifies candidates for those steps; it does not perform them.
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What the method can and cannot establish
What a high novelty score tells you
- The candidate is unusual relative to the data and property representation used by the model.
- Its behavior may reveal a relationship that was hidden by familiar chemical trends.
- It may deserve a higher-fidelity calculation or an experiment before routine candidates are tested.
What it does not tell you
- Whether the compound is practical to synthesize at useful scale.
- Whether it is stable under light, heat, air, solvents or device operating conditions.
- Whether it is toxic, environmentally persistent or legally acceptable.
- Whether it will deliver high efficiency in a complete solar cell or other product.
- Whether the predicted property survives measurement error, model bias or a different computational method.
The demonstration used density-functional-theory calculations to check the eight selected molecules. That is stronger than relying only on a machine-learning estimate, but it remains a simulation-based check. Laboratory synthesis, measurements and device testing would be needed for practical deployment.
Important sources of bias and failure
Database coverage
Blox can only explore structures represented in its starting database. If an entire class of molecules is absent, the algorithm cannot discover it. Public collections also reflect historical research priorities, so their compounds may be easier to synthesize or more heavily studied than the broader chemical universe.
Property labels and model error
Predictions inherit the quality of the measured or simulated values used for training. Sparse, inconsistent or method-dependent labels can make a candidate appear novel for the wrong reason. A novelty score should therefore trigger verification, not replace it.
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Novelty is defined relative to the selected properties. A molecule can be out-of-trend in wavelength and oscillator strength while being unremarkable in stability or processability. Adding more relevant properties may improve the search, but it also increases data requirements and computational complexity.
Evaluation cost
Each iteration requires a calculation or experiment. High-fidelity quantum-chemistry calculations and laboratory measurements can be expensive, particularly when a candidate is difficult to synthesize. Blox helps decide what to evaluate next; it does not remove the cost of evaluation.
How a research team could use Blox responsibly
- Define the discovery question broadly but concretely. Choose the application area and the properties that matter without forcing a single optimum too early.
- Audit the database. Record which structures are present, how they were synthesized, and which measurements are missing or inconsistent.
- Build a baseline model. Test prediction accuracy on held-out molecules before trusting novelty rankings.
- Inspect candidates chemically. Check obvious issues such as impossible valence, duplicate structures, known hazards or unavailable starting materials.
- Confirm unusual predictions. Use a higher-accuracy simulation, an independent computational method or an experiment.
- Update the search with verified results. Feed measured values back into the model so later novelty scores reflect evidence rather than speculation.
- Move to application testing only after qualification. Device efficiency, durability, safety and scale-up require tests that are outside the Blox ranking itself.
Why this approach matters for materials discovery
The central contribution of Blox is a change in what the algorithm is asked to find. Instead of repeatedly selecting compounds that look most like known high performers, it searches for statistically surprising combinations. That can expose “buried treasure” in collections that scientists have already built but have not characterized for every possible use.
Its value is greatest at the exploratory stage, when researchers do not yet know which property relationships are important. The method can widen the set of hypotheses before a team commits to a narrow objective. Once a promising direction is established, conventional optimization, synthesis and engineering can take over.
Blox should therefore be understood as a discovery compass, not an automated invention machine. It points to molecules that deserve attention; chemistry, measurement and engineering decide whether they become useful materials.
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